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

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Title: Automatic Organ and Pan-cancer Segmentation in Abdomen CT: the FLARE 2023 Challenge
Authors: Ma, Jun, Zhang, Yao, Gu, Song, Ge, Cheng, Wang, Ershuai, Zhou, Qin, Huang, Ziyan, Lyu, Pengju, He, Jian, Wang, Bo
Publication Year: 2024
Collection: Computer Science
Subject Terms: Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Artificial Intelligence, Computer Science - Computer Vision and Pattern Recognition
More Details: 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 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.
Comment: MICCAI 2024 FLARE Challenge Summary
Document Type: Working Paper
Access URL: http://arxiv.org/abs/2408.12534
Accession Number: edsarx.2408.12534
Database: arXiv
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  Data: 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 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.<br />Comment: MICCAI 2024 FLARE Challenge Summary
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