Publications by authors named "Filippo Molinari"

114 Publications

Diagnostic Value of Conventional PET Parameters and Radiomic Features Extracted from 18F-FDG-PET/CT for Histologic Subtype Classification and Characterization of Lung Neuroendocrine Neoplasms.

Biomedicines 2021 Mar 10;9(3). Epub 2021 Mar 10.

Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, 10126 Turin, Italy.

Aim: To evaluate if conventional Positron emission tomography (PET) parameters and radiomic features (RFs) extracted by 18F-FDG-PET/CT can differentiate among different histological subtypes of lung neuroendocrine neoplasms (Lu-NENs).

Methods: Forty-four naïve-treatment patients on whom 18F-FDG-PET/CT was performed for histologically confirmed Lu-NEN (n = 46) were retrospectively included. Manual segmentation was performed by two operators allowing for extraction of four conventional PET parameters (SUVmax, SUVmean, metabolic tumor volume (MTV), and total lesion glycolysis (TLG)) and 41 RFs. Lu-NENs were classified into two groups: lung neuroendocrine tumors (Lu-NETs) vs. lung neuroendocrine carcinomas (Lu-NECs). Lu-NETs were classified according to histological subtypes (typical (TC)/atypical carcinoid (AC)), Ki67-level, and TNM staging. The least absolute shrink age and selection operator (LASSO) method was used to select the most predictive RFs for classification and Pearson correlation analysis was performed between conventional PET parameters and selected RFs.

Results: PET parameters, in particular, SUVmax (area under the curve (AUC) = 0.91; cut-off = 5.16) were higher in Lu-NECs vs. Lu-NETs ( < 0.001). Among RFs, HISTO_Entropy_log10 was the most predictive (AUC = 0.90), but correlated with SUVmax/SUVmean (r = 0.95/r = 0.94, respectively). No statistical differences were found between conventional PET parameters and RFs ( > 0.05) and TC vs. AC classification. Conventional PET parameters were correlated with N+ status in Lu-NETs.

Conclusion: In our study, conventional PET parameters were able to distinguish Lu-NECs from Lu-NETs, but not TC from AC. RFs did not provide additional information.
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http://dx.doi.org/10.3390/biomedicines9030281DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001140PMC
March 2021

A Proposal For COVID-19 Applications Enabling Extensive Epidemiological Studies.

Procedia Comput Sci 2021 22;181:589-596. Epub 2021 Feb 22.

Dipartimento di Informatica, Sistemistica e Comunicazione, Università degli Studi di Milano-Bicocca, Viale Sarca 336, 20126, Milano, Italy.

During the next phase of COVID-19 outbreak, mobile applications could be the most used and proposed technical solution for monitoring and tracking, by acquiring data from subgroups of the population. A possible problem could be data fragmentation, which could lead to three harmful effects: i) data could not cover the minimum percentage of the people for monitoring efficacy, ii) it could be heavily biased due to different data collection policies, and iii) the app could not monitor subjects moving across different zones or countries. A common approach could solve these problems, defining requirements for the selection of observed data and technical specifications for the complete interoperability between different solutions. This work aims to integrate the international framework of requirements in order to mitigate the known issues and to suggest a method for clinical data collection that ensures to researchers and public health institution significant and reliable data. First, we propose to identify which data is relevant for COVID-19 monitoring through literature and guidelines review. Then we analysed how the currently available guidelines for COVID-19 monitoring applications drafted by European Union and World Health Organization face the issues listed before. Eventually we proposed the first draft of integration of current guidelines.
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http://dx.doi.org/10.1016/j.procs.2021.01.206DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898976PMC
February 2021

Impact of segmentation and discretization on radiomic features in Ga-DOTA-TOC PET/CT images of neuroendocrine tumor.

EJNMMI Phys 2021 Feb 27;8(1):21. Epub 2021 Feb 27.

Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Corso Dogliotti 14, 10126, Turin, Italy.

Objective: To identify the impact of segmentation methods and intensity discretization on radiomic features (RFs) extraction from Ga-DOTA-TOC PET images in patients with neuroendocrine tumors.

Methods: Forty-nine patients were retrospectively analyzed. Tumor contouring was performed manually by four different operators and with a semi-automatic edge-based segmentation (SAEB) algorithm. Three SUV fixed thresholds (20, 30, 40%) were applied. Fifty-one RFs were extracted applying two different intensity rescale factors for gray-level discretization: one absolute (AR60 = SUV from 0 to 60) and one relative (RR = min-max of the VOI SUV). Dice similarity coefficient (DSC) was calculated to quantify segmentation agreement between different segmentation methods. The impact of segmentation and discretization on RFs was assessed by intra-class correlation coefficients (ICC) and the coefficient of variance (COV). The RFs' correlation with volume and SUV was analyzed by calculating Pearson's correlation coefficients.

Results: DSC mean value was 0.75 ± 0.11 (0.45-0.92) between SAEB and operators and 0.78 ± 0.09 (0.36-0.97), among the four manual segmentations. The study showed high robustness (ICC > 0.9): (a) in 64.7% of RFs for segmentation methods using AR60, improved by applying SUV threshold of 40% (86.5%); (b) in 50.9% of RFs for different SUV thresholds using AR60; and (c) in 37% of RFs for discretization settings using different segmentation methods. Several RFs were not correlated with volume and SUV.

Conclusions: RFs robustness to manual segmentation resulted higher in NET Ga-DOTA-TOC images compared to F-FDG PET/CT images. Forty percent SUV thresholds yield superior RFs stability among operators, however leading to a possible loss of biological information. SAEB segmentation appears to be an optimal alternative to manual segmentation, but further validations are needed. Finally, discretization settings highly impacted on RFs robustness and should always be stated.
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http://dx.doi.org/10.1186/s40658-021-00367-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7914329PMC
February 2021

Ga-DOTATOC PET/CT-Based Radiomic Analysis and PRRT Outcome: A Preliminary Evaluation Based on an Exploratory Radiomic Analysis on Two Patients.

