Matches in SemOpenAlex for { <https://semopenalex.org/work/W2617063304> ?p ?o ?g. }
Showing items 1 to 86 of
86
with 100 items per page.
- W2617063304 abstract "In this paper, we propose a fully automatic MRI cardiac segmentation method based on a novel deep convolutional neural network (CNN). As opposed to most cardiac segmentation methods which focus on the left ventricle (and especially the left cavity), our method segments both the left ventricular cavity, the left ventricular epicardium, and the right ventricular cavity. The novelty of our network lies in its maximum a posteriori loss function, which is specifically designed for the cardiac anatomy. Our loss function incorporates the cross-entropy of the predicted labels, the predicted contours, a cardiac shape prior, and an a priori term. Our model also 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). Those features are learned with a multi-resolution conv-deconv grid architecture which can be seen as an extension of the U-Net. We trained and tested our model on the ACDC MICCAI'17 challenge dataset of 150 patients whose diastolic and systolic images were manually outlined by 2 medical experts. Results reveal that our method can segment all three regions of a 3D MRI cardiac volume in $0.4$ second with an average Dice index of $0.90$, which is significantly better than state-of-the-art deep learning methods." @default.
- W2617063304 created "2017-06-05" @default.
- W2617063304 creator A5014239609 @default.
- W2617063304 creator A5046041670 @default.
- W2617063304 creator A5053536156 @default.
- W2617063304 creator A5072443458 @default.
- W2617063304 creator A5086581005 @default.
- W2617063304 date "2017-05-24" @default.
- W2617063304 modified "2023-09-27" @default.
- W2617063304 title "Novel Deep Convolution Neural Network Applied to MRI Cardiac Segmentation." @default.
- W2617063304 cites W1565952653 @default.
- W2617063304 cites W1901129140 @default.
- W2617063304 cites W1903029394 @default.
- W2617063304 cites W1987512289 @default.
- W2617063304 cites W2042089741 @default.
- W2617063304 cites W2057441779 @default.
- W2617063304 cites W2076426701 @default.
- W2617063304 cites W2083383847 @default.
- W2617063304 cites W2092465019 @default.
- W2617063304 cites W2095705004 @default.
- W2617063304 cites W2160754664 @default.
- W2617063304 cites W2167230651 @default.
- W2617063304 cites W2183032044 @default.
- W2617063304 cites W2223158884 @default.
- W2617063304 cites W2255189008 @default.
- W2617063304 cites W2337438617 @default.
- W2617063304 cites W2404618390 @default.
- W2617063304 cites W2584721867 @default.
- W2617063304 cites W2952637581 @default.
- W2617063304 cites W2964121744 @default.
- W2617063304 hasPublicationYear "2017" @default.
- W2617063304 type Work @default.
- W2617063304 sameAs 2617063304 @default.
- W2617063304 citedByCount "4" @default.
- W2617063304 countsByYear W26170633042017 @default.
- W2617063304 countsByYear W26170633042018 @default.
- W2617063304 countsByYear W26170633042019 @default.
- W2617063304 crossrefType "posted-content" @default.
- W2617063304 hasAuthorship W2617063304A5014239609 @default.
- W2617063304 hasAuthorship W2617063304A5046041670 @default.
- W2617063304 hasAuthorship W2617063304A5053536156 @default.
- W2617063304 hasAuthorship W2617063304A5072443458 @default.
- W2617063304 hasAuthorship W2617063304A5086581005 @default.
- W2617063304 hasConcept C108583219 @default.
- W2617063304 hasConcept C153180895 @default.
- W2617063304 hasConcept C154945302 @default.
- W2617063304 hasConcept C31972630 @default.
- W2617063304 hasConcept C34736171 @default.
- W2617063304 hasConcept C41008148 @default.
- W2617063304 hasConcept C81363708 @default.
- W2617063304 hasConcept C89600930 @default.
- W2617063304 hasConceptScore W2617063304C108583219 @default.
- W2617063304 hasConceptScore W2617063304C153180895 @default.
- W2617063304 hasConceptScore W2617063304C154945302 @default.
- W2617063304 hasConceptScore W2617063304C31972630 @default.
- W2617063304 hasConceptScore W2617063304C34736171 @default.
- W2617063304 hasConceptScore W2617063304C41008148 @default.
- W2617063304 hasConceptScore W2617063304C81363708 @default.
- W2617063304 hasConceptScore W2617063304C89600930 @default.
- W2617063304 hasLocation W26170633041 @default.
- W2617063304 hasOpenAccess W2617063304 @default.
- W2617063304 hasPrimaryLocation W26170633041 @default.
- W2617063304 hasRelatedWork W1901129140 @default.
- W2617063304 hasRelatedWork W2337438617 @default.
- W2617063304 hasRelatedWork W2606576226 @default.
- W2617063304 hasRelatedWork W2726153085 @default.
- W2617063304 hasRelatedWork W2894420216 @default.
- W2617063304 hasRelatedWork W2912684384 @default.
- W2617063304 hasRelatedWork W2914410118 @default.
- W2617063304 hasRelatedWork W2947683783 @default.
- W2617063304 hasRelatedWork W2950951916 @default.
- W2617063304 hasRelatedWork W2952757856 @default.
- W2617063304 hasRelatedWork W2979359868 @default.
- W2617063304 hasRelatedWork W2979912876 @default.
- W2617063304 hasRelatedWork W3011573465 @default.
- W2617063304 hasRelatedWork W3037866677 @default.
- W2617063304 hasRelatedWork W3039636409 @default.
- W2617063304 hasRelatedWork W3104785286 @default.
- W2617063304 hasRelatedWork W3121083384 @default.
- W2617063304 hasRelatedWork W3128931023 @default.
- W2617063304 hasRelatedWork W3152066958 @default.
- W2617063304 hasRelatedWork W3157749203 @default.
- W2617063304 isParatext "false" @default.
- W2617063304 isRetracted "false" @default.
- W2617063304 magId "2617063304" @default.
- W2617063304 workType "article" @default.