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--- |
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license: cc-by-nc-4.0 |
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--- |
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# FLARE Task3 Domain Adaptation Dataset |
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## Data Description |
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This is the dataset for [MICCAI FLARE 2024-2025 Task3: Unsupervised Domain Adaptation for Abdominal Organ Segmentation in MRI and PET Scans](https://www.codabench.org/competitions/2296/) |
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The participants are encouraged to develop efficient abdominal organ segmentation models for MRI and PET scans with labeled and pseudo-labeled CT scans and unlabeled MRI and PET scans. |
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The **training set** holds 2050 CT scans, where 50 cases have ground-truth labels from the FLARE22 dataset, and the remaining 2000 cases have pseudo labels generated by the FLARE 2022 winning solution. |
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For domain adaptation, the training set also contains 4817 unlabeled MRI scans and 1000 unlabeled PET scans. For those participants who are constrained by computing resources, we also provide an **unlabeled core set** to develop the methods, where 100 unlabeled MRI and 100 unlabeled PET scans are sampled from the original MRI and PET training set. |
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The **validation set** contains 110 MRI scans and 50 PET scans, while the **testing set** contains 300 MRI scans and 200 PET scans. |
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## Task Setting |
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The participants should develop two domain adaptive models, one for MRI segmentation and the other for PET segmentation. |
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In MRI scans, there are **13 organs and tissues** for segmentation, including the liver (labeled 1), right kidney (labeled 2), spleen (labeled 3), pancreas (labeled 4), aorta (labeled 5), inferior vena cava (IVC, labeled 6), right adrenal gland (RAG, labeled 7), left adrenal gland (LAG, labeled 8), gallbladder (labeled 9), esophagus (labeled 10), stomach (labeled 11), duodenum (labeled 12), left kidney (labeled 13). |
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In PET scans, there are **4 organs** for segmentation, including the liver (labeled 1), right kidney (labeled 2), spleen (labeled 3), left kidney (labeled 4). |
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## Data Structure |
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**train_CT_gt_label：** |
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50 CT scans with ground-truth labels. |
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**train_CT_pseudolabel：** |
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2000 CT scans with pseudo labels generated by the FLARE 2022 winning teams, i.e., aladdin5 and blackbean. |
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**train_MRI_unlabeled：** |
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4817 unlabeled MRI scans |
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**train_PET_unlabeled：** |
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1000 unlabeled PET scans |
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**coreset_train_unlabeled_MRI_PET：** |
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100 unlabeled MRI and 100 unlabeled PET scans sampled from the original unlabeled MRI and PET training set. |
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**validation：** |
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160 MRI scans and 50 PET scans |
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FLARE-Task3-DomainAdaption/ |
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├── coreset_train_unlabeled_MRI_PET/ |
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│ ├── MRI_unlabeled_100_random/ |
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│ └── PET_unlabeled_100_random/ |
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├── train_CT_gt_label/ |
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│ ├── imagesTr/ |
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│ ├── labelsTr.7z |
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│ └── dataset.json |
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├── train_CT_pseudolabel/ |
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│ ├── imagesTr/ |
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│ ├── pseudo_label_aladdin5_flare22.7z |
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│ └── pseudo_label_blackbean_flare22.zip |
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├── train_MRI_unlabeled/ |
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│ ├── AMOS-833/ |
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│ └── LLD-MMRI-3984/ |
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├── train_PET_unlabeled/ |
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├── validation/ |
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│ ├── MRI_imagesVal/ |
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│ ├── MRI_labelsVal/ |
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│ ├── PET_imagesVal/ |
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│ ├── PET_labelsVal/ |
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│ └── readme.txt |
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└── README.md |
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## Data Download |
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```shell |
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pip install -U huggingface_hub |
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huggingface-cli download FLARE-MedFM/FLARE-Task3-DomainAdaption --repo-type dataset --local-dir ./FLARE-MedFM/FLARE-Task3-DomainAdaption --local-dir-use-symlinks False |
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``` |