Front Med (Lausanne) 2020 26;7:601853. Epub 2021 Jan 26.

Nuclear Medicine Unit, Department of Medical Sciences, University of Turin, Turin, Italy.

This work aims to evaluate whether the radiomic features extracted by 68Ga-DOTATOC-PET/CT of two patients are associated with the response to peptide receptor radionuclide therapy (PRRT) in patients affected by neuroendocrine tumor (NET). This is a pilot report in two NET patients who experienced a discordant response to PRRT (responder vs. non-responder) according to RECIST1.1. The patients presented with liver metastasis from the rectum and pancreas G3-NET, respectively. Whole-body total-lesion somatostatin receptor-expression (TLSREwb-50) and somatostatin receptor-expressing tumor volume (SRETV wb-50) were obtained in pre- and post-PRRT PET/CT. Radiomic analysis was performed, extracting 38 radiomic features (RFs) from the patients' lesions. The Mann-Whitney test was used to compare RFs in the responder patient vs. the non-responder patient. Pearson correlation and principal component analysis (PCA) were used to evaluate the correlation and independence of the different RFs. TLSREwb-50 and SRETVwb-50 modifications correlate with RECIST1.1 response. A total of 28 RFs extracted on pre-therapy PET/CT showed significant differences between the two patients in the Mann-Whitney test ( < 0.05). A total of seven second-order features, with poor correlation with SUVmax and PET volume, were identified by the Pearson correlation matrix. Finally, the first two PCA principal components explain 83.8% of total variance. TLSREwb-50 and SRETVwb-50 are parameters that might be used to predict and to assess the PET response to PRRT. RFs might have a role in defining inter-patient heterogeneity and in the prediction of therapy response. It is important to implement future studies with larger and more homogeneous patient populations to confirm the efficacy of these biomarkers.
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http://dx.doi.org/10.3389/fmed.2020.601853DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7870479PMC
January 2021

Thrombophilic alterations, migraine, and vascular disease: results from a case-control study.

Neurol Sci 2021 Jan 20. Epub 2021 Jan 20.

Institute of Neurology, Department of Applied Clinical Sciences and Biotechnology, University of L'Aquila, L'Aquila, Italy.

Background: The association between thrombophilic alterations, migraine, and vascular events has been broadly investigated but not been completely clarified.

Methods: In this cross-sectional, case-control study, we included consecutive outpatients diagnosed with migraine referring to a tertiary headache center. Migraine patients were matched to headache-free control subjects. All participants were evaluated for free protein S anticoagulant, functional protein C anticoagulant, homocysteine, and antiphospholipid antibodies (aPLs). History of ischemic stroke (IS) or transient ischemic attack (TIA), coronary heart disease, and peripheral venous thrombosis was also ascertained.

Results: We included 329 migraine patients and 329 control subjects (mean age 41 years, 77% women in both groups). Among migraine patients, 239 (72.6%) had migraine without aura and 90 (27.4%) had migraine with aura. Migraine patients had more frequently arterial hypertension, hypercholesterolemia, history of IS or TIA and, peripheral venous thrombosis compared to control subjects, whereas we found no differences in diabetes mellitus, BMI, and coronary heart disease between the two groups. At least one thrombophilic alteration was detected in 107 (32.5%) migraine patients and in 74 (22.5%) control subjects (OR = 1.66, 95% CI 1.17-2.35, p = 0.004). We identified an association of migraine with aPL positivity (OR = 2.6, 95% CI 1.5-4.7, p = 0.001) and with free protein S deficiency (OR = 4.7, 95% CI 1.6-14.0, p = 0.002), whereas we found no differences in protein C deficiency, APCR, and hyperhomocysteinemia between the two groups. Furthermore, aPL positivity and free protein S deficiency were more common in migraine patients with and without aura than in control subjects. We found that in migraine patients, aPL positivity was associated with both IS or TIA (OR = 5.6, 95% CI 1.5-20.4, p = 0.009) and with coronary heart disease (OR = 27.6, 95% CI 1.4-531.1, p = 0.028), whereas free protein S deficiency was associated with IS or TIA only (OR = 14.3, 95% CI 2.8-74.4, p = 0.002).

Conclusions: Our research documented a significative higher prevalence of aPL positivity and protein S deficiency in migraineurs than in controls. Data also showed an association between these alterations and some vascular thrombotic events in migraine patients. We can argue that thrombophilic disorders associated with migraine may contribute to the occurrence of vascular events.
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http://dx.doi.org/10.1007/s10072-020-05006-zDOI Listing
January 2021

The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis.

Comput Biol Med 2021 Jan 21;128:104129. Epub 2020 Nov 21.

Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca Degli Abruzzi 24, Turin, 10129, Italy.

Recently, deep learning frameworks have rapidly become the main methodology for analyzing medical images. Due to their powerful learning ability and advantages in dealing with complex patterns, deep learning algorithms are ideal for image analysis challenges, particularly in the field of digital pathology. The variety of image analysis tasks in the context of deep learning includes classification (e.g., healthy vs. cancerous tissue), detection (e.g., lymphocytes and mitosis counting), and segmentation (e.g., nuclei and glands segmentation). The majority of recent machine learning methods in digital pathology have a pre- and/or post-processing stage which is integrated with a deep neural network. These stages, based on traditional image processing methods, are employed to make the subsequent classification, detection, or segmentation problem easier to solve. Several studies have shown how the integration of pre- and post-processing methods within a deep learning pipeline can further increase the model's performance when compared to the network by itself. The aim of this review is to provide an overview on the types of methods that are used within deep learning frameworks either to optimally prepare the input (pre-processing) or to improve the results of the network output (post-processing), focusing on digital pathology image analysis. Many of the techniques presented here, especially the post-processing methods, are not limited to digital pathology but can be extended to almost any image analysis field.
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http://dx.doi.org/10.1016/j.compbiomed.2020.104129DOI Listing
January 2021

Multi-marker quantitative radiomics for mass characterization in dedicated breast CT imaging.

