Publications by authors named "Massimo Salvi"

19 Publications

  • Page 1 of 1

A hybrid deep learning approach for gland segmentation in prostate histopathological images.

Artif Intell Med 2021 May 16;115:102076. Epub 2021 Apr 16.

Politecnico di Torino, PoliTo(BIO)Med Lab, Biolab, Department of Electronics and Telecommunications, Corso Duca degli Abruzzi 24, Turin, 10129, Italy.

Background: In digital pathology, the morphology and architecture of prostate glands have been routinely adopted by pathologists to evaluate the presence of cancer tissue. The manual annotations are operator-dependent, error-prone and time-consuming. The automated segmentation of prostate glands can be very challenging too due to large appearance variation and serious degeneration of these histological structures.

Method: A new image segmentation method, called RINGS (Rapid IdentificatioN of Glandural Structures), is presented to segment prostate glands in histopathological images. We designed a novel glands segmentation strategy using a multi-channel algorithm that exploits and fuses both traditional and deep learning techniques. Specifically, the proposed approach employs a hybrid segmentation strategy based on stroma detection to accurately detect and delineate the prostate glands contours.

Results: Automated results are compared with manual annotations and seven state-of-the-art techniques designed for glands segmentation. Being based on stroma segmentation, no performance degradation is observed when segmenting healthy or pathological structures. Our method is able to delineate the prostate gland of the unknown histopathological image with a dice score of 90.16 % and outperforms all the compared state-of-the-art methods.

Conclusions: To the best of our knowledge, the RINGS algorithm is the first fully automated method capable of maintaining a high sensitivity even in the presence of severe glandular degeneration. The proposed method will help to detect the prostate glands accurately and assist the pathologists to make accurate diagnosis and treatment. The developed model can be used to support prostate cancer diagnosis in polyclinics and community care centres.
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http://dx.doi.org/10.1016/j.artmed.2021.102076DOI Listing
May 2021

Automated assessment of glomerulosclerosis and tubular atrophy using deep learning.

Comput Med Imaging Graph 2021 Jun 2;90:101930. Epub 2021 May 2.

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

In kidney transplantations, pathologists evaluate the architecture of both glomeruli, interstitium and tubules to assess the nephron status. An accurate assessment of glomerulosclerosis and tubular atrophy is crucial for determining kidney acceptance, which is currently based on the pathologists' histological evaluations on renal biopsies in addition to clinical data. In this work, we present an automated algorithm, called RENTAG (Robust EvaluatioN of Tubular Atrophy & Glomerulosclerosis), for the segmentation and classification of glomerular and tubular structures in histopathological images. The proposed novel strategy combines the accuracy of a level-set with the semantic segmentation of convolutional neural networks to detect the glomeruli and tubules contours. In the TEST set, our method exhibited excellent performance in both glomeruli (dice score: 0.9529) and tubule (dice score: 0.9174) detection and outperformed all the compared methods. To the best of our knowledge, the RENTAG algorithm is the first fully automated method capable of quantifying glomerulosclerosis and tubular atrophy in digital histological images. The developed software can be employed for the analysis of pre-transplantation biopsies to support the pathologists' diagnostic activity.
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http://dx.doi.org/10.1016/j.compmedimag.2021.101930DOI Listing
June 2021

Histopathological Classification of Canine Cutaneous Round Cell Tumors Using Deep Learning: A Multi-Center Study.

Front Vet Sci 2021 26;8:640944. Epub 2021 Mar 26.

Department of Veterinary Sciences, University of Turin, Turin, Italy.

Canine cutaneous round cell tumors (RCT) represent one of the routine diagnostic challenges for veterinary pathologists. Computer-aided approaches are developed to overcome these restrictions and to increase accuracy and consistency of diagnosis. These systems are also of high benefit reducing errors when a large number of cases are screened daily. In this study we describe ARCTA (Automated Round Cell Tumors Assessment), a fully automated algorithm for cutaneous RCT classification and mast cell tumors grading in canine histopathological images. ARCTA employs a deep learning strategy and was developed on 416 RCT images and 213 mast cell tumors images. In the test set, our algorithm exhibited an excellent classification performance in both RCT classification (accuracy: 91.66%) and mast cell tumors grading (accuracy: 100%). Misdiagnoses were encountered for histiocytomas in the train set and for melanomas in the test set. For mast cell tumors the reduction of a grade was observed in the train set, but not in the test set. To the best of our knowledge, the proposed model is the first fully automated algorithm in histological images specifically developed for veterinary medicine. Being very fast (average computational time 2.63 s), this algorithm paves the way for an automated and effective evaluation of canine tumors.
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http://dx.doi.org/10.3389/fvets.2021.640944DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8044886PMC
March 2021

The Role in Teledermoscopy of an Inexpensive and Easy-to-Use Smartphone Device for the Classification of Three Types of Skin Lesions Using Convolutional Neural Networks.

Diagnostics (Basel) 2021 Mar 5;11(3). Epub 2021 Mar 5.

Department of Health Science, University of Eastern Piedmont, Via Solaroli 17, 28100 Novara, Italy.

Background: The use of teledermatology has spread over the last years, especially during the recent SARS-Cov-2 pandemic. Teledermoscopy, an extension of teledermatology, consists of consulting dermoscopic images, also transmitted through smartphones, to remotely diagnose skin tumors or other dermatological diseases. The purpose of this work was to verify the diagnostic validity of images acquired with an inexpensive smartphone microscope (Nurugo), employing convolutional neural networks (CNN) to classify malignant melanoma (MM), melanocytic nevus (MN), and seborrheic keratosis (SK).

