1,196 results match your criteria segmentation cnn

AI-based monitoring of retinal fluid in disease activity and under therapy.

Prog Retin Eye Res 2021 Jun 21:100972. Epub 2021 Jun 21.

Department of Ophthalmology Medical University of Vienna, Spitalgasse 23, 1090, Vienna, Austria. Electronic address:

Retinal fluid as the major biomarker in exudative macular disease is accurately visualized by high-resolution three-dimensional optical coherence tomography (OCT), which is used world-wide as a diagnostic gold standard largely replacing clinical examination. Artificial intelligence (AI) with its capability to objectively identify, localize and quantify fluid introduces fully automated tools into OCT imaging for personalized disease management. Deep learning performance has already proven superior to human experts, including physicians and certified readers, in terms of accuracy and speed. Read More

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Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance.

Int J Comput Assist Radiol Surg 2021 Jun 24. Epub 2021 Jun 24.

Department of Management, Production and Design Engineering, Polytechnic University of Turin, Turin, Italy.

Purpose: The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient's organ with its 2D endoscopic image, to assist surgeons during the procedure.

Methods: This approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model. Read More

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LayerCAM: Exploring Hierarchical Class Activation Maps.

IEEE Trans Image Process 2021 Jun 22;PP. Epub 2021 Jun 22.

The class activation maps are generated from the final convolutional layer of CNN. They can highlight discriminative object regions for the class of interest. These discovered object regions have been widely used for weakly-supervised tasks. Read More

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Mask-R[Formula: see text]CNN: a distance-field regression version of Mask-RCNN for fetal-head delineation in ultrasound images.

Int J Comput Assist Radiol Surg 2021 Jun 22. Epub 2021 Jun 22.

Department of Information Engineering, Università Politecnica delle Marche, Ancona, Italy.

Background And Objectives: Fetal head-circumference (HC) measurement from ultrasound (US) images provides useful hints for assessing fetal growth. Such measurement is performed manually during the actual clinical practice, posing issues relevant to intra- and inter-clinician variability. This work presents a fully automatic, deep-learning-based approach to HC delineation, which we named Mask-R[Formula: see text]CNN. Read More

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SCG: Saliency and Contour Guided Salient Instance Segmentation.

IEEE Trans Image Process 2021 Jun 21;PP. Epub 2021 Jun 21.

Different from conventional instance segmentation, salient instance segmentation (SIS) faces two difficulties. The first is that it involves segmenting salient instances only while ignoring background, and the second is that it targets generic object instances without pre-defined object categories. In this paper, based on the state-of-the-art Mask R-CNN model, we propose to leverage complementary saliency and contour information to handle these two challenges. Read More

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Multi skin lesions classification using fine-tuning and data-augmentation applying NASNet.

PeerJ Comput Sci 2021 3;7:e371. Epub 2021 Jun 3.

Computer Science, Universidad Tecnológica de Panamá, Panama, Panama.

Skin lesions are one of the typical symptoms of many diseases in humans and indicative of many types of cancer worldwide. Increased risks caused by the effects of climate change and a high cost of treatment, highlight the importance of skin cancer prevention efforts like this. The methods used to detect these diseases vary from a visual inspection performed by dermatologists to computational methods, and the latter has widely used automatic image classification applying Convolutional Neural Networks (CNNs) in medical image analysis in the last few years. Read More

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MHSU-Net: A more versatile neural network for medical image segmentation.

Comput Methods Programs Biomed 2021 Jun 6;208:106230. Epub 2021 Jun 6.

Department of Systems and Computer Engineering, Carleton University, Ottawa ON, K1S 5B6, Canada.

Background And Objective: Medical image segmentation plays an important role in clinic. Recently, with the development of deep learning, many convolutional neural network (CNN)-based medical image segmentation algorithms have been proposed. Among them, U-Net is one of the most famous networks. Read More

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Automatic Segmentation of Diffuse White Matter Abnormality on T2-weighted Brain MR Images Using Deep Learning in Very Preterm Infants.

Radiol Artif Intell 2021 May 3;3(3):e200166. Epub 2021 Feb 3.