Med Phys 2021 Jan 10;48(1):313-328. Epub 2020 Dec 10.

Department of Medical Imaging, Radboud University Medical Center, PO Box 9101, Nijmegen, 6500 HB, The Netherlands.

Purpose: To develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images.

Methods: Over 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant).

Results: The majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96).

Conclusions: The proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.
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http://dx.doi.org/10.1002/mp.14610DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7898616PMC
January 2021

Automated prediction of sepsis using temporal convolutional network.

Comput Biol Med 2020 Dec 12;127:103957. Epub 2020 Aug 12.

School of Engineering, Ngee Ann Polytechnic, Singapore; Department Bioinformatics and Medical Engineering, Asia University, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan. Electronic address:

Multiple organ failure is the trademark of sepsis. Sepsis occurs when the body's reaction to infection causes injury to its tissues and organs. As a consequence, fluid builds up in the tissues causing organ failure and leading to septic shock eventually. Some symptoms of sepsis include fever, arrhythmias, blood vessel leaks, impaired clotting, and generalised inflammation. In order to address the limitations in current diagnosis, we have proposed a cost-effective automated diagnostic tool in this study. A deep temporal convolution network has been developed for the prediction of sepsis. Septic data was fed to the model and a high accuracy and area under ROC curve (AUROC) of 98.8% and 98.0% were achieved respectively, for per time-step metrics. A relatively high accuracy and AUROC of 95.5% and 91.0% were also achieved respectively, for per-patient metrics. This is a novel study in that it has investigated per time-step metrics, compared to other studies which investigated per-patient metrics. Our model has also been evaluated by three validation methods. Thus, the recommended model is robust with high accuracy and precision and has the potential to be used as a tool for the prediction of sepsis in hospitals.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103957DOI Listing
December 2020

Fully automated quantitative assessment of hepatic steatosis in liver transplants.

Comput Biol Med 2020 08 29;123:103836. Epub 2020 May 29.

Politobiomed Lab, Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy.

Background: The presence of macro- and microvesicular steatosis is one of the major risk factors for liver transplantation. An accurate assessment of the steatosis percentage is crucial for determining liver graft transplantability, which is currently based on the pathologists' visual evaluations on liver histology specimens.

Method: The aim of this study was to develop and validate a fully automated algorithm, called HEPASS (HEPatic Adaptive Steatosis Segmentation), for both micro- and macro-steatosis detection in digital liver histological images. The proposed method employs a hybrid deep learning framework, combining the accuracy of an adaptive threshold with the semantic segmentation of a deep convolutional neural network. Starting from all white regions, the HEPASS algorithm was able to detect lipid droplets and classify them into micro- or macrosteatosis.

Results: The proposed method was developed and tested on 385 hematoxylin and eosin (H&E) stained images coming from 77 liver donors. Automated results were compared with manual annotations and nine state-of-the-art techniques designed for steatosis segmentation. In the TEST set, the algorithm was characterized by 97.27% accuracy in steatosis quantification (average error 1.07%, maximum average error 5.62%) and outperformed all the compared methods.

Conclusions: To the best of our knowledge, the proposed algorithm is the first fully automated algorithm for the assessment of both micro- and macrosteatosis in H&E stained liver tissue images. Being very fast (average computational time 0.72 s), this algorithm paves the way for automated, quantitative and real-time liver graft assessments.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103836DOI Listing
August 2020

Stain Color Adaptive Normalization (SCAN) algorithm: Separation and standardization of histological stains in digital pathology.

Comput Methods Programs Biomed 2020 Sep 17;193:105506. Epub 2020 Apr 17.

Politecnico di Torino, PoliToBIOMed Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, 10129, Turin, Italy.

Background And Objective: The diagnosis of histopathological images is based on the visual analysis of tissue slices under a light microscope. However, the histological tissue appearance may assume different color intensities depending on the staining process, operator ability and scanner specifications. This stain variability affects the diagnosis of the pathologist and decreases the accuracy of computer-aided diagnosis systems. In this context, the stain normalization process has proved to be a powerful tool to cope with this issue, allowing to standardize the stain color appearance of a source image respect to a reference image.

Methods: In this paper, novel fully automated stain separation and normalization approaches for hematoxylin and eosin stained histological slides are presented. The proposed algorithm, named SCAN (Stain Color Adaptive Normalization), is based on segmentation and clustering strategies for cellular structures detection. The SCAN algorithm is able to improve the contrast between histological tissue and background and preserve local structures without changing the color of the lumen and the background.

Results: Both stain separation and normalization techniques were qualitatively and quantitively validated on a multi-tissue and multiscale dataset, with highly satisfactory results, outperforming the state-of-the-art approaches. SCAN was also tested on whole-slide images with high performances and low computational times.

Conclusions: The potential contribution of the proposed standardization approach is twofold: the improvement of visual diagnosis in digital histopathology and the development of powerful pre-processing strategies to automated classification techniques for cancer detection.
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http://dx.doi.org/10.1016/j.cmpb.2020.105506DOI Listing
September 2020

A novel hybrid approach for automated detection of retinal detachment using ultrasound images.

Comput Biol Med 2020 05 19;120:103704. Epub 2020 Mar 19.

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Clementi, 599489, Singapore; Department of Bioinformatics and Medical Engineering, Asia University, Taichung, Taiwan; International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto, Japan. Electronic address:

Retinal detachment (RD) is an ocular emergency, which needs quick intervention to preclude permanent vision loss. In general, ocular ultrasound is used by ophthalmologists to enhance their judgment in detecting RD in eyes with media opacities which precludes the retinal evaluation. However, the quality of ultrasound (US) images may be degraded due to the presence of noise, and other retinal conditions may cause membranous echoes. All these can influence the accuracy of diagnosis. Hence, to overcome the above, we are proposing an automated system to detect RD using texton, higher order spectral (HOS) cumulants and locality sensitive discriminant analysis (LSDA) techniques. Our developed method is able to classify the posterior vitreous detachment and RD using support vector machine classifier with highest accuracy of 99.13%. Our system is ready to be tested with more diverse ultrasound images and aid ophthalmologists to arrive at a more accurate diagnosis.
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http://dx.doi.org/10.1016/j.compbiomed.2020.103704DOI Listing
May 2020

Coronavirus disease (COVID-19) in a paucisymptomatic patient: epidemiological and clinical challenge in settings with limited community transmission, Italy, February 2020.

