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4D CNN for semantic segmentation of cardiac volumetric sequences
lib:a4d46827edae884b (v1.0.0)
Authors: Andriy Myronenko,Dong Yang,Varun Buch,Daguang Xu,Alvin Ihsani,Sean Doyle,Mark Michalski,Neil Tenenholtz,Holger Roth
ArXiv: 1906.07295
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1906.07295v2
We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. While only a small subset of volumes in the temporal sequence is annotated, we define a sparse loss function on available labels to allow the network to leverage unlabeled images during training and generate a fully segmented sequence. We investigate the accuracy of the proposed 4D network to predict temporally consistent segmentations and compare with traditional 3D segmentation approaches. We demonstrate the feasibility of the 4D CNN and establish its performance on cardiac 4D CCTA.
ArXiv: 1906.07295
Document: PDF DOI
Abstract URL: https://arxiv.org/abs/1906.07295v2
We propose a 4D convolutional neural network (CNN) for the segmentation of retrospective ECG-gated cardiac CT, a series of single-channel volumetric data over time. While only a small subset of volumes in the temporal sequence is annotated, we define a sparse loss function on available labels to allow the network to leverage unlabeled images during training and generate a fully segmented sequence. We investigate the accuracy of the proposed 4D network to predict temporally consistent segmentations and compare with traditional 3D segmentation approaches. We demonstrate the feasibility of the 4D CNN and establish its performance on cardiac 4D CCTA.
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