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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": "AggreProt", "description": "AggreProt is a web server to predict and engineer protein aggregation that provides protein aggregation propensity profile. 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Recommends from 8 pipelines: STAR-RSEM-DESeq2 (high accuracy), HISAT2-StringTie-Ballgown (novel transcripts), Salmon-edgeR (balanced), Kallisto-Sleuth (ultra-fast), STAR-HTSeq-limma (complex designs), STAR-featureCounts-NOISeq (non-parametric), Bowtie2-RSEM-EBSeq (Bayesian), HISAT2-Cufflinks-Cuffdiff (legacy). Priority modes: accuracy, speed, memory, or balanced.", "cmd": "raptor run --pipeline 3 --data your_fastq/ --output results/" }, { "operation": [ { "uri": "http://edamontology.org/operation_2424", "term": "Comparison" }, { "uri": "http://edamontology.org/operation_0337", "term": "Visualisation" }, { "uri": "http://edamontology.org/operation_2428", "term": "Validation" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_3494", "term": "DNA sequence" }, "format": [ { "uri": "http://edamontology.org/format_2182", "term": "FASTQ-like format (text)" } ] }, { "data": { "uri": "http://edamontology.org/data_0925", "term": "Sequence assembly" }, "format": [ { "uri": "http://edamontology.org/format_2305", "term": "GFF" } ] }, { "data": { "uri": "http://edamontology.org/data_3112", "term": "Gene expression matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2048", "term": "Report" }, "format": [ { "uri": "http://edamontology.org/format_2331", "term": "HTML" } ] }, { "data": { "uri": "http://edamontology.org/data_1772", "term": "Score" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2884", "term": "Plot" }, "format": [ { "uri": "http://edamontology.org/format_3603", "term": "PNG" }, { "uri": "http://edamontology.org/format_3508", "term": "PDF" } ] }, { "data": { "uri": "http://edamontology.org/data_0928", "term": "Gene expression profile" }, "format": [ { "uri": "http://edamontology.org/format_3751", "term": "DSV" } ] }, { "data": { "uri": "http://edamontology.org/data_2082", "term": "Matrix" }, "format": [ { "uri": "http://edamontology.org/format_3752", "term": "CSV" }, { "uri": "http://edamontology.org/format_3603", "term": "PNG" } ] } ], "note": "Comprehensive benchmarking framework comparing 8 RNA-seq pipelines. Evaluates: (1) Accuracy metrics: sensitivity, specificity, precision, F1, ROC AUC, concordance; (2) Efficiency metrics: runtime, memory, CPU utilization, disk space; (3) Reproducibility metrics: between-run concordance, fold-change correlation, p-value correlation. Benchmark datasets: simulated data (Polyester), SEQC/MAQC validation, and user data. Runtime: 2-4 hours (small datasets) to 8-24 hours (large datasets). 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HBAT 2.0 is open sourced under MIT License.", "homepage": "https://hbat.abhishek-tiwari.com", "biotoolsID": "hbat", "biotoolsCURIE": "biotools:hbat", "version": [ "2.x", "1.1, 1.0" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_2406", "term": "Protein structure analysis" } ], "input": [ { "data": { "uri": "http://edamontology.org/data_1460", "term": "Protein structure" }, "format": [ { "uri": "http://edamontology.org/format_1476", "term": "PDB" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_1549", "term": "Protein hydrogen bonds" }, "format": [] } ], "note": null, "cmd": "hbat input.pdb" } ], "toolType": [ "Command-line tool", "Desktop application" ], "topic": [ { "uri": "http://edamontology.org/topic_2814", "term": "Protein structure analysis" }, { "uri": "http://edamontology.org/topic_0078", "term": "Proteins" } ], "operatingSystem": [ "Linux", "Windows", "Mac" ], "language": [ "Perl", "Python" ], "license": "MIT", "collectionID": [], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/abhishektiwari/hbat", "type": [ "Repository", "Issue tracker" ], "note": null }, { "url": "http://www.mybiosoftware.com/hbat-1-1-hydrogen-bond-analysis-tool.html", "type": [ "Software catalogue" ], "note": null } ], "download": [ { "url": "https://pypi.org/project/hbat/", "type": "Software package", "note": "Cross-platform PyPI Python Package", "version": "2.x" }, { "url": "https://github.com/abhishektiwari/hbat/releases", "type": "Binaries", "note": "Mac and Linux Binaries including AppImage, .deb, standalone executables.", "version": "2.x" }, { "url": "https://sourceforge.net/projects/hbat/", "type": "Binaries", "note": "Windows Binaries download from