AbdomenCT-1K Dataset

We present a large and diverse abdominal CT organ segmentation dataset, termed AbdomenCT-1K, with more than 1000 (1K) CT scans from 12 medical centers, including multi-phase, multi-vendor, and multi-disease cases.
Furthermore, we conduct a large-scale study for liver, kidney, spleen, and pancreas segmentation and reveal the unsolved segmentation problems of the SOTA methods, such as the limited generalization ability on distinct medical centers, phases, and unseen diseases.
To advance the unsolved problems, we further build four organ segmentation benchmarks for fully supervised, semi-supervised, weakly supervised, and continual learning, which are currently challenging and active research topics. Accordingly, we develop a simple and effective method for each benchmark, which can be used as out-of-the-box methods and strong baselines.
We believe the AbdomenCT-1K dataset will promote future in-depth research towards clinical applicable abdominal organ segmentation methods.

本报告呈现了一个庞大而多元化的腹部CT器官分割数据集，命名为AbdomenCT-1K，包含来自12家医疗中心的超过1000（1K）个CT扫描图像，其中涵盖了多相位、多厂商以及多疾病病例。此外，我们对肝脏、肾脏、脾脏和胰腺的分割进行了大规模研究，揭示了当前最先进方法（SOTA）在分割问题上的未解决难题，例如在特定医疗中心、不同相位和未知疾病上的泛化能力有限。为解决这些未解问题，我们进一步构建了四个器官分割基准，分别针对全监督、半监督、弱监督和持续学习，这些是目前极具挑战性和活跃的研究领域。相应地，针对每个基准，我们开发了一种简单而有效的方法，这些方法可作为现成方案和强有力的基线。我们坚信，AbdomenCT-1K数据集将推动未来对临床适用的腹部器官分割方法的深入研究。