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 computationally 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).
CITATION STYLE
Rohé, M. M., Sermesant, M., & Pennec, X. (2018). Automatic multi-atlas segmentation of myocardium with SVF-Net. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10663 LNCS, pp. 170–177). Springer Verlag. https://doi.org/10.1007/978-3-319-75541-0_18
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