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"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", 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"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": "ASAFind", "description": "ASAFind is a software that predicts the intracellular location of proteins in cells with four membrane-bound complex plastids of red algal origin. \nThese plastids evolved via eukaryote-eukaryote endosymbiosis and for example can be found in diatoms and cryptophytes. ASAFind uses protein sequence data and external predictions of signal peptides (by the tools SignalP and TargetP), and predicts plastid proteins, and proteins that are targeted to the periplastidic compartment from these data; optionally, a graphical output can generated.", "homepage": "https://asafind.jcu.cz/", "biotoolsID": "asafind", "biotoolsCURIE": "biotools:asafind", "version": [ "2.0" ], "otherID": [], "relation": [ { "biotoolsID": "signalp", "type": "uses" }, { "biotoolsID": "targetp", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2489", "term": "Subcellular localisation prediction" }, { "uri": "http://edamontology.org/operation_0239", "term": "Sequence motif recognition" }, { "uri": "http://edamontology.org/operation_0422", "term": "Protein cleavage site prediction" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2886", "term": "Protein sequence record" }, "format": [ { "uri": "http://edamontology.org/format_1929", "term": "FASTA" } ] }, { "data": { "uri": "http://edamontology.org/data_1270", "term": "Feature table" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1277", "term": "Protein features" }, "format": [] }, { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [] } ], "note": null, "cmd": null } ], "toolType": [ "Script", "Command-line tool", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_2229", "term": "Cell biology" }, { "uri": "http://edamontology.org/topic_0078", "term": "Proteins" }, { "uri": "http://edamontology.org/topic_0780", "term": "Plant biology" }, { "uri": "http://edamontology.org/topic_0622", "term": "Genomics" } ], "operatingSystem": [ "Mac", "Linux" ], "language": [ "Python" ], "license": "CC-BY-SA-4.0", "collectionID": [ "ELIXIR-CZ", "Czech Republic" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [ "Tools" ], "elixirNode": [ "Czech Republic" ], "elixirCommunity": [ "Plant Sciences", "Marine Metagenomics", "Microbial Biotechnology" ], "link": [ { "url": "https://asafind.jcu.cz", "type": [ "Service" ], "note": "Web Service" }, { "url": "https://github.com/ASAFind/ASAFind-2", "type": [ "Repository" ], "note": "Information and source code, for local installation or development" } ], "download": [ { "url": "https://asafind.jcu.cz/download-page/", "type": "Downloads page", "note": "Download page on web-service, links to repository", "version": "2.0" } ], "documentation": [ { "url": "https://asafind.jcu.cz/download-page/", "type": [ "Installation instructions" ], "note": null } ], "publication": [ { "doi": "10.1111/tpj.70138", "pmid": "40464854", "pmcid": "PMC12136025", "type": [ "Primary" ], "version": "2.0", "note": "Publication of the current version of ASAFind (2.0)", "metadata": { "title": "ASAFind 2.0: multi-class protein targeting prediction for diatoms and algae with complex plastids", "abstract": "", "date": "2025-06-01T00:00:00Z", "citationCount": 0, "authors": [], "journal": "Plant Journal" } }, { "doi": "10.1111/tpj.12734", "pmid": "25438865", "pmcid": "PMC4329603", "type": [ "Other" ], "version": "1.0", "note": "Publication of the first version of ASAFind", "metadata": { "title": "Plastid proteome prediction for diatoms and other algae with secondary plastids of the red lineage", "abstract": "", "date": "2015-02-01T00:00:00Z", "citationCount": 138, "authors": [], "journal": "Plant Journal" } }, { "doi": "10.48550/arXiv.2303.02509", "pmid": null, "pmcid": null, "type": [ "Benchmarking study" ], "version": "1.0", "note": "Benchmarking of the performance of the first version of ASAFind", "metadata": null } ], "credit": [ { "name": "Marta Vohnoutová", "email": "mvohnoutova@jcu.cz", "url": "https://www.jcu.cz/cz/univerzita/lide/clovek?identita=Vohnoutova_Marta_61699", "orcidid": "https://orcid.org/0000-0002-8915-8626", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor", "Developer", "Maintainer" ], "note": null }, { "name": "Ansgar