Methods: The CNN, trained with 600 dermatoscopic images from the ISIC (International Skin Imaging Collaboration) archive, was tested on three test sets: ISIC images, images acquired with the Nurugo, and images acquired with a conventional dermatoscope.

Results: The results obtained, although with some limitations due to the smartphone device and small data set, were encouraging, showing comparable results to the clinical dermatoscope and up to 80% accuracy (out of 10 images, two were misclassified) using the Nurugo demonstrating how an amateur device can be used with reasonable levels of diagnostic accuracy.

Conclusion: Considering the low cost and the ease of use, the Nurugo device could be a useful tool for general practitioners (GPs) to perform the first triage of skin lesions, aiding the selection of lesions that require a face-to-face consultation with dermatologists.
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http://dx.doi.org/10.3390/diagnostics11030451DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8001064PMC
March 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 01 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

Automatic segmentation of ultrasound images of gastrocnemius medialis with different echogenicity levels using convolutional neural networks.

Annu Int Conf IEEE Eng Med Biol Soc 2020 07;2020:2113-2116

The purpose of this study was to develop an automatic method for the segmentation of muscle cross-sectional area on transverse B-mode ultrasound images of gastrocnemius medialis using a convolutional neural network(CNN). In the provided dataset images with both normal and increased echogenicity are present. The manually annotated dataset consisted of 591 images, from 200 subjects, 400 relative to subjects with normal echogenicity and 191 to subjects with augmented echogenicity. From the DICOM files, the image has been extracted and processed using the CNN, then the output has been post-processed to obtain a finer segmentation. Final results have been compared to the manual segmentations. Precision and Recall scores as mean ± standard deviation for training, validation, and test sets are 0.96 ± 0.05, 0.90 ± 0.18, 0.89 ± 0.15 and 0.97 ±0.03, 0.89± 0.17, 0.90 ± 0.14 respectively. The CNN approach has also been compared to another automatic algorithm, showing better performances. The proposed automatic method provides an accurate estimation of muscle cross-sectional area in muscles with different echogenicity levels.
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http://dx.doi.org/10.1109/EMBC44109.2020.9176343DOI Listing
July 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

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 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

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

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

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

Effect of low-level light therapy on diabetic foot ulcers: a near-infrared spectroscopy study.

J Biomed Opt 2017 03;22(3):38001

AOU Città della Salute e della Scienza di Torino-San Giovanni Antica Sede, Diabetology Department, Turin, Italy.

Diabetic foot ulcer (DFU) is a diabetic complication due to peripheral vasculopathy and neuropathy. A promising technology for wound healing in DFU is low-level light therapy (LLLT). Despite several studies showing positive effects of LLLT on DFU, LLLT’s physiological effects have not yet been studied. The objective of this study was to investigate vascular and nervous systems modification in DFU after LLLT. Two samples of 45 DFU patients and 11 healthy controls (HCs) were recruited. The total hemoglobin (totHb) concentration change was monitored before and after LLLT by near-infrared spectroscopy and analyzed in time and frequency domains. The spectral power of the totHb changes in the very-low frequency (VLF, 20 to 60 mHz) and low frequency (LF, 60 to 140 mHz) bandwidths was calculated. Data analysis revealed a mean increase of totHb concentration after LLLT in DFU patients, but not in HC. VLF/LF ratio decreased significantly after the LLLT period in DFU patients (indicating an increased activity of the autonomic nervous system), but not in HC. Eventually, different treatment intensities in LLLT therapy showed a different response in DFU. Overall, our results demonstrate that LLLT improves blood flow and autonomic nervous system regulation in DFU and the importance of light intensity in therapeutic protocols.
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http://dx.doi.org/10.1117/1.JBO.22.3.038001DOI Listing
March 2017

Fully Automated Muscle Ultrasound Analysis (MUSA): Robust and Accurate Muscle Thickness Measurement.

Ultrasound Med Biol 2017 01 6;43(1):195-205. Epub 2016 Oct 6.

Division of Physical Medicine and Rehabilitation, Department of Surgical Sciences, University of Turin, Turin, Italy; Division of Endocrinology, Diabetology and Metabolism, Department of Medical Sciences, University of Turin, Turin, Italy.

Musculoskeletal ultrasound imaging allows non-invasive measurement of skeletal muscle thickness. Current techniques generally suffer from manual operator dependency, while all the computer-aided approaches are limited to be semi-automatic or specifically optimized for a single muscle. The aim of this study was to develop and validate a fully automatic method, named MUSA (Muscle UltraSound Analysis), for measurement of muscle thickness on longitudinal ultrasound images acquired from different skeletal muscles. The MUSA algorithm was tested on a database of 200 B-mode ultrasound images of rectus femoris, vastus lateralis, tibialis anterior and medial gastrocnemius. The automatic muscle thickness measurements were compared to the manual measurements obtained by three operators. The MUSA algorithm achieved a 100% segmentation success rate, with mean differences between the automatic and manual measurements in the range of 0.06-0.45 mm. MUSA performance was statistically equal to the operators and its measurement accuracy was independent of the muscle thickness value.
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http://dx.doi.org/10.1016/j.ultrasmedbio.2016.08.032DOI Listing
January 2017