{"url":"/dataset/promise12","name":"PROMISE12","full_name":"PROMISE12","description_markdown":"The **PROMISE12** dataset was made available for the MICCAI 2012 prostate segmentation challenge. Magnetic Resonance (MR) images (T2-weighted) of 50 patients with various diseases were acquired at different locations with several MRI vendors and scanning protocols.\r\n\r\nSource: [Constrained Deep Networks: Lagrangian Optimization via Log-Barrier Extensions](https://arxiv.org/abs/1904.04205)\r\nImage Source: [https://promise12.grand-challenge.org/](https://promise12.grand-challenge.org/)","description_withheld":null,"homepage":"https://promise12.grand-challenge.org/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Evaluation of prostate segmentation algorithms for MRI: The PROMISE12 challenge","first_author":null,"url":"https://doi.org/10.1016/j.media.2013.12.002"},"license":{"name":"Custom","url":"https://promise12.grand-challenge.org/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"MRI","url":"/datasets/modality/mri"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Data Augmentation","url":"/task/data-augmentation","datasets_with_task":"/datasets/task/data-augmentation"},{"name":"Volumetric Medical Image Segmentation","url":"/task/volumetric-medical-image-segmentation","datasets_with_task":"/datasets/task/volumetric-medical-image-segmentation"}],"languages":[],"variants":["PROMISE 2012","PROMISE12"],"data_loaders":[],"num_papers_in_archive":84,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/volumetric-medical-image-segmentation-on","task":"Volumetric Medical Image Segmentation","dataset_variant":"PROMISE 2012","rows":2,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"V-Net + Dice-based loss","paper":"/paper/v-net-fully-convolutional-neural-networks-for","metrics":{"Dice Score":"0.869"},"code_links":[{"title":"black0017/MedicalZooPytorch","url":"https://github.com/black0017/MedicalZooPytorch"},{"title":"mattmacy/vnet.pytorch","url":"https://github.com/mattmacy/vnet.pytorch"},{"title":"yingkaisha/keras-unet-collection","url":"https://github.com/yingkaisha/keras-unet-collection"},{"title":"mindspore-ai/models","url":"https://github.com/mindspore-ai/models/tree/master/research/cv/vnet"},{"title":"faustomilletari/VNet","url":"https://github.com/faustomilletari/VNet"},{"title":"shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks","url":"https://github.com/shalabh147/Brain-Tumor-Segmentation-and-Survival-Prediction-using-Deep-Neural-Networks"},{"title":"jackyko1991/vnet-tensorflow","url":"https://github.com/jackyko1991/vnet-tensorflow"},{"title":"MiguelMonteiro/VNet-Tensorflow","url":"https://github.com/MiguelMonteiro/VNet-Tensorflow"},{"title":"seungjunlee96/U-Net_Lung-Segmentation","url":"https://github.com/seungjunlee96/U-Net_Lung-Segmentation"},{"title":"mtancak/PyTorch-UNet-Brain-Cancer-Segmentation","url":"https://github.com/mtancak/PyTorch-UNet-Brain-Cancer-Segmentation"},{"title":"bo-10000/VoxResNet","url":"https://github.com/bo-10000/VoxResNet"},{"title":"mtancak1/PyTorch-UNet-Brain-Cancer-Segmentation","url":"https://github.com/mtancak1/PyTorch-UNet-Brain-Cancer-Segmentation"},{"title":"mtancak1/PyTorch-UNet-BraTS20","url":"https://github.com/mtancak1/PyTorch-UNet-BraTS20"},{"title":"alexbmp/run-vnet-keras","url":"https://github.com/alexbmp/run-vnet-keras"},{"title":"soymintc/run-vnet-keras","url":"https://github.com/soymintc/run-vnet-keras"},{"title":"Shrajan/AAAI-2022","url":"https://github.com/Shrajan/AAAI-2022"},{"title":"nnzzll/networks","url":"https://github.com/nnzzll/networks"},{"title":"BraveDrXuTF/vae-gan-code-for-reinforced-panel","url":"https://github.com/BraveDrXuTF/vae-gan-code-for-reinforced-panel"},{"title":"Mind23-2/MindCode-2","url":"https://github.com/Mind23-2/MindCode-2/tree/main/vnet"},{"title":"YellowLight021/Vnet","url":"https://github.com/YellowLight021/Vnet"},{"title":"2023-MindSpore-1/ms-code-7","url":"https://github.com/2023-MindSpore-1/ms-code-7/tree/main/vnet"},{"title":"salem-devloper/final","url":"https://github.com/salem-devloper/final"},{"title":"MindSpore-paper-code-2/code3","url":"https://github.com/MindSpore-paper-code-2/code3/tree/main/vnet"},{"title":"alililia/VNet_Ascend","url":"https://github.com/alililia/VNet_Ascend"},{"title":"alililia/vnet_GPU","url":"https://github.com/alililia/vnet_GPU"},{"title":"chakerouari/UNET_segmetation","url":"https://github.com/chakerouari/UNET_segmetation"},{"title":"salem-devloper/COVID-Lung-Segment","url":"https://github.com/salem-devloper/COVID-Lung-Segment"},{"title":"salem-devloper/lung-chaker","url":"https://github.com/salem-devloper/lung-chaker"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-promise12","task":"Medical Image Segmentation","dataset_variant":"PROMISE12","rows":1,"metrics":["F1"],"first_row_in_archive_order":{"model":"Hi-gMISnet","paper":"/paper/hi-gmisnet-generalized-medical-image","metrics":{"F1":"90.79"},"code_links":[{"title":"tushartalukder/HigMISnet","url":"https://github.com/tushartalukder/HigMISnet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hi-gmisnet-generalized-medical-image","title":"Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN","date":"2024-05-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/conditional-random-fields-as-recurrent-neural","title":"Conditional Random Fields as Recurrent Neural Networks for 3D Medical Imaging Segmentation","date":"2018-07-19","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/v-net-fully-convolutional-neural-networks-for","title":"V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation","date":"2016-06-15","rows_on_this_dataset":1,"code_links":28,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":22,"samples_ran":2,"samples_unverified":20,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":25,"samples_ran":4,"samples_unverified":21,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}