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Proceedings Paper

Segmentation of 4D images via space-time neural networks
Author(s): Changjian Sun; Jayaram K. Udupa; Yubing Tong; Sanghun Sin; Mark Wagshul; Drew A. Torigian; Raanan Arens
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Paper Abstract

Medical imaging techniques currently produce 4D images that portray the dynamic behaviors and phenomena associated with internal structures. The segmentation of 4D images poses challenges different from those arising in segmenting 3D static images due to different patterns of variation of object shape and appearance in the space and time dimensions. In this paper, different network models are designed to learn the pattern of slice-to-slice change in the space and time dimensions independently. The two models then allow a gamut of strategies to actually segment the 4D image, such as segmentation following just the space or time dimension only, or following first the space dimension for one time instance and then following all time instances, or vice versa, etc. This paper investigates these strategies in the context of the obstructive sleep apnea (OSA) application and presents a unified deep learning framework to segment 4D images. Because of the sparse tubular nature of the upper airway and the surrounding low-contrast structures, inadequate contrast resolution obtainable in the magnetic resonance (MR) images leaves many challenges for effective segmentation of the dynamic airway in 4D MR images. Given that these upper airway structures are sparse, a Dice coefficient (DC) of ~0.88 for their segmentation based on our preferred strategy is similar to a DC of <0.95 for large non-sparse objects like liver, lungs, etc., constituting excellent accuracy.

Paper Details

Date Published: 28 February 2020
PDF: 6 pages
Proc. SPIE 11317, Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging, 113170J (28 February 2020); doi: 10.1117/12.2549605
Show Author Affiliations
Changjian Sun, Jilin Univ. (China)
Univ. of Pennsylvania (United States)
Jayaram K. Udupa, Univ. of Pennsylvania (United States)
Yubing Tong, Univ. of Pennsylvania (United States)
Sanghun Sin, Albert Einstein College of Medicine (United States)
Mark Wagshul, Albert Einstein College of Medicine (United States)
Drew A. Torigian, Univ. of Pennsylvania (United States)
Raanan Arens, Albert Einstein College of Medicine (United States)


Published in SPIE Proceedings Vol. 11317:
Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging
Andrzej Krol; Barjor S. Gimi, Editor(s)

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