Body Maps: Towards 3D Atlas of Human Body
Creators: name: Li, Wenxuan
name: Bassi, Pedro Ricardo Ariel Salvador
name: Tang, Yucheng
name: Li, Jianning
name: Zhou, Zongwei
name: Yuille, Alan
name: Chou, Yu-Cheng
name: Chen, Qi
name: Xiao, Junfei
name: Xiao, Junfei
name: Chen, Jieneng
name: Wang, Angtian
name: Liu, Yaoyao
name: Chen, Xiaoxi
name: Wang, Jincheng
name: Xue, Huimin
name: Chen, Yixiong
name: Qu, Chongyu
name: Lai, Yuxiang
name: Qiao, Hualin
name: Cao, Yining
name: Han, Haoqi
name: Lin, Xiaorui
name: Tang, Yutong
Publisher: name: Zenodo
Year: 2024
Subjects: Anatomical Structure Segmentation
Organ Volume Measurement
Domain Adaptation
Domain Generalization
Computed Tomography
MICCAI 2024 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, contains a total of 9,262 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 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.