SourceForge", "version": "1.0, 1.1" } ], "documentation": [ { "url": "https://hbat.abhishek-tiwari.com", "type": [ "General", "API documentation", "Citation instructions", "Command-line options", "Installation instructions", "Quick start guide" ], "note": null } ], "publication": [ { "doi": "10.3233/ISI-2007-00337", "pmid": "18467777", "pmcid": null, "type": [ "Primary" ], "version": null, "note": "Peer-reviewed paper for HBAT 1.0", "metadata": null }, { "doi": "10.5281/zenodo.17645321", "pmid": null, "pmcid": null, "type": [ "Other" ], "version": null, "note": "Preprint for HBAT 2.x software", "metadata": null } ], "credit": [ { "name": "Abhishek Tiwari", "email": null, "url": "https://www.abhishek-tiwari.com", "orcidid": "https://orcid.org/0000-0003-2222-2395", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Primary contact" ], "note": null } ], "owner": "abhishektiwari", "additionDate": "2017-08-03T18:45:53Z", "lastUpdate": "2025-11-29T17:30:01.186940Z", "editPermission": { "type": "private", "authors": [] }, "validated": 1, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "gc-meox-tms", "description": "This package performs in-silico MeOX + TMS derivatization (as described e.g. in https://doi.org/10.1021/acs.analchem.7b01010):\n\nMethoximation: ketone R(C=O)R' and aldehyde (-HC=O) carbonyl groups are substituted with -C=NOCH3\nTrimethylsilylation: the acidic hydrogen in -OH, -SH, -COOH, -NH2, -NHR, =NH, the hydrogen is substituted with -Si(CH3)3 The substitution doesn't always have to happen; its probability is currently hardcoded in the package. 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/host/data_out:/home/eucaim/nnUNet_output --gpus all nnunet-abdominal-organ-segmentation" } ], "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", 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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. This should also be used as documentation." }, { "url": "https://docs.google.com/spreadsheets/d/1bcdhs9Xg2MxAH0WDGz08SjEVMFr3e-TP/edit?usp=sharing&ouid=111102743696297094705&rtpof=true&sd=true", "type": [ "Other" ], "note": "The EUCAIM model card for this model, containing details on model training, data and libraries." } ], "download": [], "documentation": [ { "url": "https://github.com/josegcpa/nnunet_serve", "type": [ "API documentation" ], "note": "This is the repository containing all of the code together with a comprehensive README for deployment and examplar files." } ], "publication": [ { "doi": "10.1016/j.compbiomed.2024.108216", "pmid": "38442555", "pmcid": null, "type": [ "Primary" ], "version": null, "note": null, "metadata": { "title": "Analysis of domain shift in whole prostate gland, zonal and lesions segmentation and detection, using multicentric retrospective data", "abstract": "", "date": "2024-03-01T00:00:00Z", "citationCount": 9, "authors": [], "journal": "Computers in Biology and Medicine" } } ], "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", "Documentor", "Maintainer" ], "note": null }, { "name": "Nickolas Papanikolaou", "email": null, "url": null, "orcidid": "https://orcid.org/0000-0003-3298-2072", "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Support" ], "note": null }, { "name": "Nuno Rodrigues", "email": null, "url": null, "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Person", "typeRole": [ "Developer" ], "note": null } ], "owner": "josegcpa", "additionDate": "2025-11-07T18:15:49.962044Z", "lastUpdate": "2025-11-28T12:18:55.232382Z", "editPermission": { "type": "private", "authors": [] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": "tool" }, { "name": "MITK", "description": "The Medical Imaging Interaction Toolkit (MITK) is a free open-source software for the development of interactive medical image processing software. Based on MITK, there is the MITK Workbench, a powerful and free application to view, process, and segment medical images.", "homepage": "https://www.mitk.org/", "biotoolsID": "mitk", "biotoolsCURIE": "biotools:mitk", "version": [ "v2023.04" ], "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_3551", "term": "nrrd" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "output": [ { "data": { "uri": "http://edamontology.org/data_2968", "term": "Image" }, "format": [ { "uri": "http://edamontology.org/format_3551", "term": "nrrd" }, { "uri": "http://edamontology.org/format_3548", "term": "DICOM format" }, { "uri": "http://edamontology.org/format_3549", "term": "nii" } ] } ], "note": null, "cmd": null } ], "toolType": [ "Workbench", "Desktop application", "Library" ], "topic": [], "operatingSystem": [ "Mac", "Linux", "Windows" ], "language": [ "C++" ], "license": "BSD-3-Clause", "collectionID": [ "EUCAIM" ], "maturity": "Mature", "cost": "Free of charge", "accessibility": "Open access", "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/MITK/MITK/", "type": [ "Repository" ], "note": null }, { "url": "https://lists.sourceforge.net/lists/listinfo/mitk-users", "type": [ "Mailing list" ], "note": "If you have any questions related to the use of or the development with MITK, please use the official mailing list.