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 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).
Keywords
- Multi-atlas Segmentation (MAS)
- Fully Convolutional Neural Network
- Reference Deformation
- Ground Truth Segmentation
- Label Fusion Method
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Rohé, MM., Sermesant, M., Pennec, X. (2018). Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net. In: , et al. Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges. STACOM 2017. Lecture Notes in Computer Science(), vol 10663. Springer, Cham. https://doi.org/10.1007/978-3-319-75541-0_18
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DOI: https://doi.org/10.1007/978-3-319-75541-0_18
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