Gruber", "email": "agruber@prf.jcu.cz", "url": "https://www.jcu.cz/cz/univerzita/lide/clovek?identita=Gruber_Ansgar_118401", "orcidid": "https://orcid.org/0000-0002-5876-4391", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Documentor" ], "note": null } ], "owner": "martavohnoutova", "additionDate": "2025-12-15T14:17:14.627791Z", "lastUpdate": "2025-12-15T17:36:43.189214Z", "editPermission": { "type": "group", "authors": [ "agruber" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "thaf", "description": "thaf is a command-line tool to extract transcript sequences from a genome FASTA file based on GFF3 annotation files. It can also generate transcript-to-gene mapping files compatible with tools such as Salmon.", "homepage": "https://crates.io/crates/thaf", "biotoolsID": "thaf", "biotoolsCURIE": "biotools:thaf", "version": [ "0.0.4" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3267", "term": "Sequence coordinate conversion" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1234", "term": "Sequence set (nucleic acid)" }, "format": [] } ], "note": "Produce transcriptome file from genomic DNA and gff3", "cmd": "thaf \\\n -f <INPUT_GFF3> \\\n -d <DNA_FASTA> \\\n -t <OUTPUT_FASTA> \\\n [-g <GENEMAP_FILE>]\n [-e <FEATURES>]" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3308", "term": "Transcriptomics" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" }, { "uri": "http://edamontology.org/topic_3170", "term": "RNA-Seq" } ], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "Rust" ], "license": "BSD-3-Clause", "collectionID": [], "maturity": "Emerging", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://crates.io/crates/thaf", "type": [ "Software catalogue" ], "note": "Crates.io page providing installation instructions and README." }, { "url": "https://github.com/bourumir-wyngs/thaf", "type": [ "Repository" ], "note": "Source code. Also, use GitHub issue tracker to send bug reports and extension proposals." } ], "download": [ { "url": "https://crates.io/crates/thaf", "type": "Other", "note": "Use Cargo commands to install the application. Cargo will build it from the source.", "version": "0.0.4" } ], "documentation": [ { "url": "https://crates.io/crates/thaf", "type": [ "General" ], "note": "README provides enough information to use the tool" } ], "publication": [], "credit": [ { "name": "Audrius Meskauskas", "email": "contact@verdanta.tech", "url": "https://verdanta.info", "orcidid": "https://orcid.org/0009-0006-1714-3691", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": "Verdanta is an early startup in Switzerland, providing curated RNASeq pipeline. Thaf is part of our software stack that is released open source." } ], "owner": "Bourumir", "additionDate": "2025-12-15T17:27:55.661547Z", "lastUpdate": "2025-12-15T17:31:34.066168Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "EAPP - Extended a Priori Probability tool", "description": "The tool provides a semi-supervised metric (EAPP) for binary classification tasks that considers not only the a priori probability but also some possible bias present in the dataset, as well as other features that could provide a relatively trivial separability of the target classes. 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After the mammogram segmentation, the tool returns a DICOM-SEG image with both the dense tissue and the breast tissue mask combined.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/image_batch_iti-breast", "biotoolsID": "breast_dense_tissue_segmentation", "biotoolsCURIE": "biotools:breast_dense_tissue_segmentation", "version": [ "v1.0.6" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "input_dcm_path: input path to the 2D DICOM image (FFDM); output_directory: output directory to store the 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"gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": "Researcher at University of Bari" }, { "name": "Angelo Sante Varvara", "email": "angelovarvara94@gmail.com", "url": "https://www.linkedin.com/in/angelo-sante-varvara-b514661a6/", "orcidid": "https://orcid.org/0009-0005-3559-8430", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": "PhD Student at SEMM Institute" } ], "owner": "asvarvara", "additionDate": "2025-12-15T11:29:08.759624Z", "lastUpdate": "2025-12-15T11:29:47.221679Z", "editPermission": { "type": "group", "authors": [ "sharonnatasha.cox" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "seqfu", "description": "A collection of utilities to manipulate FASTA and FASTQ files, supporting