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        {
            "name": "MR-based neuroblastoma tumour detection and segmentation (EUCAIM-SW-019_T-01-02-002)",
            "description": "This tool is specifically designed and validated for automated detection and segmentation of neuroblastic tumours in T2-weighted magnetic resonance images (T2-MR) using deep learning. It processes DICOM or NIfTI input data and outputs in NIFTI or DICOM SEG.\n\nTRAINING & VALIDATION COHORTS:\n\nInitial Development (Veiga-Canuto 2022):\n-Training: 106 patients, 5-fold CV (median DSC 0.965 ± 0.018).\n-Internal validation: 26 patients (median DSC 0.918 ± 0.067).\n-Sources: La Fe (Spain), SIOPEN HR-NBL1/LINES, St. Anna (Austria), Pisa (Italy).\n-Mean age: 37.6 ± 39.3 months.\n-Median tumor volume: 116,518 mm³.\n\nExternal Validation (Veiga-Canuto 2023):\n-300 patients, 535 independent T2 MRI scans (486 at diagnosis, 49 post-chemotherapy).\n-Performance: median DSC 0.997 (0.944–1.000), 94% successful detection.\n-Sources: 12 European countries (HR-NBL1/SIOPEN 119, LINES/SIOPEN 107, German Registry 62, others 12).\n-Heterogeneous data: 1.5T (435), 3T (100); Siemens (318), Philips (109), GE (105), Canon (3).",
            "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/mr_based_neuroblastoma_tumour_detection_and_segmentation/info-tab",
            "biotoolsID": "mr-based_neuroblastoma_tumour_detection_and_segmentation",
            "biotoolsCURIE": "biotools:mr-based_neuroblastoma_tumour_detection_and_segmentation",
            "version": [
                "2.0.0"
            ],
            "otherID": [],
            "relation": [],
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                    "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_3549",
                                    "term": "nii"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
                                },
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                                    "uri": "http://edamontology.org/format_3752",
                                    "term": "CSV"
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                                },
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                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
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                    ],
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                    "cmd": "docker run --rm \\\n  -v \"<input_path>:/input\" \\\n  -v \"<output_path>:/output\" \\\n  --gpus all \\\n  harbor.eucaim.cancerimage.eu/processing-tools/mr_based_neuroblastoma_tumour_detection_and_segmentation:2.0.0 \\\n  --mode dicom-seg \\\n  --series-selector /output/config/series_to_segment.csv \\\n  --seg-series-number 2302001 \\\n  --seg-algorithm-name \"nnUNet_Neuroblastoma_Primage_training\" \\\n  --seg-coordinating-center \"EUCAIM Consortium\" \\\n  --keep-nifti false"
                }
            ],
            "toolType": [
                "Library"
            ],
            "topic": [
                {
                    "uri": "http://edamontology.org/topic_2640",
                    "term": "Oncology"
                },
                {
                    "uri": "http://edamontology.org/topic_3474",
                    "term": "Machine learning"
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                    "uri": "http://edamontology.org/topic_3365",
                    "term": "Data architecture, analysis and design"
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                    "url": "https://www.linkedin.com/company/grupo-de-investigaci%C3%B3n-biom%C3%A9dica-en-imagen/",
                    "type": [
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            "documentation": [
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                    "url": "https://www.mdpi.com/2072-6694/14/15/3648",
                    "type": [
                        "General"
                    ],
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                {
                    "url": "https://drive.eucaim.cancerimage.eu/s/44BxMSNpxwgsZiE",
                    "type": [
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                    "doi": "10.3390/cancers14153648",
                    "pmid": null,
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                    "version": null,
                    "note": null,
                    "metadata": null
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                    "doi": "10.3390/cancers15051622",
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                    "typeEntity": "Institute",
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                    "name": "Leonor Cerda-Alberich",
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                    "name": "Diana Veiga Canuto",
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        },
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            "name": "DICOM File Integrity Checker (EUCAIM-SW-002_T-01-01-002)",
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            "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/3/repositories/dicom_file_integrity_checker/info-tab",
            "biotoolsID": "dicom_file_integrity_checker_by_gibi230",
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                "2.1.1"
            ],
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                            "uri": "http://edamontology.org/operation_0336",
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                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3620",
                                    "term": "xlsx"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
                                }
                            ]
                        }
                    ],
                    "output": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3620",
                                    "term": "xlsx"
                                },
                                {
                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
                                }
                            ]
                        }
                    ],
                    "note": "For Data Holders (ingestion-tools)",
                    "cmd": "docker run -it --rm --name my-container \\\n  -v \"<input_path>:/input\" \\\n  -v \"<output_path>:/output\" \\\n  -v \"<config_path>:/config\" \\\n  harbor.eucaim.cancerimage.eu/ingestion-tools/dicom_file_integrity_checker:latest"
                },
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                            "uri": "http://edamontology.org/operation_0336",
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                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
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                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
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                                    "uri": "http://edamontology.org/format_3548",
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                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
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                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_2331",
                                    "term": "HTML"
                                },
                                {
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                                    "term": "xlsx"
