%0 Journal Article
%A Ma, Jun
%A Zhang, Yao
%A Gu, Song
%A Ge, Cheng
%A Mae, Shihao
%A Young, Adamo
%A Zhu, Cheng
%A Yang, Xin
%A Meng, Kangkang
%A Huang, Ziyan
%A Zhang, Fan
%A Pan, Yuanke
%A Huang, Shoujin
%A Wang, Jiacheng
%A Sun, Mingze
%A Zhang, Rongguo
%A Jia, Dengqiang
%A Choi, Jae Won
%A Alves, Natália
%A de Wilde, Bram
%A Koehler, Gregor
%A Lai, Haoran
%A Wang, Ershuai
%A Wiesenfarth, Manuel
%A Zhu, Qiongjie
%A Dong, Guoqiang
%A He, Jian
%A Wang, Bo
%A Maier-Hein, Klaus
%T Unleashing the strengths of unlabelled data in deep learning-assisted pan-cancer abdominal organ quantification: the FLARE22 challenge.
%J The lancet / Digital health
%V 6
%N 11
%@ 2589-7500
%C London
%I The Lancet
%M DKFZ-2024-02143
%P e815 - e826
%D 2024
%X Deep learning has shown great potential to automate abdominal organ segmentation and quantification. However, most existing algorithms rely on expert annotations and do not have comprehensive evaluations in real-world multinational settings. To address these limitations, we organised the FLARE 2022 challenge to benchmark fast, low-resource, and accurate abdominal organ segmentation algorithms. We first constructed an intercontinental abdomen CT dataset from more than 50 clinical research groups. We then independently validated that deep learning algorithms achieved a median dice similarity coefficient (DSC) of 90·0
%K Deep Learning
%K Humans
%K Tomography, X-Ray Computed
%K Algorithms
%K Abdomen: diagnostic imaging
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:39455194
%R 10.1016/S2589-7500(24)00154-7
%U https://inrepo02.dkfz.de/record/294310