Body Maps: Towards 3D Atlas of Human Body
Creators: name: Li, Wenxuan
name: Chou, Yu-Cheng
name: Chen, Jieneng
name: Chen, Qi
name: Qu, Chongyu
name: Yuille, Alan
name: Zhou, Zongwei
name: Liu, Yaoyao
name: Wang, Angtian
name: Xiao, Junfei
name: Tang, Yucheng
name: Chen, Xiaoxi
name: Wang, Jincheng
name: Xue, Huimin
name: Chen, Yixiong
name: Ma, Yujiu
name: Lai, Yuxiang
name: Qiao, Hualin
name: Cao, Yining
name: Han, Haoqi
name: Li, Meihua
name: Lin, Xiaorui
name: Tang, Yutong
name: Xu, Jinghui
Publisher: name: Zenodo
Year: 2024
Subjects: Anatomical Structure Segmentation
Organ Volume Measurement
Domain Adaptation
Domain Generalization
Computed Tomography
ISBI Challenges

description: Variations in organ sizes and shapes can indicate a range of medical conditions, from benign anomalies to life-threatening diseases. Precise organ volume measurement is fundamental for effective patient care, but manual organ contouring is extremely time-consuming and exhibits considerable variability among expert radiologists. Artificial Intelligence (AI) holds the promise of improving volume measurement accuracy and reducing manual contouring efforts. We formulate our challenge as a semantic segmentation task, which automatically identifies and delineates the boundary of various anatomical structures essential for numerous downstream applications such as disease diagnosis, prognosis, and surgical planning. Our primary goal is to promote the development of AI algorithms and to benchmark the state of the art in this field. The BodyMaps challenge particularly focuses on assessing and improving the generalizability and efficiency of AI algorithms in medical segmentation across diverse clinical settings and patient demographics. In light of this, the innovation of our BodyMaps challenge includes the use of (1) large-scale, diverse datasets for both training and evaluating AI algorithms, (2) novel evaluation metrics that emphasize the accuracy of hard-to-segment anatomical structures, and (3) penalties for algorithms with extended inference times. Specifically, this challenge involves two unique datasets. First, AbdomenAtlas, the largest annotated dataset [Qu et al., 2023, Li et al., 2023], contains a total of 10,142 three-dimensional computed tomography (CT) volumes. In each CT volume, 25 anatomical structures are annotated by voxel. AbdomenAtlas is a multi-domain dataset of pre, portal, arterial, and delayed phase CT volumes collected from 88 global hospitals in 9 countries, diversified in age, pathological conditions, body parts, and race background. The AbdomenAtlas dataset will be made available to the public progressively during the challenge period and the participants are encouraged to use any other public/private datasets for training AI algorithms. Second, W-1K is a proprietary collection of 1,000 CT volumes, where 15 anatomical structures are annotated by voxel. The CT volumes and annotations of W-1K will be reserved for external validation of AI algorithms. The final score will be calculated on the W-1K dataset, measuring both segmentation performance and inference speed of the AI algorithms. Note that the segmentation performance will not only be limited to the average segmentation performance but also prioritize the performance of hard-to-segment structures. We hope our BodyMaps challenge can set the stage for larger-scale clinical trials and offer exceptional opportunities to practitioners in the medical imaging community.

References 

Qu, C., T. Zhang, H. Qiao, J. Liu, Y. Tang, A. L. Yuille, Z. Zhou. "AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three Weeks." In NeurIPS, 2023.

Li, W., A. L. Yuille, Z. Zhou. "How Well Do Supervised 3D Models Transfer to Medical Imaging Tasks?" 2023.