FLARE 2022
MICCAI FLARE 2022挑战赛要求算法同时分割13个腹部器官，这是医学影像分析中的关键任务。该竞赛提供了迄今为止最大的腹部CT数据集，包含来自20多个中心的共计2300例三维CT影像数据。其中包含50例标注病例和2000例未标注病例用于训练，50例用于验证，200例用于最终测试和排名。
腹部可视化图片
可视化图片 1
可视化图片 2
数据集元信息
| 维度 | 3D |
| 模态 | ct |
| 任务类型 | segmentation |
| 解剖结构 | 肾脏、肝脏、胰腺、脾脏、胃、食管、十二指肠、肾上腺 |
| 解剖区域 | 腹部 |
| 类别数 | 13 |
| 数据量 | 50例标注+2000例未标注(训练集)；50例(验证集)；200例(测试集) |
| 文件格式 | .nii.gz |
文件结构
FLARE2022
├── Training
│ ├── FLARE22_LabeledCase50
│ │ ├── images
│ │ │ ├── FLARE22_Tr_0001_0000.nii.gz
│ │ │ ├── FLARE22_Tr_0002_0000.nii.gz
│ │ │ ├── ...
│ │ │ └── FLARE22_Tr_0050_0000.nii.gz
│ │ └── labels
│ │ ├── FLARE22_Tr_0001.nii.gz
│ │ ├── FLARE22_Tr_0002.nii.gz
│ │ ├── ...
│ │ └── FLARE22_Tr_0050.nii.gz
│ ├── FLARE22_UnlabeledCase1-1000
│ │ ├── Case_00001_0000.nii.gz
│ │ ├── Case_00002_0000.nii.gz
│ │ ├── ...
│ │ └── Case_01000_0000.nii.gz
│ └── FLARE22_UnlabeledCase1001-2000
│ ├── Case_01001_0000.nii.gz
│ ├── Case_01002_0000.nii.gz
│ ├── ...
│ └── Case_02000_0000.nii.gz
├── Tuning
│ ├── images
│ │ ├── FLARETs_0001_0000.nii.gz
│ │ ├── FLARETs_0002_0000.nii.gz
│ │ ├── ...
│ │ └── FLARETs_0050_0000.nii.gz
│ └── labels
│ ├── FLARETs_0001.nii.gz
│ ├── FLARETs_0002.nii.gz
│ ├── ...
│ └── FLARETs_0050.nii.gz
└── Testing
├── FLARETs_0001_0000.nii.gz
├── FLARETs_0002_0000.nii.gz
├── ...
└── FLARETs_0200_0000.nii.gz引用
@article{FLARE22,
author = {Jun Ma and Yao Zhang and Song Gu and Cheng Ge and Shihao Ma and Adamo Young and Cheng Zhu and Kangkang Meng and Xin Yang and Ziyan Huang and Fan Zhang and Wentao Liu and YuanKe Pan and Shoujin Huang and Jiacheng Wang and Mingze Sun and Weixin Xu and Dengqiang Jia and Jae Won Choi and Natália Alves and Bram de Wilde and Gregor Koehler and Yajun Wu and Manuel Wiesenfarth and Qiongjie Zhu and Guoqiang Dong and Jian He and the FLARE Challenge Consortium and Bo Wang},
title = {Unleashing the Strengths of Unlabeled Data in Pan-cancer Abdominal Organ Quantification: the FLARE22 Challenge},
year = {2023},
journal = {arXiv preprint arXiv:2308.05862},
}