{
    "camitk": {
        "extensions": {
            "actions": [
                {
                    "classMembers": "     QProcess* totalSegmentatorProcess;\n    void defineTotalSegmentatorExecutable();",
                    "classification": {
                        "family": "Segmentation",
                        "tags": [
                            "TotalSegmentator"
                        ]
                    },
                    "componentClass": "ImageComponent",
                    "description": "<p>Segment CT or MRI using <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a>.</p><p>If you use this tool please cite it as follows:<br/>Wasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., Segeroth, M., 2023. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. <a href='https://doi.org/10.1148/ryai.230024'>DOI</a></p><p>TotalSegmentator is heavily based on <a href='https://github.com/MIC-DKFZ/nnUNet'>nnUnet</a>.</p>",
                    "gui": "Default Action GUI",
                    "headerIncludes": "#include<QProcess>",
                    "name": "TotalSegmentator",
                    "notEmbedded": false,
                    "parameters": [
                        {
                            "defaultValue": 0,
                            "description": "<ul dir=\\\"auto\\\"> <li><strong>total</strong>: default task containing 117 main classes (see <a href='https://github.com/wasserth/TotalSegmentator#class-details'>here</a> for a list of classes)</li> <li><strong>total_mr</strong>: default task containing 50 main classes on MR images (see <a href=\\\"https://github.com/wasserth/TotalSegmentator#class-details\\\">here</a> for a list of classes)</li> <li><strong>lung_vessels</strong>: lung_vessels (cite <a href=\\\"https://www.sciencedirect.com/science/article/pii/S0720048X22001097\\\" rel=\\\"nofollow\\\">paper</a>), lung_trachea_bronchia</li> <li><strong>body</strong>: body, body_trunc, body_extremities, skin</li> <li><strong>body_mr</strong>: body_trunc, body_extremities (for MR images)</li> <li><strong>vertebrae_mr</strong>: sacrum, vertebrae_L5, vertebrae_L4, vertebrae_L3, vertebrae_L2, vertebrae_L1, vertebrae_T12, vertebrae_T11, vertebrae_T10, vertebrae_T9, vertebrae_T8, vertebrae_T7, vertebrae_T6, vertebrae_T5, vertebrae_T4, vertebrae_T3, vertebrae_T2, vertebrae_T1, vertebrae_C7, vertebrae_C6, vertebrae_C5, vertebrae_C4, vertebrae_C3, vertebrae_C2, vertebrae_C1 (for CT this is part of the <code>total</code> task)</li> <li><strong>cerebral_bleed</strong>: intracerebral_hemorrhage (cite <a href=\\\"https://www.mdpi.com/2077-0383/12/7/2631\\\" rel=\\\"nofollow\\\">paper</a>)*</li> <li><strong>hip_implant</strong>: hip_implant*</li> <li><strong>pleural_pericard_effusion</strong>: pleural_effusion (cite <a href=\\\"http://dx.doi.org/10.1097/RLI.0000000000000869\\\" rel=\\\"nofollow\\\">paper</a>), pericardial_effusion (cite <a href=\\\"http://dx.doi.org/10.3390/diagnostics12051045\\\" rel=\\\"nofollow\\\">paper</a>)*</li> <li><strong>head_glands_cavities</strong>: eye_left, eye_right, eye_lens_left, eye_lens_right, optic_nerve_left, optic_nerve_right, parotid_gland_left, parotid_gland_right, submandibular_gland_right, submandibular_gland_left, nasopharynx, oropharynx, hypopharynx, nasal_cavity_right, nasal_cavity_left, auditory_canal_right, auditory_canal_left, soft_palate, hard_palate (cite <a href=\\\"https://www.mdpi.com/2072-6694/16/2/415\\\" rel=\\\"nofollow\\\">paper</a>)</li> <li><strong>head_muscles</strong>: masseter_right, masseter_left, temporalis_right, temporalis_left, lateral_pterygoid_right, lateral_pterygoid_left, medial_pterygoid_right, medial_pterygoid_left, tongue, digastric_right, digastric_left</li> <li><strong>headneck_bones_vessels</strong>: larynx_air, thyroid_cartilage, hyoid, cricoid_cartilage, zygomatic_arch_right, zygomatic_arch_left, styloid_process_right, styloid_process_left, internal_carotid_artery_right, internal_carotid_artery_left, internal_jugular_vein_right, internal_jugular_vein_left (cite <a href=\\\"https://www.mdpi.com/2072-6694/16/2/415\\\" rel=\\\"nofollow\\\">paper</a>)</li> <li><strong>headneck_muscles</strong>: