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It is designed to automatically adapt to a given dataset, analyzing the provided training cases to configure a matching U-Net-based segmentation pipeline without requiring expertise from the user.\nThe tool provides pretrained models for \nPancreas and Pancreas tumor segmentation, Colon cancer primaries segmentation, Abdominal organ segmentation, Liver and liver tumor segmentation, Kidney and kidney tumor segmentation, Brain Tumor segmentation and Hippocampus (MR data) segmentation", "homepage": "https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1", "biotoolsID": "nnunet", "biotoolsCURIE": "biotools:nnunet", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Run nnUnet pretrained on Task007_Pancreas targeting are pancras and pancreas tumor", "cmd": "docker run --rm -v /host/dataset/study_001/:/home/eucaim/nnUNet_input -v /host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-pancreas-tumour" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Run nnUnet model pretrained on Task029_LiTS targeting are liver and liver tumor", "cmd": "docker run --rm -v /host/dataset/study_001:/home/eucaim/nnUNet_input -v /host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-liver-tumour" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Run nnUnet pretrained on Task004_Hippocampus MRI data targeting posterior and anterior parts of the hippocampus", "cmd": "docker run --rm -v /host/dataset/study_001:/home/eucaim/nnUNet_input -v /host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-hippocampus-segmentation" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Run nnUnet pretrained on Task010_Colon targeting are colon cancer primaries.", "cmd": "docker run --rm -v /host/dataset/study_001:/home/eucaim/nnUNet_input -v /host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-colon-cancer" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Run nnUnet pretrained on Task169_BrainTumorPET targeting are brain tumor", "cmd": "docker run --rm -v /host/dataset/study_001:/home/eucaim/nnUNet_input -v /host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-brain-tumour-pet" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Run nnUNet model pretrained on Task017_AbdominalOrganSegmentation targeting thirteen different abdominal organs", "cmd": "docker run --rm -v /host/dataset/study_001:/home/eucaim/nnUNet_input -v /host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-abdominal-organ-segmentation" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3424", "term": "Raw image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Run nnUNet model pretrained on Task135_KiTS2021 targeting are kidney, tumor and cyst.", "cmd": "docker run --rm -v /host/dataset/study_001:/home/eucaim/nnUNet_input -v /host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-kidney-tumour" } ], "toolType": [ "Command-line tool" ], "topic": [], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "Apache-2.0", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [ { "url": "https://github.com/MIC-DKFZ/nnUNet/tree/nnunetv1", "type": [ "General" ], "note": "GitHub Readme" }, { "url": "https://github.com/MIC-DKFZ/nnunet-eucaim-docker/", "type": [ "User manual" ], "note": "Docker containers and other relevant instructions" } ], "publication": [ { "doi": "10.1038/s41592-020-01008-z", "pmid": null, "pmcid": null, "type": [ "Method" ], "version": null, "note": null, "metadata": { "title": "nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation", "abstract": "", "date": "2021-02-01T00:00:00Z", "citationCount": 6593, "authors": [], "journal": "Nature Methods" } } ], "credit": [ { "name": "DKFZ Heidelberg", "email": "r.floca@Dkfz-Heidelberg.de", "url": "https://www.dkfz.de/", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider", "Contributor", "Maintainer" ], "note": "Group Lead and Board Member, Medical Image Computing department, DKFZ Heidelberg" } ], "owner": "ASHISR", "additionDate": "2025-07-21T09:36:40.711284Z", "lastUpdate": "2025-12-16T12:04:54.619598Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "MR-based DIPG tumour detection and segmentation (EUCAIM-SW-020_T-01-02-003)", "description": "The tool performs an automatic segmentation of the possible DIPG tumours on MR images. 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"abstract": "", "date": "2024-07-01T00:00:00Z", "citationCount": 1, "authors": [], "journal": "Bioinformatics" } } ], "credit": [], "owner": "robomics", "additionDate": "2024-04-18T08:31:46.459964Z", "lastUpdate": "2025-12-03T14:44:30.069607Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "SoluProtMutsupDB", "description": "A manually curated database of protein solubility changes upon mutations.", "homepage": "http://loschmidt.chemi.muni.cz/soluprotmutdb", "biotoolsID": "soluprotmutsupdb", "biotoolsCURIE": "biotools:soluprotmutsupdb", "version": [], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_0409", "term": "Protein solubility prediction" }, { "uri": "http://edamontology.org/operation_3436", "term": "Aggregation" }, { "uri": "http://edamontology.org/operation_0331", "term": "Variant effect prediction" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0199", "term": "Genetic variation" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" }, { "uri": "http://edamontology.org/topic_0078", "term": "Proteins" }, { "uri": "http://edamontology.org/topic_0219", "term": "Data submission, annotation and curation" }, { "uri": "http://edamontology.org/topic_3068", "term": "Literature and language" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [], "license": null, "collectionID": [], "maturity": null, "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [ { "doi": "10.1016/J.CSBJ.2022.11.009", "pmid": "36420168", "pmcid": "PMC9678803", "type": [], "version": null, "note": null, "metadata": { "title": "SoluProtMutDB: A manually curated database of protein solubility changes upon mutations", "abstract": "© 2022 The Author(s)Protein solubility is an attractive engineering target primarily due to its relation to yields in protein production and manufacturing. Moreover, better knowledge of the mutational effects on protein solubility could connect several serious human diseases with protein aggregation. However, we have limited understanding of the protein structural determinants of solubility, and the available data have mostly been scattered in the literature. Here, we present SoluProtMutDB – the first database containing data on protein solubility changes upon mutations. Our database accommodates 33 000 measurements of 17 000 protein variants in 103 different proteins. The database can serve as an essential source of information for the researchers designing improved protein variants or those developing machine learning tools to predict the effects of mutations on solubility. The database comprises all the previously published solubility datasets and thousands of new data points from recent publications, including deep mutational scanning experiments. Moreover, it features many available experimental conditions known to affect protein solubility. The datasets have been manually curated with substantial corrections, improving suitability for machine learning applications. 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