Multi-Planar Deep Segmentation Networks for Cardiac Substructures from MRI and CT

• Published in: Statistical Atlases and Computational Models of the Heart. ACDC and MMWHS Challenges Vol. 10663; pp. 199 - 206
• Main Authors: Mortazi, Aliasghar, Burt, Jeremy, Bagci, Ulas
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2018; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Cardiac magnetic resonance imaging; Cardiovascular disorders; Computed tomography; Convolutional neural network; Whole heart segmentation
• ISBN: 9783319755403; 3319755404
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-319-75541-0_21

Non-invasive detection of cardiovascular disorders from radiology scans requires quantitative image analysis of the heart and its substructures. There are well-established measurements that radiologists use for diseases assessment such as ejection fraction, volume of four chambers, and myocardium mass. These measurements are derived as outcomes of precise segmentation of the heart and its substructures. The aim of this paper is to provide such measurements through an accurate image segmentation algorithm that automatically delineates seven substructures of the heart from MRI and/or CT scans. Our proposed method is based on multi-planar deep convolutional neural networks (CNN) with an adaptive fusion strategy where we automatically utilize complementary information from different planes of the 3D scans for improved delineations. For CT and MRI, we have separately designed three CNNs (the same architectural configuration) for three planes, and have trained the networks from scratch for voxel-wise labeling for the following cardiac structures: myocardium of left ventricle (Myo), left atrium (LA), left ventricle (LV), right atrium (RA), right ventricle (RV), ascending aorta (Ao), and main pulmonary artery (PA). We have evaluated the proposed method with 4-fold-cross-validation on the multi-modality whole heart segmentation challenge (MM-WHS 2017) dataset. A precision and dice index of 0.93 and 0.90, and 0.87 and 0.85 were achieved for CT and MR images, respectively. Cardiac CT volume was segmented in about 50 s, with cardiac MRI segmentation requiring around 17 s with multi-GPU/CUDA implementation.