Euro Surveill 2020 03;25(11)

The members of these groups are acknowledged at the end of the article.

Data concerning the transmission of the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2) in paucisymptomatic patients are lacking. We report an Italian paucisymptomatic case of coronavirus disease 2019 with multiple biological samples positive for SARS-CoV-2. This case was detected using the World Health Organization protocol on cases and contact investigation. Current discharge criteria and the impact of extra-pulmonary SARS-CoV-2 samples are discussed.
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http://dx.doi.org/10.2807/1560-7917.ES.2020.25.11.2000230DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7096776PMC
March 2020

Automatic Optic Nerve Measurement: A New Tool to Standardize Optic Nerve Assessment in Ultrasound B-Mode Images.

Ultrasound Med Biol 2020 Jun 6;46(6):1533-1544. Epub 2020 Mar 6.

Department of Neurology, Saarland University Medical Center, Homburg, Germany.

Transorbital sonography provides reliable information about the estimation of intra-cranial pressure by measuring the optic nerve sheath diameter (ONSD), whereas the optic nerve (ON) diameter (OND) may reveal ON atrophy in patients with multiple sclerosis. Here, an AUTomatic Optic Nerve MeAsurement (AUTONoMA) system for OND and ONSD assessment in ultrasound B-mode images based on deformable models is presented. The automated measurements were compared with manual ones obtained by two operators, with no significant differences. AUTONoMA correctly segmented the ON and its sheath in 71 out of 75 images. The mean error compared with the expert operator was 0.06 ± 0.52 mm and 0.06 ± 0.35 mm for the ONSD and OND, respectively. The agreement between operators and AUTONoMA was good and a positive correlation was found between the readers and the algorithm with errors comparable with the inter-operator variability. The AUTONoMA system may allow for standardization of OND and ONSD measurements, reducing manual evaluation variability.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2020.01.034DOI Listing
June 2020

Multimodal T2w and DWI Prostate Gland Automated Registration.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:4427-4430

Multiparametric magnetic resonance imaging (mpMRI) is emerging as a promising tool in the clinical pathway of prostate cancer (PCa). The registration between a structural and a functional imaging modality, such as T2-weighted (T2w) and diffusion-weighted imaging (DWI) is fundamental in the development of a mpMRI-based computer aided diagnosis (CAD) system for PCa. Here, we propose an automated method to register the prostate gland in T2w and DWI image sequences by a landmark-based affine registration and a non-parametric diffeomorphic registration. An expert operator manually segmented the prostate gland in both modalities on a dataset of 20 patients. Target registration error and Jaccard index, which measures the overlap between masks, were evaluated pre- and post- registration resulting in an improvement of 44% and 21%, respectively. In the future, the proposed method could be useful in the framework of a CAD system for PCa detection and characterization in mpMRI.
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http://dx.doi.org/10.1109/EMBC.2019.8856467DOI Listing
July 2019

Non-invasive analysis of actinic keratosis using a cold stimulation and near-infrared spectroscopy.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:467-470

Non-melanoma skin cancers are the most common tumor in the Caucasian population, and include actinic keratosis (AK), which is considered an early form of in-situ squamous cell carcinoma (SCC). Currently the only way to monitor lesion progression (i.e., from AK to invasive SCC) is through an invasive bioptic procedure. Near-infrared spectroscopy (NIRS) is a non-invasive technique that studies haemoglobin (oxygenated haemoglobin, O2Hb, and deoxygenated haemoglobin, HHb) relative concentration variations. The objective of this study is to evaluate if AKs present a different vascular response when compared to healthy skin using time and frequency parameters extracted from the NIRS signals. The NIRS signals were acquired on the AKs and a healthy skin area of patients (n=53), with the same acquisition protocol: baseline signals (1.5 min), application of ice pack near lesion (1.5 min), removal of ice pack and acquisition of vascular recovery (1.5 min). We calculated 18 features to evaluate if the vascular response was different in the two cases (i.e., healthy skin and AK lesions). By applying the multivariate analysis of variance (MANOVA), a statistically significant difference is found in the O2Hb and HHb after the stimulus application. This shows how the NIRS technique can give important vascular information that could help the diagnosis of a lesion and the evaluation of its progression. Overall, the obtained results encourage us to look further into the study of the skin lesions and their progression with NIRS signals.
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http://dx.doi.org/10.1109/EMBC.2019.8857279DOI Listing
July 2019

Automatic Extraction of Dermatological Parameters from Nevi Using an Inexpensive Smartphone Microscope: A Proof of Concept.

Annu Int Conf IEEE Eng Med Biol Soc 2019 Jul;2019:399-402

The evolution of smartphone technology has made their use more common in dermatological applications. Here we studied the feasibility of using an inexpensive smartphone microscope for the extraction of dermatological parameters and compared the results obtained with a portable dermoscope, commonly used in clinical practice. Forty-two skin lesions were imaged with both devices and visually analyzed by an expert dermatologist. The presence of a reticular pattern was observed in 22 dermoscopic images, but only in 10 smartphone images. The proposed paradigm segments the image and extracts texture features which are used to train and validate a neural network to classify the presence of a reticular pattern. Using 5-fold cross-validation, an accuracy of 100% and 95% was obtained with the dermoscopic and smartphone images, respectively. This approach can be useful for general practitioners and as a triage tool for skin lesion analysis.
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http://dx.doi.org/10.1109/EMBC.2019.8856720DOI Listing
July 2019

Automated plaque classification using computed tomography angiography and Gabor transformations.

Artif Intell Med 2019 09 14;100:101724. Epub 2019 Sep 14.

Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; University of Malaya Research Imaging Centre (UMRIC), Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia.