Imaging Research Center, Department of Radiology (H.L., L.H.), and Perinatal Institute (H.L., V.S.P.I., N.A.P., L.H.), Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, MLC 5033, Cincinnati, OH 45229; Department of Electronic Engineering and Computing Systems, University of Cincinnati, Cincinnati, Ohio (M.C.); Deep MRI Imaging, Lewes, Del (J.W.); and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio (N.A.P., L.H.).

About 50%-80% of very preterm infants (VPIs) (≤ 32 weeks gestational age) exhibit diffuse white matter abnormality (DWMA) on their MR images at term-equivalent age. It remains unknown if DWMA is associated with developmental impairments, and further study is warranted. To aid in the assessment of DWMA, a deep learning model for DWMA quantification on T2-weighted MR images was developed. Read More

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Deep learning prediction of mild cognitive impairment conversion to Alzheimer's disease at 3 years after diagnosis using longitudinal and whole-brain 3D MRI.

PeerJ Comput Sci 2021 25;7:e560. Epub 2021 May 25.

Department of Radiology, Montefiore Medical Center, Albert Einstein College of Medicine, Bronx, NY, United States of America.

Background: While there is no cure for Alzheimer's disease (AD), early diagnosis and accurate prognosis of AD may enable or encourage lifestyle changes, neurocognitive enrichment, and interventions to slow the rate of cognitive decline. The goal of our study was to develop and evaluate a novel deep learning algorithm to predict mild cognitive impairment (MCI) to AD conversion at three years after diagnosis using longitudinal and whole-brain 3D MRI.

Methods: This retrospective study consisted of 320 normal cognition (NC), 554 MCI, and 237 AD patients. Read More

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Deep learning method for aortic root detection.

Comput Biol Med 2021 Jun 15;135:104533. Epub 2021 Jun 15.

Complejo Hospitalario Universitario de Santiago (CHUS), Santiago de Compostela, Spain.

Background: Computed tomography angiography (CTA) is a preferred imaging technique for a wide range of vascular diseases. However, extensive manual analysis is required to detect and identify several anatomical landmarks for clinical application. This study demonstrates the feasibility of a fully automatic method for detecting the aortic root, which is a key anatomical landmark in this type of procedure. Read More

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Automatic lung segmentation in COVID-19 patients: Impact on quantitative computed tomography analysis.

Phys Med 2021 Jun 7;87:115-122. Epub 2021 Jun 7.

Department of Medical Physics, ASST Grande Ospedale Metropolitano Niguarda, Piazza Ospedale Maggiore 3, 20162 Milan, Italy; Department of Physics, Università degli Studi di Milano, via Giovanni Celoria 16, 20133 Milan, Italy. Electronic address:

Purpose: To assess the impact of lung segmentation accuracy in an automatic pipeline for quantitative analysis of CT images.

Methods: Four different platforms for automatic lung segmentation based on convolutional neural network (CNN), region-growing technique and atlas-based algorithm were considered. The platforms were tested using CT images of 55 COVID-19 patients with severe lung impairment. Read More

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Automatic Scan Range Delimitation in Chest CT Using Deep Learning.

Radiol Artif Intell 2021 May 10;3(3):e200211. Epub 2021 Feb 10.

Department of Diagnostic and Interventional Radiology and Neuroradiology (A.D., M.S.K., M.C.S., L.U., K.N.) and Department of Radiotherapy (N.G.), University Hospital Essen, University of Duisburg-Essen, Hufelandstrasse 55, D-45147 Essen, Germany.

Purpose: To develop and evaluate fully automatic scan range delimitation for chest CT by using deep learning.

Materials And Methods: For this retrospective study, scan ranges were annotated by two expert radiologists in consensus in 1149 (mean age, 65 years ± 16 [standard deviation]; 595 male patients) chest CT topograms acquired between March 2002 and February 2019 (350 with pleural effusion, 376 with atelectasis, 409 with neither, 14 with both). A conditional generative adversarial neural network was trained on 1000 randomly selected topograms to generate virtual scan range delimitations. Read More

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Modeling Healthy Anatomy with Artificial Intelligence for Unsupervised Anomaly Detection in Brain MRI.

Radiol Artif Intell 2021 May 17;3(3):e190169. Epub 2021 Feb 17.

