NDLI: Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks

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Ω-Net (Omega-Net): Fully automatic, multi-view cardiac MR detection, orientation, and segmentation with deep neural networks

Content Provider	Scilit
Author	Vigneault, Davis M. Xie, Weidi Ho, Carolyn Y. Bluemke, David A. Noble, J. Alison
Copyright Year	2018
Description	Journal: Medical Image Analysis Pixelwise segmentation of the left ventricular (LV) myocardium and the four cardiac chambers in 2-D steady state free precession (SSFP) cine sequences is an essential preprocessing step for a wide range of analyses. Variability in contrast, appearance, orientation, and placement of the heart between patients, clinical views, scanners, and protocols makes fully automatic semantic segmentation a notoriously difficult problem. Here, we present Ω-Net (Omega-Net): A novel convolutional neural network (CNN) architecture for simultaneous localization, transformation into a canonical orientation, and semantic segmentation. First, an initial segmentation is performed on the input image; second, the features learned during this initial segmentation are used to predict the parameters needed to transform the input image into a canonical orientation; and third, a final segmentation is performed on the transformed image. In this work, Ω-Nets of varying depths were trained to detect five foreground classes in any of three clinical views (short axis, SA; four-chamber, 4C; two-chamber, 2C), without prior knowledge of the view being segmented. This constitutes a substantially more challenging problem compared with prior work. The architecture was trained using three-fold cross-validation on a cohort of patients with hypertrophic cardiomyopathy (HCM, $N=42) and healthy control subjects (N=21). Network performance, as measured by weighted foreground intersection-over-union (IoU), was substantially improved for the best-performing Ω-Net compared with U-Net segmentation without localization or orientation (0.858 vs 0.834). In addition, to be comparable with other works, Ω-Net was retrained from scratch using five-fold cross-validation on the publicly available 2017 MICCAI Automated Cardiac Diagnosis Challenge (ACDC) dataset. The Ω-Net outperformed the state-of-the-art method in segmentation of the LV and RV bloodpools, and performed slightly worse in segmentation of the LV myocardium. We conclude that this architecture represents a substantive advancement over prior approaches, with implications for biomedical image segmentation more generally.$
Related Links	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7571050/pdf
ISSN	13618415
e-ISSN	13618423
DOI	10.1016/j.media.2018.05.008
Journal	Medical Image Analysis
Volume Number	48
Language	English
Publisher	Elsevier BV
Publisher Date	2018-05-22
Access Restriction	Open
Subject Keyword	Journal: Medical Image Analysis Radiology, Nuclear Medicine and Imaging Cardiac Magnetic Resonance Semantic Segmentation Deep Convolutional Neural Networks Spatial Transformer Networks
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Radiology, Nuclear Medicine and Imaging Health Informatics Computer Vision and Pattern Recognition Radiological and Ultrasound Technology

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