Cardiovascular diseases are the primary cause of death globally. These are often associated with atherosclerosis. This inflammation process triggers important variations in the coronary arteries (CA) and can lead to coronary artery disease (CAD). The presence of CA calcification (CAC) has recently been shown to be a strong predictor of CAD. In this clinical setting, computed tomography angiography (CTA) has begun to play a crucial role as a non-intrusive imaging method to characterize and study CA plaques. Herein, we describe an automated algorithm to classify plaque as either normal, calcified, or non-calcified using 2646 CTA images acquired from 73 patients. The automated technique is based on various features that are extracted from the Gabor transform of the acquired CTA images. Specifically, seven features are extracted from the Gabor coefficients : energy, and Kapur, Max, Rényi, Shannon, Vajda, and Yager entropies. The features were then ordered based on the F-value and input to numerous classification methods to achieve the best classification accuracy with the least number of features. Moreover, two well-known feature reduction techniques were employed, and the features acquired were also ranked according to F-value and input to several classifiers. The best classification results were obtained using all computed features without the employment of feature reduction, using a probabilistic neural network. An accuracy, positive predictive value, sensitivity, and specificity of 89.09%, 91.70%, 91.83% and 83.70% was obtained, respectively. Based on these results, it is evident that the technique can be helpful in the automated classification of plaques present in CTA images, and may become an important tool to reduce procedural costs and patient radiation dose. This could also aid clinicians in plaque diagnostics.
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http://dx.doi.org/10.1016/j.artmed.2019.101724DOI Listing
September 2019

Quantitative brain relaxation atlases for personalized detection and characterization of brain pathology.

Magn Reson Med 2020 01 16;83(1):337-351. Epub 2019 Aug 16.

Advanced Clinical Imaging Technology, Siemens Healthcare AG, Lausanne, Switzerland.

Purpose: To exploit the improved comparability and hardware independency of quantitative MRI, databases of MR physical parameters in healthy tissue are required, to which tissue properties of patients can be compared. In this work, normative values for longitudinal and transverse relaxation times in the brain were established and tested in single-subject comparisons for detection of abnormal relaxation times.

Methods: Relaxometry maps of the brain were acquired from 52 healthy volunteers. After spatially normalizing the volumes into a common space, T and T inter-subject variability within the healthy cohort was modeled voxel-wise. A method for a single-subject comparison against the atlases was developed by computing z-scores with respect to the established healthy norms. The comparison was applied to two multiple sclerosis and one clinically isolated syndrome cases for a proof of concept.

Results: The established atlases exhibit a low variation in white matter structures (median RMSE of models equal to 32 ms for T and 4 ms for T ), indicating that relaxation times are in a narrow range for normal tissues. The proposed method for single-subject comparison detected relaxation time deviations from healthy norms in the example patient data sets. Relaxation times were found to be increased in brain lesions (mean z-scores >5). Moreover, subtle and confluent differences (z-scores ~2-4) were observed in clinically plausible regions (between lesions, corpus callosum).

Conclusions: Brain T and T quantitative norms were derived voxel-wise with low variability in healthy tissue. Example patient deviation maps demonstrated good sensitivity of the atlases for detecting relaxation time alterations.
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http://dx.doi.org/10.1002/mrm.27927DOI Listing
January 2020

Automated segmentation of brain cells for clonal analyses in fluorescence microscopy images.

J Neurosci Methods 2019 09 5;325:108348. Epub 2019 Jul 5.

Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy. Electronic address:

The understanding of how cell diversity within and across distinct brain regions is ontogenetically achieved is a pivotal topic in neuroscience. Clonal analyses based on multicolor cell labeling represent a powerful tool to tackle this issue and disclose lineage relationships, but produce enormous sets of fluorescence images, leading to time consuming analyses that may be biased by the operator's subjectivity. Thus, time-efficient automated software are needed to analyze images easily, accurately and without subjective bias. In this paper, we present a fully automated method, named FAST ('Fluorescent cell Analysis Segmentation Tool'), for the segmentation of neural cells labeled by multicolor combinations of fluorophores and for their classification into clones. The proposed method was tested on 77 high-magnification fluorescence images of adult mouse cerebellar tissues acquired using a confocal microscope. Automatic results were compared with manual annotations and two open-source software designed for cell detection in microscopic imaging. The algorithm showed very good performance in the cellular detection and in the assignment of the clonal identity. To the best of our knowledge, FAST is the first fully automated technique for the analysis of cellular clones based on combinatorial expression of fluorescent proteins. The proposed approach allows to perform clonal analyses easily, accurately and objectively, overcoming those biases and errors that may result from manual annotations. Moreover, it can be broadly applied to the quantification and colocalization within cells of fluorescent markers, therefore representing a versatile and powerful tool for automated quantitative analyses in fluorescence microscopy.
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http://dx.doi.org/10.1016/j.jneumeth.2019.108348DOI Listing
September 2019

Functional Outcome of Elderly Patients Treated for Odontoid Fracture: A Multicenter Study.

Spine (Phila Pa 1976) 2019 Jul;44(13):951-958

Neurosurgery Division, "M. Bufalini" Hospital, Cesena, Italy.

Study Design: Retrospective multicenter study.

Objective: Analysis of impact of conservative and surgical treatments on functional outcome of geriatric odontoid fractures.

Summary Of Background Data: Treatment of odontoid fractures in aged population is still debatable.

