0000000000335747

AUTHOR

Clement Zotti

showing 3 related works from this author

GridNet with Automatic Shape Prior Registration for Automatic MRI Cardiac Segmentation

2018

In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN) designed for the 2017 ACDC MICCAI challenge. The novelty of our network comes with its embedded shape prior and its loss function tailored to the cardiac anatomy. Our model includes a cardiac center-of-mass regression module which allows for an automatic shape prior registration. Also, since our method processes raw MR images without any manual preprocessing and/or image cropping, our CNN learns both high-level features (useful to distinguish the heart from other organs with a similar shape) and low-level features (useful to get accurate segmentation results).…

Cardiac anatomybusiness.industryComputer scienceComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONNovelty030204 cardiovascular system & hematologyGridConvolutional neural networkAccurate segmentation030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineFully automaticPreprocessorSegmentationComputer visionArtificial intelligencebusiness
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Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

2018

Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the “Automatic Cardiac Diagnosis Challenge” dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how f…

MaleDatabases FactualHeart DiseasesComputer science[SDV]Life Sciences [q-bio]Lleft and right ventricles030218 nuclear medicine & medical imagingTask (project management)Cardiac segmentation and diagnosis03 medical and health sciences0302 clinical medicineDeep LearningImage Interpretation Computer-AssistedmedicineMedical imagingHumansSegmentationElectrical and Electronic EngineeringRadiological and Ultrasound Technologymedicine.diagnostic_testbusiness.industryMyocardiumDeep learningMagnetic resonance imagingPattern recognitionHeartImage segmentationMagnetic Resonance ImagingComputer Science ApplicationsCardiac Imaging Techniquesmedicine.anatomical_structureVentricleFemaleArtificial intelligencebusinessCardiac magnetic resonanceLeft and right ventricles030217 neurology & neurosurgerySoftwareMRIIEEE transactions on medical imaging
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Convolutional Neural Network With Shape Prior Applied to Cardiac MRI Segmentation.

2019

In this paper, we present a novel convolutional neural network architecture to segment images from a series of short-axis cardiac magnetic resonance slices (CMRI). The proposed model is an extension of the U-net that embeds a cardiac shape prior and involves a loss function tailored to the cardiac anatomy. Since the shape prior is computed offline only once, the execution of our model is not limited by its calculation. Our system takes as input raw magnetic resonance images, requires no manual preprocessing or image cropping and is trained to segment the endocardium and epicardium of the left ventricle, the endocardium of the right ventricle, as well as the center of the left ventricle. Wit…

Databases FactualComputer scienceHealth InformaticsImage processingConvolutional neural network030218 nuclear medicine & medical imaging03 medical and health sciences0302 clinical medicineHealth Information ManagementSørensen–Dice coefficientImage Processing Computer-AssistedHumansElectrical and Electronic EngineeringArtificial neural networkbusiness.industryMedical image computingCenter (category theory)Pattern recognitionHeartImage segmentationMagnetic Resonance ImagingComputer Science ApplicationsCardiac Imaging TechniquesHausdorff distancecardiovascular systemArtificial intelligenceNeural Networks Computerbusiness030217 neurology & neurosurgeryIEEE journal of biomedical and health informatics
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