Automatic Multi-Atlas Segmentation of Myocardium with SVF-Net

• Published in: Lecture notes in computer science Vol. 10663; pp. 170 - 177
• Main Authors: Rohé, Marc-Michel, Sermesant, Maxime, Pennec, Xavier
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Fully Convolutional Neural Network; Ground Truth Segmentation; Label Fusion Method; Multi-atlas Segmentation (MAS); Reference Deformation
• ISBN: 9783319755403; 3319755404
• ISSN: 0302-9743; 1611-3349; 1611-3349
• DOI: 10.1007/978-3-319-75541-0_18

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).