Gzipped input.", "homepage": "https://telatin.github.io/seqfu2/", "biotoolsID": "seqfu", "biotoolsCURIE": 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"elixirCommunity": [], "link": [ { "url": "https://telatin.github.io/seqfu2", "type": [ "Other" ], "note": "Documentation" }, { "url": "https://github.com/telatin/seqfu2/", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://github.com/telatin/seqfu2", "type": "Software package", "note": null, "version": null }, { "url": "https://github.com/telatin/seqfu2/releases", "type": "Downloads page", "note": null, "version": null }, { "url": "https://bioconda.github.io/recipes/seqfu/README.html", "type": "Software package", "note": null, "version": "1.0.0-1.22.3" } ], "documentation": [ { "url": "https://telatin.github.io/seqfu2/", "type": [ "User manual" ], "note": null } ], "publication": [ { "doi": "10.3390/bioengineering8050059", "pmid": "34066939", "pmcid": "PMC8148589", "type": [ "Primary" ], "version": "1.0", "note": null, "metadata": { "title": "Seqfu: A suite of utilities for the robust and reproducible manipulation of sequence files", "abstract": "", "date": "2021-01-01T00:00:00Z", "citationCount": 36, "authors": [], "journal": "Bioengineering" } } ], "credit": [], "owner": "telatin", "additionDate": "2020-03-17T13:50:50Z", "lastUpdate": "2025-12-12T21:43:34.328705Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DICOM tags extractor", "description": "Scans the DICOM files of a defined directory at the series level, and produces as output either 1/one single csv file containing all preselected DICOM tags for each detected series; 2/ one single csv file only containing the patientID and the corresponding StudyUIDs.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_tag_selection/info-tab", "biotoolsID": "dicom_tags_extractor", "biotoolsCURIE": "biotools:dicom_tags_extractor", "version": [ "1.1" ], "otherID": [], "relation": [ { "biotoolsID": "eetl_toolset", "type": "usedBy" }, { "biotoolsID": 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--plugin eucaim_catalogue --out faireva_out" } ], "toolType": [ "Plug-in", "Command-line tool", "Web service" ], "topic": [ { "uri": "http://edamontology.org/topic_4012", "term": "FAIR data" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "Apache-2.0", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/IFCA-Advanced-Computing/FAIR_eva", "type": [ "Repository" ], "note": "Repository for FAIR EVA, without the EUCAIM specific plugin" } ], "download": [], "documentation": [ { "url": "https://github.com/IFCA-Advanced-Computing/FAIR_eva/blob/main/docs/index.md", "type": [ "General" ], "note": "Documentation for the main package, EUCAIM plugin specific documentation not included." }, { "url": "https://gitlabpages.ifca.es/fair-eva-guide-608baa/", "type": [ "User manual" ], "note": "Wiki page with FAIR EVA for EUCAIM specific instructions." } ], "publication": [ { "doi": "10.1038/s41597-023-02652-8", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": null, "abstract": null, "date": null, "citationCount": 0, "authors": [], "journal": null } } ], "credit": [ { "name": "Fernando Aguilar", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0001-9462-4831", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Maintainer", "Documentor" ], "note": "Creator and devloper of the main package FAIR EVA.\nThis software started to be developed within IFCA-Advanced-Computing receives funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857647." }, { "name": "David Rodríguez", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0002-9160-5106", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer", "Primary contact" ], "note": "Developer of the EUCAIM plugin." }, { "name": "Inés Victoria", "email": null, "url": null, "orcidid": "https://orcid.org/0009-0003-7950-3937", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": "Developer of the EUCAIM plugin." } ], "owner": "davrodgon", "additionDate": "2025-06-30T08:52:48.500476Z", "lastUpdate": "2025-12-04T10:57:20.901575Z", "editPermission": { "type": "group", "authors": [ "ines.ifca", "davrodgon" ] }, "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. The database is available at loschmidt.chemi.muni.cz/soluprotmutdb.", "date": "2022-01-01T00:00:00Z", "citationCount": 0, "authors": [ { "name": "Velecky J." }, { "name": "Hamsikova M." }, { "name": "Stourac J." }, { "name": "Musil M." }, { "name": "Damborsky J." }, { "name": "Bednar