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                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
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                }
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            ],
            "topic": [
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                    "uri": "http://edamontology.org/topic_3316",
                    "term": "Computer science"
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                    "uri": "http://edamontology.org/topic_3077",
                    "term": "Data acquisition"
                },
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                    "term": "Data management"
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            "link": [
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                    "type": [
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                    "note": null
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                    "type": [
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                    ],
                    "note": "Link to EUCAIM's Harbor for Data Holders"
                },
                {
                    "url": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/dicom_file_integrity_checker/artifacts-tab",
                    "type": [
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                    ],
                    "note": "Link to EUCAIM's Harbor for Data Users"
                }
            ],
            "download": [
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                    "type": [
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                    "note": null
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                    "type": [
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                        "Developer",
                        "Documentor",
                        "Maintainer"
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                    "note": null
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                    "name": "Adrian Galiana-Bordera",
                    "email": "adrian_galiana@iislafe.es",
                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-8324-8284",
                    "gridid": null,
                    "rorid": null,
                    "fundrefid": null,
                    "typeEntity": "Person",
                    "typeRole": [
                        "Developer"
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                },
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                    "name": "Carina Soler-Pons",
                    "email": "carina_soler@iislafe.es",
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                    "url": null,
                    "orcidid": "https://orcid.org/0000-0002-8234-010X",
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                    "typeEntity": "Person",
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            "owner": "pedromiguel_martinez_HULAFE",
            "additionDate": "2025-04-15T17:42:40.968987Z",
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        },
        {
            "name": "Denoising-Inhomogeneity Correction Tool (EUCAIM-SW-015_T-01-01-015)",
            "description": "The tool is designed to perform a customisable image pre-processing to reduce noise and inhomogeneity field effect, thus improving image quality and reproducibility of radiomics features. This tool consists of two independent steps: one for denoising using one of the 5 integrated filters (Bilateral Filter, Anisotropic Diffusion Filter (ADF), Curvature Flow Filter (CFF), SUSAN and Non Local Means (NLM)), and another for the ANTs N4 and another for the ANT's N4 bias correction filter. The parameter configuration of this tool has been optimised for TW1, T2W, DWI and DCE sequences in neuroblastoma (NB) and paediatric brain tumours, but it can also be configured with some of their parameters using a JSON parameter configuration file.",
            "homepage": "https://harbor.eucaim.cancerimage.eu/harbor/projects/4/repositories/denoising_inhomogeneity_correction_tool/info-tab",
            "biotoolsID": "denoising-inhomogeneity_correction_tool",
            "biotoolsCURIE": "biotools:denoising-inhomogeneity_correction_tool",
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            ],
            "otherID": [],
            "relation": [],
            "function": [
                {
                    "operation": [
                        {
                            "uri": "http://edamontology.org/operation_3695",
                            "term": "Data filtering"
                        }
                    ],
                    "input": [
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
                            },
                            "format": [
                                {
                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
                                }
                            ]
                        },
                        {
                            "data": {
                                "uri": "http://edamontology.org/data_2048",
                                "term": "Report"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_3464",
                                    "term": "JSON"
                                }
                            ]
                        }
                    ],
                    "output": [
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                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
                            },
                            "format": [
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                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
                                }
                            ]
                        }
                    ],
                    "note": null,
                    "cmd": "docker run -it --rm --name container_name -v \"<input_path>:/input\" -v \"<output_path>:/output\" -v \"<config_path>:/config\" harbor.eucaim.cancerimage.eu/processing-tools/denoising_inhomogeneity_correction_tool:1.1.0 --config /config/config.json"
                },
                {
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                            "term": "Data filtering"
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                    ],
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                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
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                            "format": [
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                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
                                }
                            ]
                        }
                    ],
                    "output": [
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                            "data": {
                                "uri": "http://edamontology.org/data_2968",
                                "term": "Image"
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                            "format": [
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                                    "uri": "http://edamontology.org/format_3548",
                                    "term": "DICOM format"
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                    "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.",
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