Department of Computer Aided Medical Procedures, Technical University of Munich, Boltzmannstr 3, 85748 Garching near Munich, Germany (C.B., N.N., S.A.); Department of Diagnostic and Interventional Neuroradiology (B.W., C.Z.) and Department of Neurology (M.M.), Klinikum Rechts der Isar, Technical University of Munich, Munich, Germany; and Whiting School of Engineering, Johns Hopkins University, Baltimore, Md (N.N.).

Purpose: To develop an unsupervised deep learning model on MR images of normal brain anatomy to automatically detect deviations indicative of pathologic states on abnormal MR images.

Materials And Methods: In this retrospective study, spatial autoencoders with skip-connections (which can learn to compress and reconstruct data) were leveraged to learn the normal variability of the brain from MR scans of healthy individuals. A total of 100 normal, in-house MR scans were used for training. Read More

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Image-driven classification of functioning and nonfunctioning pituitary adenoma by deep convolutional neural networks.

Comput Struct Biotechnol J 2021 14;19:3077-3086. Epub 2021 May 14.

State Key Laboratory of Oncology in South China, Cancer Center, Collaborative Innovation Center for Cancer Medicine, School of Life Science, Sun Yat-sen University, Guangzhou, Guangdong 510060, China.

The secreting function of pituitary adenomas (PAs) plays a critical role in making the treatment strategies. However, Magnetic Resonance Imaging (MRI) analysis for pituitary adenomas is labor intensive and highly variable among radiologists. In this work, by applying convolutional neural network (CNN), we built a segmentation and classification model to help distinguish functioning pituitary adenomas from non-functioning subtypes with 3D MRI images from 185 patients with PAs (two centers). Read More

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Automatic Recognition and Classification System of Thyroid Nodules in CT Images Based on CNN.

Comput Intell Neurosci 2021 27;2021:5540186. Epub 2021 May 27.

Key Laboratory of RF Circuits and Systems, Ministry of Education, Hangzhou Dianzi University, Hangzhou, Zhejiang, China.

Thyroid nodule lesions are one of the most common lesions of the thyroid; the incidence rate has been the highest in the past thirty years. X-ray computed tomography (CT) plays an increasingly important role in the diagnosis of thyroid diseases. Nonetheless, as a result of the artifact and high complexity of thyroid CT image, the traditional machine learning method cannot be applied to CT image processing. Read More

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Application of convolutional neural networks towards nuclei segmentation in localization-based super-resolution fluorescence microscopy images.

BMC Bioinformatics 2021 Jun 15;22(1):325. Epub 2021 Jun 15.

Biomedical Optical Imaging Laboratory, Departments of Medicine and Bioengineering, University of Pittsburgh, Pittsburgh, PA, 15213, USA.

Background: Automated segmentation of nuclei in microscopic images has been conducted to enhance throughput in pathological diagnostics and biological research. Segmentation accuracy and speed has been significantly enhanced with the advent of convolutional neural networks. A barrier in the broad application of neural networks to nuclei segmentation is the necessity to train the network using a set of application specific images and image labels. Read More

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Automated Brain Masking of Fetal Functional MRI with Open Data.

Neuroinformatics 2021 Jun 15. Epub 2021 Jun 15.

Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, NY, USA.

Fetal resting-state functional magnetic resonance imaging (rs-fMRI) has emerged as a critical new approach for characterizing brain development before birth. Despite the rapid and widespread growth of this approach, at present, we lack neuroimaging processing pipelines suited to address the unique challenges inherent in this data type. Here, we solve the most challenging processing step, rapid and accurate isolation of the fetal brain from surrounding tissue across thousands of non-stationary 3D brain volumes. Read More

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A convolutional neural network for common coordinate registration of high-resolution histology images.

Bioinformatics 2021 Jun 15. Epub 2021 Jun 15.

Center for Computational Biology, Flatiron Institute, New York, NY, 10010, USA.

Motivation: Registration of histology images from multiple sources is a pressing problem in large-scale studies of spatial -omics data. Researchers often perform "common coordinate registration," akin to segmentation, in which samples are partitioned based on tissue type to allow for quantitative comparison of similar regions across samples. Accuracy in such registration requires both high image resolution and global awareness, which mark a difficult balancing act for contemporary deep learning architectures. Read More

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A self-supervised learning strategy for postoperative brain cavity segmentation simulating resections.

Int J Comput Assist Radiol Surg 2021 Jun 13. Epub 2021 Jun 13.

