Invert‐U‐Net DNN segmentation model for MRI cardiac left ventricle segmentation. Issue 16 (18th October 2018)

• Record Type: Journal Article
• Authors: Cong, Chao; Zhang, Hongmin
• Is Part Of: Journal of engineering. Volume 2018:Issue 16(2018)
• Page Start: 1463
• Page End: 1467
• Publication Date: 2018-10-18
• Subjects: image segmentation -- medical image processing -- biomedical MRI -- neural nets -- cardiology -- image filtering; invert‐U‐Net DNN segmentation model -- deeply supervised end‐to‐end model -- fully automated segmentation -- cardiac magnetic resonance imaging images -- deep neural network‐based segmentation -- innovative filter number structure -- MICCAI 2009 LV segmentation challenge -- MRI cardiac left ventricle segmentation -- Sunnybrook MR datasets -- MRI -- channel distribution; Engineering -- Periodicals; Engineering; Electronic journals; Periodicals; 620.005
• DOI: 10.1049/joe.2018.8302
• Languages: English
• ISSNs: 2051-3305

Abstract

Abstract : In this study, a deeply supervised end‐to‐end model is presented for fully automated segmentation for cardiac magnetic resonance imaging (MRI) images. Firstly, the mechanism of deep neural network (DNN)‐based segmentation is discussed with the relationship of channels and their distribution in network in depth. Following this idea, a U‐Net‐based model, namely an invert‐U‐Net model is presented with an innovative filter number structure. Based on the invert‐U‐Net model, the experiment is carefully designed, and the hyper‐parameters are considerately arranged. Finally, the model is applied and evaluated using Sunnybrook MR datasets from the MICCAI 2009 LV segmentation challenge and the experimental result shows that it outperforms the state‐of‐the‐art methods.