4D CNN for Semantic Segmentation of Cardiac Volumetric Sequences

Autor:	Dong Yang, Daguang Xu, Holger R. Roth, Alvin Ihsani, Varun Buch, Mark Michalski, Sean Doyle, Andriy Myronenko, Neil A. Tenenholtz
Rok vydání:	2020
Předmět:	Computer science business.industry Volumetric data 3d segmentation Leverage (statistics) Segmentation Pattern recognition Artificial intelligence business Convolutional neural network
Zdroj:	Statistical Atlases and Computational Models of the Heart. Multi-Sequence CMR Segmentation, CRT-EPiggy and LV Full Quantification Challenges ISBN: 9783030390730 STACOM@MICCAI
Popis:	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 (video: https://drive.google.com/uc?id=1n-GJX5nviVs8R7tque2zy2uHFcN_Ogn1.).
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::adc2f7425306519a89bb465d7a923f6e https://doi.org/10.1007/978-3-030-39074-7_8 Zobrazit plný text záznamu