D." }, { "name": "Mazurenko S." } ], "journal": "Computational and Structural Biotechnology Journal" } } ], "credit": [ { "name": "David Bednar", "email": "222755@mail.muni.cz", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Stanislav Mazurenko", "email": "mazurenko@mail.muni.cz", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Jan Velecký", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null }, { "name": "Marie Hamsikova", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "velda", "additionDate": "2023-01-25T22:49:31.072923Z", "lastUpdate": "2025-12-03T12:49:50.125351Z", "editPermission": { "type": "group", "authors": [ "velda", "m.musil" ] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "AutoProSeq - automatic classification of sequence types for prostate mpMRI", "description": "XGBoost-based automatic classification of sequence types for prostate mpMRI into T2W, DWI, ADC, DCE and others (any other sequence type) using metadata alone. Works as a CLI, allows direct application to DICOM datasets or CSV/tsv/Excel/parquet datasets, features integration for Orthanc and DICOM-web as RESTful APIs. This was applied to all data on ProCAncer-I to derive initial results for sequence types. Refines predictions using rule-based heuristics for T2W, DWI and ADC.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/champ-autoproseq", "biotoolsID": "auto-prostate-sequence-classifier", "biotoolsCURIE": "biotools:auto-prostate-sequence-classifier", "version": [ "v0.1" ], "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_0968", "term": "Keyword" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "note": "The input can be provided as a DICOM dataset (which will require --dicom-recursion, details below or in the documentation) or as a single DICOM study. This command will print a CSV with 5 columns: i) patient_id, ii) study_uid, iii) series_uid, iv) classification from the XGBoost model, v) classification after heuristics-based corrections.\nThe sequence classification is performed between T2W, DWI, ADC, DCE or others. The provided <dicom-recursion> refers to the number of folders until a DICOM series is found. So if the provided input is a patient folder with nested studies with nested series, the level of DICOM recursion should be 2. If the provided input is a directory with patient folders, the level of DICOM recursion should be 3.", "cmd": "docker run -v <dir-with-dicom>:/data harbor.eucaim.cancerimage.eu/processing-tools/champ-autoproseq --dicom_recursion <dicom-recursion> --input_paths /data" } ], "toolType": [ "Web API", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "MIT", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/CCIG-Champalimaud/metadata-classification", "type": [ "Repository" ], "note": "Repository containing code, README and additional usage information for this tool." } ], "download": [], "documentation": [ { "url": "https://github.com/CCIG-Champalimaud/metadata-classification", "type": [ "Other" ], "note": "Repository containing the code, README and additional information on how to run the tool. If used as an API (when integrated with Orthanc or DICOM-web), the documentation is available upon launching the API at <api-url>/docs." } ], "publication": [ { "doi": "10.1186/s13244-025-01938-2", "pmid": "40146375", "pmcid": "PMC12187622", "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Automatic sequence identification in multicentric prostate multiparametric MRI datasets for clinical machine-learning", "abstract": "", "date": "2025-12-01T00:00:00Z", "citationCount": 1, "authors": [], "journal": "Insights into Imaging" } } ], "credit": [ { "name": "José Guilherme de Almeida", "email": "jose.almeida@research.fchampalimaud.org", "url": "https://josegcpa.net", "orcidid": "https://orcid.org/0000-0002-1887-0157", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Maintainer" ], "note": null }, { "name": "Nikolaos Papanikolaou", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3298-2072", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Contributor" ], "note": null } ], "owner": "josegcpa", "additionDate": "2025-12-02T17:37:57.215347Z", "lastUpdate": "2025-12-03T09:27:04.489071Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "IQA DCE tool", "description": "The purpose of the tool is to provide a complete framework for automatic detection of image quality for Breast DCE MR images. The assessment is performed on the first post-contrast