Multi-class prediction for improving intestine segmentation on non-fecal-tagged CT volume

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

doi:10.1117/12.2611441

4 April 2022 Multi-class prediction for improving intestine segmentation on non-fecal-tagged CT volume

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

Author Affiliations +

Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120331J (2022) https://doi.org/10.1117/12.2611441
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

This paper proposes an intestine segmentation method on CT volume based on a multi-class prediction of intestinal content materials (ICMs). The mechanical intestinal obstruction and the ileus (non-mechanical intestinal obstruction) are diseases which disrupt the movement of ICMs. Although clinicians find the obstruction point that movement of intestinal contents is required on CT volumes, it is difficult for non-expert clinicians to find the obstruction point. We have studied a CADe system which presents obstruction candidates to users by segmentation of the intestines on CT volumes. Generation of incorrect shortcuts in segmentation results was partly reduced in our proposed method by introducing distance maps. However, incorrect shortcuts still remained between the regions filled by air. This paper proposes an improved intestine segmentation method from CT volumes. We introduce a multi-class segmentation of ICMs (air, liquid, and feces). Reduction of incorrect shortcut generation is specifically applied to air regions. Experiments using 110 CT volumes showed that our proposed method reduced incorrect shortcuts. Rates of segmented regions that are analyzed as running through the intestine were 59.6% and 62.4% for the previous and proposed methods, respectively. This result partly implies that our proposed method reduced production of incorrect shortcuts.

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Hirohisa Oda, Yuichiro Hayashi, Takayuki Kitasaka, Aitaro Takimoto, Akinari Hinoki, Hiroo Uchida, Kojiro Suzuki, Masahiro Oda, and Kensaku Mori "Multi-class prediction for improving intestine segmentation on non-fecal-tagged CT volume", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120331J (4 April 2022); https://doi.org/10.1117/12.2611441

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