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Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images

Chen C, Biffi C, Tarroni G, Petersen S, Bai W, Rueckert D (2019)

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Publication Type: Conference contribution

Publication year: 2019

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 11765 LNCS

Pages Range: 523-531

Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Event location: Shenzhen, CHN

ISBN: 9783030322441

DOI: 10.1007/978-3-030-32245-8_58

Abstract

Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.

Involved external institutions

How to cite

APA:

Chen, C., Biffi, C., Tarroni, G., Petersen, S., Bai, W., & Rueckert, D. (2019). Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 523-531). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.

MLA:

Chen, Chen, et al. "Learning Shape Priors for Robust Cardiac MR Segmentation from Multi-view Images." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 523-531.

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