dynamic phase in order to assess the most clinically relevant sequence among a number of identical acquisitions. Two categories are available for image classification, i.e. high and low quality, depending on the level of noise, degree of blurring and presence of artifacts.", "homepage": "https://hub.docker.com/repository/docker/billykforth/iqa_dce_tool/general", "biotoolsID": "iqa_dce_tool", "biotoolsCURIE": "biotools:iqa_dce_tool", "version": [ "1.0" ], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3444", "term": "MRI" }, { "uri": "http://edamontology.org/topic_3572", "term": "Data quality management" }, { "uri": "http://edamontology.org/topic_2640", "term": "Oncology" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Python" ], "license": "EUPL-1.1", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [ { "url": "https://hub.docker.com/r/billykforth/iqa_dce_tool", "type": "Container file", "note": null, "version": "1.0" } ], "documentation": [ { "url": "https://docs.google.com/document/d/1hG_M21gvO4D8zkHmwtbavT8Dp6bliRU4/edit", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.3390/jimaging11110417", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [], "owner": "billyk", "additionDate": "2025-11-26T13:27:32.776575Z", "lastUpdate": "2025-12-03T06:46:13.717361Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "DICOM defacing anonymisation", "description": "This tool can be used to remove facial features (\"defacing\") from DICOM images. It may be used 1) only for facial removing, or 2) for a complete import and anonymization using DICOM transfer. Defaced images needs to be manually quality checked, and this is done through a web interface to a local server supplied with the Docker containers.", "homepage": "https://www.dicom2usb.com", "biotoolsID": "dicom_defacing_anonymation", "biotoolsCURIE": "biotools:dicom_defacing_anonymation", "version": [], "otherID": [], "relation": [], "function": [], "toolType": [ "Command-line tool", "Desktop application", "Workflow" ], "topic": [ { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" }, { "uri": "http://edamontology.org/topic_4044", "term": "Data protection" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [], "license": "Proprietary", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Restricted access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [], "download": [], "documentation": [], "publication": [], "credit": [], "owner": "janaxelsson", "additionDate": "2025-12-01T09:36:32.395959Z", "lastUpdate": "2025-12-01T16:25:11.338723Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "APE", "description": "APE (the Automated Pipeline Explorer) as a command-line tool and API for automated composition of scientific workflows. APE is easily configured to a new application domain by providing it with a domain ontology and semantically annotated tools. It can then be used to synthesize purpose-specific workflows based on a specification of the available workflow inputs, desired outputs and possibly additional constraints.", "homepage": "https://github.com/sanctuuary/APE/", "biotoolsID": "ape", "biotoolsCURIE": "biotools:ape", "version": [ "2.1.7" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3429", "term": "Generation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_0949", "term": "Workflow metadata" }, "format": [ { "uri": "http://edamontology.org/format_3464", "term": "JSON" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_0949", "term": "Workflow metadata" }, "format": [ { "uri": "http://edamontology.org/format_3604", "term": "SVG" }, { "uri": "http://edamontology.org/format_3857", "term": "CWL" }, { "uri": "http://edamontology.org/format_3603", "term": "PNG" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Library", "Web API", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_0769", "term": "Workflows" }, { "uri": "http://edamontology.org/topic_0089", "term": "Ontology and terminology" }, { "uri": "http://edamontology.org/topic_0091", "term": "Bioinformatics" } ], "operatingSystem": [ "Mac", "Windows", "Linux" ], "language": [ "Java" ], "license": "Apache-2.0", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/sanctuuary/ape", "type": [ "Repository" ], "note": null } ], "download": [ { "url": "https://mvnrepository.com/artifact/io.github.sanctuuary/APE/2.1.7", "type": "Software package", "note": "Download APE java library or the CLI", "version": "2.1.7" } ], "documentation": [ { "url": "https://ape-framework.readthedocs.io/", "type": [ "API documentation" ], "note": null } ], "publication": [ { "doi": null, "pmid": null, "pmcid": "PMC7304703", "type": [ "Primary" ], "version": null, "note": null, "metadata": null }, { "doi": "10.1021/acs.jproteome.0c00983", "pmid": "33720735", "pmcid": "PMC8041394", "type": [ "Benchmarking study" ], "version": null, "note": null, "metadata": { "title": "APE in the Wild: Automated Exploration of Proteomics Workflows in the bio.tools Registry", "abstract": "", "date": "2021-04-02T00:00:00Z", "citationCount": 7, "authors": [], "journal": "Journal of Proteome Research" } } ], "credit": [ { "name": "Vedran Kasalica", "email": "vedran.kasalica@health-ri.nl", "url": null, "orcidid": "https://orcid.org/0000-0002-0097-1056", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null }, { "name": "Anna-Lena Lamprecht", "email": "anna-lena.lamprecht@uni-potsdam.de", "url": null, "orcidid": "https://orcid.org/0000-0003-1953-5606", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [], "note": null } ], "owner": "VKasalica", "additionDate": "2021-01-18T10:10:48Z", "lastUpdate": "2025-12-01T13:47:56.062392Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "HBAT", "description": "HBAT (Hydrogen Bond Analysis Tool) is a package to automate the analysis of potential hydrogen bonds and similar type of weak interactions in macromolecular structures, available in Protein Data Bank (PDB) file format. HBAT uses a geometric approach to identify molecular interactions by analysing distance and angular criteria.\n\nHBAT 1.1 was released in 2007 as closed source Windows binary and continue to be available via SourceForge. \n\nHBAT 2.0 was introduced in 2025 and a completed rewrite of original implementation with cross-platform support. 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Typically, multiple substitution attempts are run on each input molecule, and all distinct results are gathered.\nKnown limitation is methoximation on cycles which should be broken. 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Built in Python, it enables precise quantitative analysis of brain gliomas in MRI scans to support clinical decision-making in both diagnosis and prognosis.", "homepage": "https://github.com/BIT-UPM/EUCAIM/brain_glioma_segmenter", "biotoolsID": "brain_glioma_segmenter", "biotoolsCURIE": "biotools:brain_glioma_segmenter", "version": [ "v1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Workflow", "Web application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3444", "term": "MRI" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "Python" ], "license": "CC-BY-NC-ND-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://segmenter.hope4kids.io/", "type": [ "Other" ], "note": "Main app webpage" }, { "url": "https://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-Kids", "type": [ "Repository" ], "note": "GitHub repository" } ], "download": [], "documentation": [ { "url": "https://docs.hope4kids.io/HOPE-Segmenter-Kids/", "type": [ "API documentation", "Citation instructions", "Terms of use", "Code of conduct", "Release notes", "User manual", "General", "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1007/978-3-031-76163-8_20", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging", "abstract": "", "date": "2024-01-01T00:00:00Z", "citationCount": 2, "authors": [], "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)" } }, { "doi": "10.1109/ISBI56570.2024.10635469", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Enhancing Generalizability in Brain Tumor Segmentation: Model Ensemble with Adaptive Post-Processing", "abstract": "", "date": "2024-01-01T00:00:00Z", "citationCount": 1, "authors": [], "journal": "Proceedings - International Symposium on Biomedical Imaging" } }, { "doi": "10.48550/arXiv.2412.04094", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null }, { "doi": "10.48550/arXiv.2412.04111", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Daniel Capellán-Martín", "email": "daniel.capellan@upm.es", "url": null, "orcidid": "https://orcid.org/0000-0002-9743-0845", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact", "Contributor", "Developer", "Support" ], "note": null }, { "name": "Abhijeet Parida", "email": "pabhijeet@childrensnational.org", "url": null, "orcidid": "https://orcid.org/0000-0002-4978-0576", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Contributor", "Developer", "Support", "Maintainer" ], "note": null }, { "name": "Zhifan Jiang", "email": "zjiang@childrensnational.