Methods: One hundred fourty-seven consecutive odontoid fractures in elderly patients were classified according to Anderson-D'Alonzo and Roy-Camille classifications. Philadelphia type collar was always positioned and kept as a treatment whenever acceptable. Halo-vest, anterior screw fixation, C1-C2 posterior arthrodesis, and occipito-cervical fixation were the other treatments adopted. Conservative or surgical treatment strategy was more significantly influenced by antero-posterior displacement (< or >5 mm) and by surgeon decision. On admission ASA, modified Rankin scale (mRS-pre) and Charlson Comorbidity Index (CCI) were assessed. Modified Rankin scale (mRS-post), Neck Disability Index (NDI), and Smiley Webster Pain Scale (SWPS) were administered 12 to 15 months after treatment to estimate functional outcome in terms of general disability, neck-related disability, and ability to return to work/former activity. Risk of treatment crossover was calculated considering factors affecting outcome. Fracture healing process in terms of fusion-stability, no fusion-stability, no fusion-no stability was evaluated at 12 months through a cervical computed tomography (CT) scan. Dynamic cervical spine x-rays were obtained whether necessary. No fusion-stability was considered an adequate treatment goal in our geriatric population. Chi square/Fisher exact test and logistic regression were performed for statistical anal.

Results: Overall 67 patients were treated conservatively whereas 80 underwent surgery. Collar was adopted in 45 patients, while anterior odontoid fixation and C1-C2 posterior arthrodesis were preferred for 30 patients each. 79.8% of patients showed good outcomes according to NDI. No significant differences were observed between patients of 65 to 79 years and more than or equal to 80 years (P = 0.81). CCI greatly correlated with mRS-post, with higher indexes in 68.8% of cases characterized by good outcomes (P = 0.05). mRS-pre correlated with NDI (P < 0.000001) and mRS-post (P = 0.04). CCI, mRS-pre, and surgery were associated with worse NDI, while both C1-C2 posterior arthrodesis and occipito-cervical stabilization were associated with worse mRS-post, respectively in 40% and 30% of cases. Younger patients had a higher risk of treatment crossover.

Conclusion: mRS-pre and CCI provided two independent predictive values respectively for functional outcome and post-treatment disability. Compared with conservative immobilizations, surgery revealed no advantages in the elderly in terms of functional outcome.

Level Of Evidence: 3.
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http://dx.doi.org/10.1097/BRS.0000000000002982DOI Listing
July 2019

Automatic discrimination of neoplastic epithelium and stromal response in breast carcinoma.

Comput Biol Med 2019 07 11;110:8-14. Epub 2019 May 11.

Department of Pathology, Ospedale San Lazzano, 12051, Alba, Italy.

Background And Objectives: In breast carcinoma, epithelial-stromal interactions play a pivotal role in tumor formation and progression, and it must be accurately assessed for a correct extraction of predictive and prognostic biomarkers. Evaluation of preoperative (baseline) neoplasia/stroma ratio and the enumeration of tumor infiltrating lymphocytes (TIL) represent only two conditions in which precise discrimination of cancer epithelium and stromal reaction are relevant. However, subjectivity and expertise of the operators may lead to different degrees of assessment.

Methods: In this paper, we present a fully automated method for the discrimination between neoplastic epithelium and stromal reaction in breast carcinoma. Starting from cell nuclei, the proposed method implements computer vision strategies to split the neoplastic epithelium tissue from the stromal reaction.

Results: The algorithm is tested on 100 H&E (hematoxylin and eosin) stained images representative of 10 different cases of invasive carcinoma. The algorithm performance in the detection of neoplastic epithelium (compared to manual annotations by an expert pathologist) gave a F1 of 0.8894 and mean jaccard of 0.8481.

Conclusions: To the best of our knowledge, the proposed method is the first fully automated algorithm for the discrimination between neoplastic epithelium and stromal reaction in H&E stained images of breast tissue. The proposed approach paves the way for an automated and quantitative analysis of predictive and prognostic biomarkers in breast carcinoma.
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http://dx.doi.org/10.1016/j.compbiomed.2019.05.009DOI Listing
July 2019

Automated Segmentation of Fluorescence Microscopy Images for 3D Cell Detection in human-derived Cardiospheres.

Sci Rep 2019 04 30;9(1):6644. Epub 2019 Apr 30.

Department of Electronics and Telecommunications, Politecnico di Torino, Turin, 10129, Italy.

The 'cardiosphere' is a 3D cluster of cardiac progenitor cells recapitulating a stem cell niche-like microenvironment with a potential for disease and regeneration modelling of the failing human myocardium. In this multicellular 3D context, it is extremely important to decrypt the spatial distribution of cell markers for dissecting the evolution of cellular phenotypes by direct quantification of fluorescent signals in confocal microscopy. In this study, we present a fully automated method, named CARE ('CARdiosphere Evaluation'), for the segmentation of membranes and cell nuclei in human-derived cardiospheres. The proposed method is tested on twenty 3D-stacks of cardiospheres, for a total of 1160 images. Automatic results are compared with manual annotations and two open-source software designed for fluorescence microscopy. CARE performance was excellent in cardiospheres membrane segmentation and, in cell nuclei detection, the algorithm achieved the same performance as two expert operators. To the best of our knowledge, CARE is the first fully automated algorithm for segmentation inside in vitro 3D cell spheroids, including cardiospheres. The proposed approach will provide, in the future, automated quantitative analysis of markers distribution within the cardiac niche-like environment, enabling predictive associations between cell mechanical stresses and dynamic phenotypic changes.
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http://dx.doi.org/10.1038/s41598-019-43137-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6491482PMC
April 2019

Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals.

Comput Biol Med 2019 03 19;106:71-81. Epub 2019 Jan 19.

