Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge

Organ and cancer segmentation in abdomen Computed Tomography (CT) scans is the prerequisite for precise cancer diagnosis and treatment. Most existing benchmarks and algorithms are tailored to specific cancer types, limiting their ability to provide comprehensive cancer analysis. This work presents the first international competition on abdominal organ and pan-cancer segmentation by providing a large-scale and diverse dataset, including 4650 CT scans with various cancer types from over 40 medical centers. The winning team established a new state-of-the-art with a deep learning-based cascaded framework, achieving average Dice Similarity Coefficient (DSC) scores of 92.3% for organs and 64.9% for lesions on the hidden multi-national testing set. The dataset and code of top teams are publicly available, offering a benchmark platform to drive further innovations https://codalab.lisn.upsaclay.fr/competitions/12239. Abdomen organs are quite common cancer sites, such as colorectal cancer and pancreatic cancer, which are the second and third most common cause of cancer death [1]. Computed Tomography (CT) scanning yields important prognostic information for cancer patients and is a widely used imaging technology for cancer diagnosis and treatment monitoring [2]. In both clinical trials and daily clinical practice, radiologists and clinicians measure the tumor and organ on CT scans based on manual measurements (e.g., Response Evaluation Criteria In Solid Tumors (RECIST) criteria) [3]. However, this manual assessment is inherently subjective with considerable inter-and intra-expert variability and cannot measure the 3D tumor morphology. Deep learning-based methods have shown great potential for automatic tumor segmentation and quantification. Many challenges have been established to benchmark algorithm performance by providing standard datasets and fair evaluation platforms, such as the brain tumor segmentation (BraTS) [4], liver and liver tumor segmentation [5], kidney and kidney tumor segmentation [6], and pancreas and colon lesion segmentation [7].