Intestine segmentation from CT volume based on bidirectional teaching

Qin An; Hirohisa Oda; Yuichiro Hayashi; Takayuki Kitasaka; Akinari Hinoki; Hiroo Uchida; Kojiro Suzuki; Aitaro Takimoto; Masahiro Oda; Kensaku Mori

doi:10.1117/12.3006623

2 April 2024 Intestine segmentation from CT volume based on bidirectional teaching

Qin An, Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Aitaro Takimoto, Masahiro Oda, Kensaku Mori

Author Affiliations +

Proceedings Volume 12926, Medical Imaging 2024: Image Processing; 129260Z (2024) https://doi.org/10.1117/12.3006623
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Abstract

This paper proposes an intestine segmentation method to segment intestines from CT volumes for helping clinicians diagnose intestine obstruction. For large-scale labeled datasets, fully-supervised methods have shown superior results. However, medical image segmentation is usually difficult to achieve accurate prediction due to the limited number of labeled data available for training. To address this challenge, we introduce a novel multi-view symmetrical network (MVS-Net) for intestine segmentation and incorporate bidirectional teaching to utilize unlabeled datasets. Specifically, we design the MVS-Net, which can use different sizes of convolution kernels instead of a fixed kernel size, enabling the network to capture multi-scale features from images’ different perceptual fields and ensure segmentation accuracy. Additionally, the pseudo-labels are generated by bidirectional teaching, which can make the network captures semantic information from large-scale unlabeled data for increasing the training data. We repeated the experiment five times, and used the averaged result on the intestines dataset to represent the segmentation accuracy of the proposed method. The experimental results showed the average Dice was 78.86%, the average recall 84.50%, and the average precision 75.94%, respectively.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Qin An, Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Aitaro Takimoto, Masahiro Oda, and Kensaku Mori "Intestine segmentation from CT volume based on bidirectional teaching", Proc. SPIE 12926, Medical Imaging 2024: Image Processing, 129260Z (2 April 2024); https://doi.org/10.1117/12.3006623

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