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Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net

Abstract : Segmentation of the myocardium is a key step for image guided diagnosis in many cardiac diseases. In this article, we propose an automatic multi-atlas segmentation framework which relies on a very fast registration algorithm trained with convolutional neural networks. The speed of this registration method allows us to use a high number of templates in the multi-atlas segmentation while remaining computation-ally tractable. The performance of the propose approach is evaluated on a dataset of 100 end-diastolic and end-systolic MRI images of the STACOM 2017 Automated Cardiac Diagnosis Challenge (ACDC).
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Contributor : Marc-Michel Rohé <>
Submitted on : Friday, August 18, 2017 - 5:01:52 PM
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Marc-Michel Rohé, Maxime Sermesant, Xavier Pennec. Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net. STACOM: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges, Sep 2017, Québec, Canada. pp.170-177, ⟨10.1007/978-3-319-75541-0_18⟩. ⟨hal-01575297⟩



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