BESNet: Boundary-Enhanced Segmentation of Cells in Histopathological Images. - Hirohisa Oda; Holger R. Roth; Kosuke Chiba; Jure Sokolic; Takayuki Kitasaka; Masahiro Oda 001; Akinari Hinoki; Hiroo Uchida; Julia A. Schnabel; Kensaku Mori (MICCAI (2), 2018)

Spaciousness Filters for Non-contrast CT Volume Segmentation of the Intestine Region for Emergency Ileus Diagnosis. - Hirohisa Oda; Kohei Nishio; Takayuki Kitasaka; Benjamin Villard; Hizuru Amano; Kosuke Chiba; Akinari Hinoki; Hiroo Uchida; Kojiro Suzuki; Hayato Itoh; Masahiro Oda 001; Kensaku Mori (UNSURE/CLIP@MICCAI, 2019)

Intestine Segmentation with Small Computational Cost for Diagnosis Assistance of Ileus and Intestinal Obstruction. - Hirohisa Oda; Yuichiro Hayashi; Takayuki Kitasaka; Aitaro Takimoto; Akinari Hinoki; Hiroo Uchida; Kojiro Suzuki; Masahiro Oda 001; Kensaku Mori (CLIP/DCL/LL-COVID19/PPML@MICCAI, 2021)

Intestinal region reconstruction of ileus cases from 3D CT images based on graphical representation and its visualization. - Hirohisa Oda; Yuichiro Hayashi; Takayuki Kitasaka; Yudai Tamada; Aitaro Takimoto; Akinari Hinoki; Hiroo Uchida; Kojiro Suzuki; Hayato Itoh; Masahiro Oda 001; Kensaku Mori (Computer-Aided Diagnosis, 2021)

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 001; Kensaku Mori (Computer-Aided Diagnosis, 2022)

M U-Net: Intestine Segmentation Using Multi-dimensional Features for Ileus Diagnosis Assistance. - Qin An; Hirohisa Oda; Yuichiro Hayashi; Takayuki Kitasaka; Akinari Hinoki; Hiroo Uchida; Kojiro Suzuki; Aitaro Takimoto; Masahiro Oda 001; Kensaku Mori (AMAI@MICCAI, 2023)

HTSeg: Hybrid Two-Stage Segmentation Framework for Intestine Segmentation from CT Volumes. - Qin An; Hirohisa Oda; Yuichiro Hayashi; Takayuki Kitasaka; Aitaro Takimoto; Akinari Hinoki; Hiroo Uchida; Kojiro Suzuki; Masahiro Oda 001; Kensaku Mori (CLIP@MICCAI, 2024)

Multi-dimensional consistency learning between 2D Swin U-Net and 3D U-Net for intestine segmentation from CT volume. - Qin An; Hirohisa Oda; Yuichiro Hayashi; Takayuki Kitasaka; Hiroo Uchida; Akinari Hinoki; Kojiro Suzuki; Aitaro Takimoto; Masahiro Oda 001; Kensaku Mori (Int. J. Comput. Assist. Radiol. Surg., 2025)