School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.

Purpose: Accurate segmentation of brain resection cavities (RCs) aids in postoperative analysis and determining follow-up treatment. Convolutional neural networks (CNNs) are the state-of-the-art image segmentation technique, but require large annotated datasets for training. Annotation of 3D medical images is time-consuming, requires highly trained raters and may suffer from high inter-rater variability. Read More

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MIDeepSeg: Minimally interactive segmentation of unseen objects from medical images using deep learning.

Med Image Anal 2021 May 18;72:102102. Epub 2021 May 18.

School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, China; SenseTime Research, Shanghai, China.

Segmentation of organs or lesions from medical images plays an essential role in many clinical applications such as diagnosis and treatment planning. Though Convolutional Neural Networks (CNN) have achieved the state-of-the-art performance for automatic segmentation, they are often limited by the lack of clinically acceptable accuracy and robustness in complex cases. Therefore, interactive segmentation is a practical alternative to these methods. Read More

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Abdominal Aortic Aneurysm Segmentation Using Convolutional Neural Networks Trained with Images Generated with a Synthetic Shape Model.

Mach Learn Med Eng Cardiovasc Health Intravasc Imaging Comput Assist Stenting (2019) 2019 12;11794:167-174. Epub 2019 Oct 12.

Applied Chest Imaging Laboratory, Brigham and Women's Hospital, Harvard medical school, Boston, USA.

An abdominal aortic aneurysm (AAA) is a ballooning of the abdominal aorta, that if not treated tends to grow and rupture. Computed Tomography Angiography (CTA) is the main imaging modality for the management of AAAs, and segmenting them is essential for AAA rupture risk and disease progression assessment. Previous works have shown that Convolutional Neural Networks (CNNs) can accurately segment AAAs, but have the limitation of requiring large amounts of annotated data to train the networks. Read More

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October 2019

Robust R-Peak Detection in Low-Quality Holter ECGs using 1D Convolutional Neural Network.

IEEE Trans Biomed Eng 2021 Jun 10;PP. Epub 2021 Jun 10.

Objective: Noise and low quality of ECG signals acquired from Holter or wearable devices deteriorate the accuracy and robustness of R-peak detection algorithms. This paper presents a generic and robust system for R-peak detection in Holter ECG signals. While many proposed algorithms have successfully addressed the problem of ECG R-peak detection, there is still a notable gap in the performance of these detectors on such low-quality ECG records. Read More

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A Persistent Homology-Based Topological Loss Function for Multi-class CNN Segmentation of Cardiac MRI.

Stat Atlases Comput Models Heart 2020 29;2020:3-13. Epub 2021 Jan 29.

School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

With respect to spatial overlap, CNN-based segmentation of short axis cardiovascular magnetic resonance (CMR) images has achieved a level of performance consistent with inter observer variation. However, conventional training procedures frequently depend on pixel-wise loss functions, limiting optimisation with respect to extended or global features. As a result, inferred segmentations can lack spatial coherence, including spurious connected components or holes. Read More

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January 2021

Broad Dataset and Methods for Counting and Localization of On-Ear Corn Kernels.

Front Robot AI 2021 14;8:627009. Epub 2021 May 14.

Intelinair, Inc., Yerevan, Armenia.

Crop monitoring and yield prediction are central to management decisions for farmers. One key task is counting the number of kernels on an ear of corn to estimate yield in a field. As ears of corn can easily have 400-900 kernels, manual counting is unrealistic; traditionally, growers have approximated the number of kernels on an ear of corn through a mixture of counting and estimation. Read More

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Efficient high cone-angle artifact reduction in circular cone-beam CT using deep learning with geometry-aware dimension reduction.

Phys Med Biol 2021 Jun 9. Epub 2021 Jun 9.

Centrum Wiskunde en Informatica, Amsterdam, Noord-Holland, NETHERLANDS.

High cone-angle artifacts (HCAAs) appear frequently in circular cone-beam computed tomography (CBCT) images and can heavily affect diagnosis and treatment planning. To reduce HCAAs in CBCT scans, we propose a novel deep learning approach that reduces the three dimensional (3D) nature of HCAAs to two-dimensional (2D) problems in an efficient way. Specifically, we exploit the relationship between HCAAs and the rotational scanning geometry by training a convolutional neural network (CNN) using image slices that were radially sampled from CBCT scans. Read More

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Fully Automated Segmentation of Brain Tumor from Multiparametric MRI Using 3D Context Deep Supervised U-Net.