\n\nNote that you have to subscribe to the mailing list in order to send mails" } ], "download": [ { "url": "https://www.mitk.org/wiki/Downloads", "type": "Downloads page", "note": null, "version": null } ], "documentation": [ { "url": "https://docs.mitk.org/2023.04/", "type": [ "General" ], "note": null } ], "publication": [ { "doi": "10.1016/j.media.2005.04.005", "pmid": "15896995", "pmcid": null, "type": [], "version": null, "note": "Nolden, Marco, et al. \"The Medical Imaging Interaction Toolkit: challenges and advances: 10 years of open-source development.\"\nInternational journal of computer assisted radiology and surgery 8 (2013): 607-620", "metadata": { "title": "The medical imaging interaction toolkit", "abstract": "Thoroughly designed, open-source toolkits emerge to boost progress in medical imaging. The Insight Toolkit (ITK) provides this for the algorithmic scope of medical imaging, especially for segmentation and registration. But medical imaging algorithms have to be clinically applied to be useful, which additionally requires visualization and interaction. The Visualization Toolkit (VTK) has powerful visualization capabilities, but only low-level support for interaction. In this paper, we present the Medical Imaging Interaction Toolkit (MITK). The goal of MITK is to significantly reduce the effort required to construct specifically tailored, interactive applications for medical image analysis. MITK allows an easy combination of algorithms developed by ITK with visualizations created by VTK and extends these two toolkits with those features, which are outside the scope of both. MITK adds support for complex interactions with multiple states as well as undo-capabilities, a very important prerequisite for convenient user interfaces. Furthermore, MITK facilitates the realization of multiple, different views of the same data (as a multiplanar reconstruction and a 3D rendering) and supports the visualization of 3D+t data, whereas VTK is only designed to create one kind of view of 2D or 3D data. MITK reuses virtually everything from ITK and VTK. Thus, it is not at all a competitor to ITK or VTK, but an extension, which eases the combination of both and adds the features required for interactive, convenient to use medical imaging software. MITK is an open-source project (www.mitk.org) © 2005 Elsevier B.V. All rights reserved.", "date": "2005-12-01T00:00:00Z", "citationCount": 378, "authors": [ { "name": "Wolf I." }, { "name": "Vetter M." }, { "name": "Wegner I." }, { "name": "Bottger T." }, { "name": "Nolden M." }, { "name": "Schobinger M." }, { "name": "Hastenteufel M." }, { "name": "Kunert T." }, { "name": "Meinzer H.-P." } ], "journal": "Medical Image Analysis" } }, { "doi": "10.1007/s11548-013-0840-8", "pmid": "23588509", "pmcid": null, "type": [], "version": null, "note": "Nolden, Marco, et al. \"The Medical Imaging Interaction Toolkit: challenges and advances: 10 years of open-source development.\"\nInternational journal of computer assisted radiology and surgery 8 (2013): 607-620", "metadata": { "title": "The medical imaging interaction toolkit: Challenges and advances: 10 years of open-source development", "abstract": "Purpose: The Medical Imaging Interaction Toolkit (MITK) has been available as open-source software for almost 10 years now. In this period the requirements of software systems in the medical image processing domain have become increasingly complex. The aim of this paper is to show how MITK evolved into a software system that is able to cover all steps of a clinical workflow including data retrieval, image analysis, diagnosis, treatment planning, intervention support, and treatment control. Methods: MITK provides modularization and extensibility on different levels. In addition to the original toolkit, a module system, micro services for small, system-wide features, a service-oriented architecture based on the Open Services Gateway initiative (OSGi) standard, and an extensible and configurable application framework allow MITK to be used, extended and deployed as needed. A refined software process was implemented to deliver high-quality software, ease the fulfillment of regulatory requirements, and enable teamwork in mixed-competence teams. Results: MITK has been applied by a worldwide community and integrated into a variety of solutions, either at the toolkit level or as an application framework with custom extensions. The MITK Workbench has been released as a highly extensible and customizable end-user application. Optional support for tool tracking, image-guided therapy, diffusion imaging as well as various external packages (e.g. CTK, DCMTK, OpenCV, SOFA, Python) is available. MITK has also been used in several FDA/CE-certified applications, which demonstrates the high-quality software and rigorous development process. Conclusions: MITK provides a versatile platform with a high degree of modularization and interoperability and is well suited to meet the challenging tasks of today's and tomorrow's clinically motivated research. © 2013 CARS.", "date": "2013-01-01T00:00:00Z", "citationCount": 354, "authors": [ { "name": "Nolden M." }, { "name": "Zelzer S." }, { "name": "Seitel A." }, { "name": "Wald D." }, { "name": "Muller M." }, { "name": "Franz A.M." }, { "name": "Maleike D." }, { "name": "Fangerau M." }, { "name": "Baumhauer M." }, { "name": "Maier-Hein L." }, { "name": "Maier-Hein K.H." }, { "name": "Meinzer H.-P." }, { "name": "Wolf I." } ], "journal": "International Journal of Computer Assisted Radiology and Surgery" } } ], "credit": [ { "name": "German Cancer Research Center (DKFZ)", "email": null, "url": "https://www.dkfz.de/de", "orcidid": null, "gridid": "grid.7497.d", "rorid": "04cdgtt98", "fundrefid": null, "typeEntity": null, "typeRole": [], "note": null } ], "owner": "sschaum", "additionDate": "2023-06-26T12:38:15.108087Z", "lastUpdate": "2025-11-28T09:27:43.812008Z", "editPermission": { "type": "group", "authors": [ "ASHISR" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null }, { "name": "Lethe DICOM Anonymizer", "description": "A DICOM Anonymization pipeline in a Docker container. This pipeline is designed to anonymize DICOM files according to the EUCAIM standard and includes the following steps:\n\nStep 1 (Optional): Perform OCR on DICOM pixel data to remove sensitive information (burned-in information).\nStep 2: Deidentify DICOM metadata using the RSNA CTP Anonymizer and the EUCAIM anonymization script. \nStep 3 (Optional): Deidentify clinical data provided in CSV files so that the referenced patient id is anonymized the same way CTP does in Step 2.", "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/lethe-dicom-anonymizer/info-tab", "biotoolsID": "lethe_dicom_anonymizer", "biotoolsCURIE": "biotools:lethe_dicom_anonymizer", "version": [ "0.9.12" ], "otherID": [], "relation": [], "function": [ { "operation": [ { "uri": "http://edamontology.org/operation_3283", "term": "Anonymisation" } ], "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": null, "cmd": null } ], "toolType": [ "Desktop application", "Command-line tool" ], "topic": [ { "uri": "http://edamontology.org/topic_3384", "term": "Medical imaging" }, { "uri": "http://edamontology.org/topic_4044", "term": "Data protection" }, { "uri": "http://edamontology.org/topic_4012", "term": "FAIR data" } ], "operatingSystem": [ "Linux", "Mac", "Windows" ], "language": [ "Python" ], "license": "EUPL-1.1", "collectionID": [ "EUCAIM" ], "maturity": null, "cost": null, "accessibility": null, "elixirPlatform": [], "elixirNode": [], "elixirCommunity": [], "link": [ { "url": "https://github.com/cbml-forth/lethe_anon_pipeline", "type": [ "Repository" ], "note": null } ], "download": [], "documentation": [ { "url": "https://github.com/cbml-forth/lethe_anon_pipeline/blob/main/README.md", "type": [ "User manual" ], "note": null } ], "publication": [], "credit": [ { "name": "Computational BioMedicine Laboratory, Foundation for Research and Technology Hellas (FORTH)", "email": null, "url": "https://www.ics.forth.gr/cbml/?lang=en", "orcidid": null, "gridid": null, "rorid": null, "fundrefid": null, "typeEntity": "Institute", "typeRole": [ "Provider" ], "note": null } ], "owner": "vkalokyri", "additionDate": "2025-10-29T08:08:30.731334Z", "lastUpdate": "2025-11-28T07:49:09.801303Z", "editPermission": { "type": "group", "authors": [ "ssfak" ] }, "validated": 0, "homepage_status": 0, "elixir_badge": 0, "confidence_flag": null } ] }