Biolab, Department of Electronics and Telecommunications, Politecnico di Torino, Turin, Italy. Electronic address:

Automated evaluation of a subject's neurocognitive performance (NCP) is a relevant topic in neurological and clinical studies. NCP represents the mental/cognitive human capacity in performing a specific task. It is difficult to develop the study protocols as the subject's NCP changes in a known predictable way. Sleep is time-varying NCP and can be used to develop novel NCP techniques. Accurate analysis and interpretation of human sleep electroencephalographic (EEG) signals is needed for proper NCP assessment. In addition, sleep deprivation may cause prominent cognitive risks in performing many common activities such as driving or controlling a generic device; therefore, sleep scoring is a crucial part of the process. In the sleep cycle, the first stage of non-rapid eye movement (NREM) sleep or stage N1 is the transition between wakefulness and drowsiness and becomes relevant for the study of NCP. In this study, a novel cascaded recurrent neural network (RNN) architecture based on long short-term memory (LSTM) blocks, is proposed for the automated scoring of sleep stages using EEG signals derived from a single-channel. Fifty-five time and frequency-domain features were extracted from the EEG signals and fed to feature reduction algorithms to select the most relevant ones. The selected features constituted as the inputs to the LSTM networks. The cascaded architecture is composed of two LSTM RNNs: the first network performed 4-class classification (i.e. the five sleep stages with the merging of stages N1 and REM into a single stage) with a classification rate of 90.8%, and the second one obtained a recognition performance of 83.6% for 2-class classification (i.e. N1 vs REM). The overall percentage of correct classification for five sleep stages is found to be 86.7%. The objective of this work is to improve classification performance in sleep stage N1, as a first step of NCP assessment, and at the same time obtain satisfactory classification results in the other sleep stages.
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http://dx.doi.org/10.1016/j.compbiomed.2019.01.013DOI Listing
March 2019

Transverse Muscle Ultrasound Analysis (TRAMA): Robust and Accurate Segmentation of Muscle Cross-Sectional Area.

Ultrasound Med Biol 2019 03 9;45(3):672-683. Epub 2019 Jan 9.

Division of Physical Medicine and Rehabilitation, Department of Surgical Sciences, University of Turin, Turin, Italy.

Ultrasonography allows non-invasive and real time-measurement of the visible cross-sectional area (CSA) of muscles, which is a clinically relevant descriptor of muscle size. The aim of this study was to develop and validate a fully automatic method called transverse muscle ultrasound analysis (TRAMA) for segmentation of the muscle in B-mode transverse ultrasound images and measurement of muscle CSA. TRAMA was tested on a database of 200 ultrasound images of the rectus femoris, vastus lateralis, tibialis anterior and medial gastrocnemius muscles. The automatic CSA measurements were compared with manual measurements obtained by two operators. There were no statistical differences between the automatic and manual measurements of CSA of the four muscles, and TRAMA performance was comparable to intra-operator variability in terms of the Dice similarity coefficient and Hausdorff distance between the automatic and manual segmentations. Compared with manual segmentation, the Dice similarity coefficient for the proposed method was always higher than 93%; the Hausdorff distance never exceeded 4 mm, and the maximum absolute error was 62 mm. TRAMA is the first automated algorithm that analyzes and segments ultrasound scans of the muscle in the transverse plane. It can be adopted in future studies for automatic segmentation of muscle regions of interest to enhance and automatize a multitexture analysis of muscle structure.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2018.11.012DOI Listing
March 2019

Functional Outcome After Odontoid Fractures in the Elderly.

Acta Neurochir Suppl 2019;125:329-333

Neurosurgery, Sant'Anna University Hospital, Ferrara, Italy.

While several papers on mortality and the fusion rate in elderly patients treated surgically or non-surgically for odontoid fractures exist, little information is available on quality of life after treatment. The aim of treatment in these patients should not be fracture healing alone but also quality of life improvement.A literature search using PubMed identified seven papers including information on functional evaluation of 402 patients.Patients treated with anterior screw fixation had a good functional outcome in 92.6% of cases. This percentage seemed to decrease in octogenarians. Less information was available for patients treated with posterior approaches; it would seem that up to a half of such patients experienced pain and limitations in activities of daily living after surgery. Patients treated with a halo device had a functional outcome that was worse (or at least no better) than that of patients treated with surgery, with absence of limitations in activities of daily living in 77.3% of patients. Patients treated with a collar had a good functional outcome in the majority of cases, with absence of limitations in activities of daily living in 89% of patients.More studies are needed for evaluation of functional outcome, especially in patients treated with a collar, a halo device or a posterior approach.
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http://dx.doi.org/10.1007/978-3-319-62515-7_48DOI Listing
August 2019

On the Relationship between Dynamic Contrast-Enhanced Ultrasound Parameters and the Underlying Vascular Architecture Extracted from Acoustic Angiography.

Ultrasound Med Biol 2019 02 30;45(2):539-548. Epub 2018 Nov 30.

Department of Electrical Engineering, Technical University of Eindhoven, Eindhoven, The Netherlands.

Dynamic contrast-enhanced ultrasound (DCE-US) has been proposed as a powerful tool for cancer diagnosis by estimation of perfusion and dispersion parameters reflecting angiogenic vascular changes. This work was aimed at identifying which vascular features are reflected by the estimated perfusion and dispersion parameters through comparison with acoustic angiography (AA). AA is a high-resolution technique that allows quantification of vascular morphology. Three-dimensional AA and 2-D DCE-US bolus acquisitions were used to monitor the growth of fibrosarcoma tumors in nine rats. AA-derived vascular properties were analyzed along with DCE-US perfusion and dispersion to investigate the differences between tumor and control and their evolution in time. AA-derived microvascular density and DCE-US perfusion exhibited good agreement, confirmed by their spatial distributions. No vascular feature was correlated with dispersion. Yet, dispersion provided better cancer classification than perfusion. We therefore hypothesize that dispersion characterizes vessels that are smaller than those visible with AA.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2018.08.018DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6352898PMC
February 2019

Automated detection and classification of liver fibrosis stages using contourlet transform and nonlinear features.

Comput Methods Programs Biomed 2018 Nov 2;166:91-98. Epub 2018 Oct 2.

Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, 50603 Kuala Lumpur, Malaysia; School of Medicine, Faculty of Health and Medical Sciences, Taylor's University, 47500 Subang Jaya, Malaysia.

Background And Objective: Liver fibrosis is a type of chronic liver injury that is characterized by an excessive deposition of extracellular matrix protein. Early detection of liver fibrosis may prevent further growth toward liver cirrhosis and hepatocellular carcinoma. In the past, the only method to assess liver fibrosis was through biopsy, but this examination is invasive, expensive, prone to sampling errors, and may cause complications such as bleeding. Ultrasound-based elastography is a promising tool to measure tissue elasticity in real time; however, this technology requires an upgrade of the ultrasound system and software. In this study, a novel computer-aided diagnosis tool is proposed to automatically detect and classify the various stages of liver fibrosis based upon conventional B-mode ultrasound images.