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Contributor", "Developer", "Support" ], "note": null }, { "name": "María Jesus Ledesma-Carbayo", "email": "mj.ledesma@upm.es", "url": null, "orcidid": "https://orcid.org/0000-0001-6846-3923", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact", "Contributor" ], "note": null }, { "name": "Marius George Linguraru", "email": "mlingura@childrensnational.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact", "Contributor" ], "note": null } ], "owner": "dani-capellan", "additionDate": "2025-11-05T10:49:02.917891Z", "lastUpdate": "2025-11-28T13:56:29.777759Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Pediatric Brain Tumor Segmenter", "description": "The Pediatric Brain Tumor Segmenter is a software tool developed by researchers from Universidad Politécnica de Madrid and Children’s National Hospital for the segmentation and analysis of pediatric brain tumors in magnetic resonance imaging (MRI). Built in Python, it enables precise quantitative analysis of pediatric brain tumors in MRI scans to support clinical decision-making in both diagnosis and prognosis.", "homepage": "https://github.com/BIT-UPM/EUCAIM/pediatric_brain_tumor_segmenter", "biotoolsID": "pediatric_brain_tumor_segmenter", "biotoolsCURIE": "biotools:pediatric_brain_tumor_segmenter", "version": [ "v1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [], "output": [], "note": null, "cmd": null } ], "toolType": [ "Web application", "Workflow", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3444", "term": "MRI" }, { "uri": "http://edamontology.org/topic_3474", "term": "Machine learning" } ], "operatingSystem": [ "Windows", "Mac", "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-SA-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://segmenter.hope4kids.io/", "type": [ "Other" ], "note": "Main app webpage" }, { "url": "https://github.com/Pediatric-Accelerated-Intelligence-Lab/HOPE-Segmenter-Kids", "type": [ "Repository" ], "note": "GitHub repository" } ], "download": [], "documentation": [ { "url": "https://docs.hope4kids.io/HOPE-Segmenter-Kids/", "type": [ "API documentation", "Citation instructions", "Terms of use", "Code of conduct", "Release notes", "User manual", "General", "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.1007/978-3-031-76163-8_20", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Model Ensemble for Brain Tumor Segmentation in Magnetic Resonance Imaging", "abstract": "", "date": "2024-01-01T00:00:00Z", "citationCount": 2, "authors": [], "journal": "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)" } }, { "doi": "10.1109/ISBI56570.2024.10635469", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": { "title": "Enhancing Generalizability in Brain Tumor Segmentation: Model Ensemble with Adaptive Post-Processing", "abstract": "", "date": "2024-01-01T00:00:00Z", "citationCount": 1, "authors": [], "journal": "Proceedings - International Symposium on Biomedical Imaging" } }, { "doi": "10.48550/arXiv.2412.04094", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null }, { "doi": "10.48550/arXiv.2412.04111", "pmid": null, "pmcid": null, "type": [], "version": null, "note": null, "metadata": null } ], "credit": [ { "name": "Daniel Capellán-Martín", "email": "daniel.capellan@upm.es", "url": null, "orcidid": "https://orcid.org/0000-0002-9743-0845", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact", "Developer", "Contributor", "Support" ], "note": null }, { "name": "Abhijeet Parida", "email": "pabhijeet@childrensnational.org", "url": null, "orcidid": "https://orcid.org/0000-0002-4978-0576", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Developer", "Contributor", "Support", "Maintainer" ], "note": null }, { "name": "Zhifan Jiang", "email": "zjiang@childrensnational.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Contributor", "Developer", "Support" ], "note": null }, { "name": "María Jesus Ledesma-Carbayo", "email": "mj.ledesma@upm.es", "url": null, "orcidid": "https://orcid.org/0000-0001-6846-3923", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact", "Contributor" ], "note": null }, { "name": "Marius George Linguraru", "email": "mlingura@childrensnational.org", "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": null, "typeRole": [ "Primary contact", "Contributor" ], "note": null } ], "owner": "dani-capellan", "additionDate": "2025-10-28T18:31:05.926498Z", "lastUpdate": "2025-11-28T13:55:19.273705Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Prostate zone segmentation tool", "description": "This tool automatically segments the the prostate into two zones: the central and transition zones (TZ+CZ) and the peripheral zone (PZ). It takes as input a T2-weighted image and produces a segmentation in the same input format.