Med Phys 2021 Jun 8. Epub 2021 Jun 8.

Department of Radiation Oncology and Winship Cancer Institute, Emory University, Atlanta, GA, 30322, USA.

Purpose: Owing to histologic complexities of brain tumors, its diagnosis requires the use of multi-modalities to obtain valuable structural information so that brain tumor subregions can be properly delineated. In current clinical workflow, physicians typically perform slice by slice delineation of brain tumor subregions, which is a time-consuming process and also more susceptible to intra- and inter-rater variabilities possibly leading to misclassification. To deal with this issue, this study aims to develop an automatic segmentation of brain tumor in MR images using deep learning. Read More

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Image Stitching Based on Semantic Planar Region Consensus.

IEEE Trans Image Process 2021 14;30:5545-5558. Epub 2021 Jun 14.

Image stitching for two images without a global transformation between them is notoriously difficult. In this paper, noticing the importance of semantic planar structures under perspective geometry, we propose a new image stitching method which stitches images by allowing for the alignment of a set of matched dominant semantic planar regions. Clearly different from previous methods resorting to plane segmentation, the key to our approach is to utilize rich semantic information directly from RGB images to extract semantic planar image regions with a deep Convolutional Neural Network (CNN). Read More

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Automated and robust organ segmentation for 3D-based internal dose calculation.

EJNMMI Res 2021 Jun 7;11(1):53. Epub 2021 Jun 7.

ABX - CRO advanced pharmaceutical services, Dresden, Germany.

Purpose: In this work, we address image segmentation in the scope of dosimetry using deep learning and make three main contributions: (a) to extend and optimize the architecture of an existing convolutional neural network (CNN) in order to obtain a fast, robust and accurate computed tomography (CT)-based organ segmentation method for kidneys and livers; (b) to train the CNN with an inhomogeneous set of CT scans and validate the CNN for daily dosimetry; and (c) to evaluate dosimetry results obtained using automated organ segmentation in comparison with manual segmentation done by two independent experts.

Methods: We adapted a performant deep learning approach using CT-images to delineate organ boundaries with sufficiently high accuracy and adequate processing time. The segmented organs were consequently used as binary masks for further convolution with a point spread function to retrieve the activity values from quantitatively reconstructed SPECT images for "volumetric"/3D dosimetry. Read More

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Automatic coronary artery calcium scoring from unenhanced-ECG-gated CT using deep learning.

Diagn Interv Imaging 2021 Jun 4. Epub 2021 Jun 4.

Radiology Department, AP-HP, Hôpital Européen Georges Pompidou, Georges Pompidou, Université de Paris, PARCC, INSERM, 75015 Paris, France.

Purpose: The purpose of this study was to develop and evaluate an algorithm that can automatically estimate the amount of coronary artery calcium (CAC) from unenhanced electrocardiography (ECG)-gated computed tomography (CT) cardiac volume acquisitions by using convolutional neural networks (CNN).

Materials And Methods: The method used a set of five CNN with three-dimensional (3D) U-Net architecture trained on a database of 783 CT examinations to detect and segment coronary artery calcifications in a 3D volume. The Agatston score, the conventional CAC scoring, was then computed slice by slice from the resulting segmentation mask and compared to the ground truth manually estimated by radiologists. Read More

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A Hybrid Feature Selection based Brain Tumor Detection and Segmentation in Multiparametric Magnetic Resonance Imaging.

Med Phys 2021 Jun 5. Epub 2021 Jun 5.

School of Sport and Health Sciences, Xi'an Physical Education University, Xi'an, 710068, China.

Purpose: To develop a novel method based on feature selection, combining convolutional neural network (CNN) and ensemble learning (EL), to achieve high accuracy and efficiency of glioma detection and segmentation using multiparametric MRIs.

Methods: We proposed an evolutionary feature selection-based hybrid approach for glioma detection and segmentation on 4 MR sequences (T2-FLAIR, T1, T1Gd, and T2). First, we trained a lightweight CNN to detect glioma and mask the suspected region to process large batch of MRI images. Read More

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