Methods: The proposed method uses a 2D contourlet transform and a set of texture features that are efficiently extracted from the transformed image. Then, the combination of a kernel discriminant analysis (KDA)-based feature reduction technique and analysis of variance (ANOVA)-based feature ranking technique was used, and the images were then classified into various stages of liver fibrosis.

Results: Our 2D contourlet transform and texture feature analysis approach achieved a 91.46% accuracy using only four features input to the probabilistic neural network classifier, to classify the five stages of liver fibrosis. It also achieved a 92.16% sensitivity and 88.92% specificity for the same model. The evaluation was done on a database of 762 ultrasound images belonging to five different stages of liver fibrosis.

Conclusions: The findings suggest that the proposed method can be useful to automatically detect and classify liver fibrosis, which would greatly assist clinicians in making an accurate diagnosis.
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http://dx.doi.org/10.1016/j.cmpb.2018.10.006DOI Listing
November 2018

Contrast-enhanced ultrasound tractography for 3D vascular imaging of the prostate.

Sci Rep 2018 10 2;8(1):14640. Epub 2018 Oct 2.

Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands.

Diffusion tensor tractography (DTT) enables visualization of fiber trajectories in soft tissue using magnetic resonance imaging. DTT exploits the anisotropic nature of water diffusion in fibrous structures to identify diffusion pathways by generating streamlines based on the principal diffusion vector. Anomalies in these pathways can be linked to neural deficits. In a different field, contrast-enhanced ultrasound is used to assess anomalies in blood flow with the aim of locating cancer-induced angiogenesis. Like water diffusion, blood flow and transport of contrast agents also shows a principal direction; however, this is now determined by the local vasculature. Here we show how the tractographic techniques developed for magnetic resonance imaging DTT can be translated to contrast-enhanced ultrasound, by first estimating contrast flow velocity fields from contrast-enhanced ultrasound acquisitions, and then applying tractography. We performed 4D in-vivo contrast-enhanced ultrasound of three human prostates, proving the feasibility of the proposed approach with clinically acquired datasets. By comparing the results to histopathology after prostate resection, we observed qualitative agreement between the contrast flow tracts and typical markers of cancer angiogenic microvasculature: higher densities and tortuous geometries in tumor areas. The method can be used in-vivo using a standard contrast-enhanced ultrasound protocol, opening up new possibilities in the area of vascular characterization for cancer diagnostics.
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http://dx.doi.org/10.1038/s41598-018-32982-2DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6168586PMC
October 2018

Movement of giant lipid vesicles induced by millimeter wave radiation change when they contain magnetic nanoparticles.

Drug Deliv Transl Res 2019 02;9(1):131-143

Institute of Translational Pharmacology, CNR, Rome, Italy.

Superparamagnetic iron oxide nanoparticles are used in a rapidly expanding number of research and practical applications in biotechnology and biomedicine. Recent developments in iron oxide nanoparticle design and understanding of nanoparticle membrane interactions have led to applications in magnetically triggered, liposome delivery vehicles with controlled structure. Here we study the effect of external physical stimuli-such as millimeter wave radiation-on the induced movement of giant lipid vesicles in suspension containing or not containing iron oxide maghemite (γ-FeO) nanoparticles (MNPs). To increase our understanding of this phenomenon, we used a new microscope image-based analysis to reveal millimeter wave (MMW)-induced effects on the movement of the vesicles. We found that in the lipid vesicles not containing MNPs, an exposure to MMW induced collective reorientation of vesicle motion occurring at the onset of MMW switch "on." Instead, no marked changes in the movements of lipid vesicles containing MNPs were observed at the onset of first MMW switch on, but, importantly, by examining the course followed; once the vesicles are already irradiated, a directional motion of vesicles was induced. The latter vesicles were characterized by a planar motion, absence of gravitational effects, and having trajectories spanning a range of deflection angles narrower than vesicles not containing MNPs. An explanation for this observed delayed response could be attributed to the possible interaction of MNPs with components of lipid membrane that, influencing, e.g., phospholipids density and membrane stiffening, ultimately leads to change vesicle movement.
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http://dx.doi.org/10.1007/s13346-018-0572-yDOI Listing
February 2019

Multi-tissue and multi-scale approach for nuclei segmentation in H&E stained images.

Biomed Eng Online 2018 Jun 20;17(1):89. Epub 2018 Jun 20.

Biolab, Department of Electronics and Telecomunications, Politecnico di Torino, 10129, Turin, Italy.

Background: Accurate nuclei detection and segmentation in histological images is essential for many clinical purposes. While manual annotations are time-consuming and operator-dependent, full automated segmentation remains a challenging task due to the high variability of cells intensity, size and morphology. Most of the proposed algorithms for the automated segmentation of nuclei were designed for specific organ or tissues.

Results: The aim of this study was to develop and validate a fully multiscale method, named MANA (Multiscale Adaptive Nuclei Analysis), for nuclei segmentation in different tissues and magnifications. MANA was tested on a dataset of H&E stained tissue images with more than 59,000 annotated nuclei, taken from six organs (colon, liver, bone, prostate, adrenal gland and thyroid) and three magnifications (10×, 20×, 40×). Automatic results were compared with manual segmentations and three open-source software designed for nuclei detection. For each organ, MANA obtained always an F1-score higher than 0.91, with an average F1 of 0.9305 ± 0.0161. The average computational time was about 20 s independently of the number of nuclei to be detected (anyway, higher than 1000), indicating the efficiency of the proposed technique.

Conclusion: To the best of our knowledge, MANA is the first fully automated multi-scale and multi-tissue algorithm for nuclei detection. Overall, the robustness and versatility of MANA allowed to achieve, on different organs and magnifications, performances in line or better than those of state-of-art algorithms optimized for single tissues.
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http://dx.doi.org/10.1186/s12938-018-0518-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6011253PMC
June 2018