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.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Crowd-benchmarking tools Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!