Multi-Class Segmentation of Aortic Branches and Zones in Computed Tomography Angiography: The AortaSeg24 Challenge

2 code implementations • 7 Feb 2025 • Muhammad Imran, Jonathan R. Krebs, Vishal Balaji Sivaraman, Teng Zhang, Amarjeet Kumar, Walker R. Ueland, Michael J. Fassler, Jinlong Huang, Xiao Sun, Lisheng Wang, Pengcheng Shi, Maximilian Rokuss, Michael Baumgartner, Yannick Kirchhof, Klaus H. Maier-Hein, Fabian Isensee, Shuolin Liu, Bing Han, Bong Thanh Nguyen, Dong-Jin Shin, Park Ji-Woo, Mathew Choi, Kwang-Hyun Uhm, Sung-Jea Ko, Chanwoong Lee, Jaehee Chun, Jin Sung Kim, Minghui Zhang, Hanxiao Zhang, Xin You, Yun Gu, Zhaohong Pan, Xuan Liu, Xiaokun Liang, Markus Tiefenthaler, Enrique Almar-Munoz, Matthias Schwab, Mikhail Kotyushev, Rostislav Epifanov, Marek Wodzinski, Henning Muller, Abdul Qayyum, Moona Mazher, Steven A. Niederer, Zhiwei Wang, Kaixiang Yang, Jintao Ren, Stine Sofia Korreman, Yuchong Gao, Hongye Zeng, Haoyu Zheng, Rui Zheng, Jinghua Yue, Fugen Zhou, Bo Liu, Alexander Cosman, Muxuan Liang, Chang Zhao, Gilbert R. Upchurch Jr., Jun Ma, Yuyin Zhou, Michol A. Cooper, Wei Shao

Furthermore, no open-source dataset is currently available to support the development of multi-class aortic segmentation methods.

Data Augmentation Segmentation

SegRap2023: A Benchmark of Organs-at-Risk and Gross Tumor Volume Segmentation for Radiotherapy Planning of Nasopharyngeal Carcinoma

1 code implementation • 15 Dec 2023 • Xiangde Luo, Jia Fu, Yunxin Zhong, Shuolin Liu, Bing Han, Mehdi Astaraki, Simone Bendazzoli, Iuliana Toma-Dasu, Yiwen Ye, Ziyang Chen, Yong Xia, Yanzhou Su, Jin Ye, Junjun He, Zhaohu Xing, Hongqiu Wang, Lei Zhu, Kaixiang Yang, Xin Fang, Zhiwei Wang, Chan Woong Lee, Sang Joon Park, Jaehee Chun, Constantin Ulrich, Klaus H. Maier-Hein, Nchongmaje Ndipenoch, Alina Miron, Yongmin Li, Yimeng Zhang, Yu Chen, Lu Bai, Jinlong Huang, Chengyang An, Lisheng Wang, Kaiwen Huang, Yunqi Gu, Tao Zhou, Mu Zhou, Shichuan Zhang, Wenjun Liao, Guotai Wang, Shaoting Zhang

The precise delineation of Gross Tumor Volumes (GTVs) and Organs-At-Risk (OARs) is crucial in radiation treatment, directly impacting patient prognosis.

Computed Tomography (CT) Image Segmentation +4

OpenKBP-Opt: An international and reproducible evaluation of 76 knowledge-based planning pipelines

1 code implementation • 16 Feb 2022 • Aaron Babier, Rafid Mahmood, Binghao Zhang, Victor G. L. Alves, Ana Maria Barragán-Montero, Joel Beaudry, Carlos E. Cardenas, Yankui Chang, Zijie Chen, Jaehee Chun, Kelly Diaz, Harold David Eraso, Erik Faustmann, Sibaji Gaj, Skylar Gay, Mary Gronberg, Bingqi Guo, Junjun He, Gerd Heilemann, Sanchit Hira, Yuliang Huang, Fuxin Ji, Dashan Jiang, Jean Carlo Jimenez Giraldo, Hoyeon Lee, Jun Lian, Shuolin Liu, Keng-Chi Liu, José Marrugo, Kentaro Miki, Kunio Nakamura, Tucker Netherton, Dan Nguyen, Hamidreza Nourzadeh, Alexander F. I. Osman, Zhao Peng, José Darío Quinto Muñoz, Christian Ramsl, Dong Joo Rhee, Juan David Rodriguez, Hongming Shan, Jeffrey V. Siebers, Mumtaz H. Soomro, Kay Sun, Andrés Usuga Hoyos, Carlos Valderrama, Rob Verbeek, Enpei Wang, Siri Willems, Qi Wu, Xuanang Xu, Sen yang, Lulin Yuan, Simeng Zhu, Lukas Zimmermann, Kevin L. Moore, Thomas G. Purdie, Andrea L. McNiven, Timothy C. Y. Chan

The dose predictions were input to four optimization models to form 76 unique KBP pipelines that generated 7600 plans.

Segmentation by Test-Time Optimization (TTO) for CBCT-based Adaptive Radiation Therapy

no code implementations • 8 Feb 2022 • Xiao Liang, Jaehee Chun, Howard Morgan, Ti Bai, Dan Nguyen, Justin C. Park, Steve Jiang

Firstly, we trained a population model with 200 patients, and then applied TTO to the remaining 39 test patients by refining the trained population model to obtain 39 individualized models.

Image Registration

Intentional Deep Overfit Learning (IDOL): A Novel Deep Learning Strategy for Adaptive Radiation Therapy

no code implementations • 23 Apr 2021 • Jaehee Chun, Justin C. Park, Sven Olberg, You Zhang, Dan Nguyen, Jing Wang, Jin Sung Kim, Steve Jiang

Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by utilizing the IDOL framework.

Super-Resolution