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Proceedings Paper

Incorporating minimal user input into deep-learning-based image segmentation
Author(s): Maysam Shahedi; Martin Halicek; James D. Dormer; Baowei Fei
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Paper Abstract

Computer-assisted image segmentation techniques could help clinicians to perform the border delineation task faster with lower inter-observer variability. Recently, convolutional neural networks (CNNs) are widely used for automatic image segmentation. In this study, we used a technique to involve observer inputs for supervising CNNs to improve the accuracy of the segmentation performance. We added a set of sparse surface points as an additional input to supervise the CNNs for more accurate image segmentation. We tested our technique by applying minimal interactions to supervise the networks for segmentation of the prostate on magnetic resonance images. We used U-Net and a new network architecture that was based on U-Net (dual-input path [DIP] U-Net), and showed that our supervising technique could significantly increase the segmentation accuracy of both networks as compared to fully automatic segmentation using U-Net. We also showed DIP U-Net outperformed U-Net for supervised image segmentation. We compared our results to the measured inter-expert observer difference in manual segmentation. This comparison suggests that applying about 15 to 20 selected surface points can achieve a performance comparable to manual segmentation.

Paper Details

Date Published: 10 March 2020
PDF: 8 pages
Proc. SPIE 11313, Medical Imaging 2020: Image Processing, 1131313 (10 March 2020); doi: 10.1117/12.2549716
Show Author Affiliations
Maysam Shahedi, The Univ. of Texas at Dallas (United States)
Martin Halicek, The Univ. of Texas at Dallas (United States)
Emory Univ. (United States)
Georgia Institute of Technology (United States)
James D. Dormer, The Univ. of Texas at Dallas (United States)
Baowei Fei, The Univ. of Texas at Dallas (United States)
Univ. of Texas Southwestern Medical Ctr. (United States)


Published in SPIE Proceedings Vol. 11313:
Medical Imaging 2020: Image Processing
Ivana Išgum; Bennett A. Landman, Editor(s)

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