\n\nThree datasets were used to train this model: ProstateX (n=152), Prostate158 (n=119) and ProstateNet (the ProCAncer-I dataset; n=532). We used the T2-weighted images available in each dataset and trained a standard three-class nnU-Net model with three classes: background, peripheral zone and the combination of the transition and central zones as annotated by radiologists. Using 149 cases from the three previously noted datasets, this model achieved DSC=0.81 (95% CI=[0.59, 0.92]) for PZ and 0.87 (95% CI=[0.60, 0.96]) for CZ+TZ. In a clinical validation using ProCAncer-I data, this model achieved, for PZ and TZ+CZ, DSC=0.65 and DSC=0.77, respectively, comparable to the observed between-radiologist DSC for a subset of cases (DSC=0.61 and DSC=0.73, respectively).", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/champ-prostate-zone-segmentation", "biotoolsID": "prostate_zone_segmentation_tool", "biotoolsCURIE": "biotools:prostate_zone_segmentation_tool", "version": [], "otherID": [], "relation": [ { "biotoolsID": "nnunet", "type": "uses" } ], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Image annotation for individual DICOM and Nifti images: Please note that it is easier to mount the input and output paths path and keep `--study_path` always as `/data/input` and `--output_dir` always as `/data/output`. This way only `--series_folders` requires any change. `--series_folders` can refer both to a Nifti file (if the input is Nifti) or the a DICOM series folder.\n\nIf the input is DICOM, `--is_dicom` should be used.", "cmd": "docker run --shm-size=1gb -v $(pwd)/example:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus=\"all\" test-eucaim:latest nnunet-predict --study_path /data/input --series_folders <series-folder> --output_dir /data/output" }, { "operation": [ { "uri": "http://edamontology.org/operation_3553", "term": "Image annotation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_3442", "term": "MRI image" }, "format": [ { "uri": "http://edamontology.org/format_3549", "term": "nii" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" } ] } ], "note": "Batch image annotation for DICOM and Nifti images using dataset JSON and data directory supports two methods: using a dataset JSON and using a data directory. \n\nThe dataset JSON-based method relies on having a dataset JSON which has a list of dictionaries that follow this structure: `{\"study_path\": str, \"series_folders\": [[str]], \"output_dir\": str}`. These are analogous to the previous modes. Be mindful that paths should be specified accordingly for the Docker file-system. The dataset JSON should be specified using `--data_json`.\n\nThe data directory-based method relies on having a directory structured as patient/study/series, where each series follows an nnU-Net-like convention (i.e. `<series_id>_0000`, `<series_id>_0001`, etc.). In this case, it should be `<series-id>_0000`. The data directory should be specified using `--data_dir` and requires the specification of an `--output_dir`.\n\nIf the input is DICOM, `--is_dicom` should be specified.", "cmd": "docker run --shm-size=1gb -v $(pwd)/example_dataset:/data/input:ro -v $(pwd)/test_output:/data/output:rw --gpus=\"all\" test-eucaim:latest nnunet-predict-batch {{--data_json /data/input/<dataset_json>.json|--data_dir /data/input --output_dir /data/output}}" } ], "toolType": [ "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3063", "term": "Medical informatics" } ], "operatingSystem": [ "Linux" ], "language": [ "Python" ], "license": "CC-BY-NC-4.0", "collectionID": [ "EUCAIM" ], "maturity": "Emerging", "cost": "Free of charge (with restrictions)", "accessibility": "Open access (with restrictions)", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "Repository" ], "note": "This is the nnunet_serve repository, containing the generic nnU-Net building and serving functionalities used by the Computational Clinical Imaging Group at Champalimaud Foundation. 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