sternocleidomastoid_right, sternocleidomastoid_left, superior_pharyngeal_constrictor, middle_pharyngeal_constrictor, inferior_pharyngeal_constrictor, trapezius_right, trapezius_left, platysma_right, platysma_left, levator_scapulae_right, levator_scapulae_left, anterior_scalene_right, anterior_scalene_left, middle_scalene_right, middle_scalene_left, posterior_scalene_right, posterior_scalene_left, sterno_thyroid_right, sterno_thyroid_left, thyrohyoid_right, thyrohyoid_left, prevertebral_right, prevertebral_left (cite <a href=\\\"https://www.mdpi.com/2072-6694/16/2/415\\\" rel=\\\"nofollow\\\">paper</a>)</li> <li><strong>liver_vessels</strong>: liver_vessels, liver_tumor (cite <a href=\\\"https://arxiv.org/abs/1902.09063\\\" rel=\\\"nofollow\\\">paper</a>)*</li> <li><strong>oculomotor_muscles</strong>: skull, eyeball_right, lateral_rectus_muscle_right, superior_oblique_muscle_right, levator_palpebrae_superioris_right, superior_rectus_muscle_right, medial_rectus_muscle_left, inferior_oblique_muscle_right, inferior_rectus_muscle_right, optic_nerve_left, eyeball_left, lateral_rectus_muscle_left, superior_oblique_muscle_left, levator_palpebrae_superioris_left, superior_rectus_muscle_left, medial_rectus_muscle_right, inferior_oblique_muscle_left, inferior_rectus_muscle_left, optic_nerve_right*</li> <li><strong>lung_nodules</strong>: lung, lung_nodules (provided by <a href=\\\"https://bluemind.co/\\\" rel=\\\"nofollow\\\">BLUEMIND AI</a>: Fitzjalen R., Aladin M., Nanyan G.) (trained on 1353 subjects, partly from LIDC-IDRI)</li> <li><strong>kidney_cysts</strong>: kidney_cyst_left, kidney_cyst_right (strongly improved accuracy compared to kidney_cysts inside of <code>total</code> task)</li> <li><strong>breasts</strong>: breast</li> <li><strong>liver_segments</strong>: liver_segment_1, liver_segment_2, liver_segment_3, liver_segment_4, liver_segment_5, liver_segment_6, liver_segment_7, liver_segment_8 (Couinaud segments)</li> <li><strong>liver_segments_mr</strong>: liver_segment_1, liver_segment_2, liver_segment_3, liver_segment_4, liver_segment_5, liver_segment_6, liver_segment_7, liver_segment_8 (for MR images) (Couinaud segments)</li> </ul>",
                            "enumValues": [
                                "total",
                                "total_mr",
                                "lung_vessels",
                                "body",
                                "body_mr",
                                "vertebrae_mr",
                                "cerebral_bleed",
                                "hip_implant",
                                "pleural_pericard_effusion",
                                "head_glands_cavities",
                                "head_muscles",
                                "headneck_bones_vessels",
                                "headneck_muscles",
                                "liver_vessels",
                                "oculomotor_muscles",
                                "lung_nodules",
                                "kidney_cysts",
                                "breasts",
                                "liver_segments",
                                "liver_segments_mr"
                            ],
                            "group": "",
                            "name": "Task",
                            "readOnly": false,
                            "type": "enum",
                            "unit": ""
                        },
                        {
                            "defaultValue": false,
                            "description": "",
                            "enumValues": [
                                "cpu",
                                "gpu"
                            ],
                            "group": "",
                            "name": "Gpu",
                            "readOnly": false,
                            "type": "bool",
                            "unit": ""
                        },
                        {
                            "defaultValue": 0,
                            "description": "",
                            "enumValues": [
                                "Normal",
                                "Fast (resolution 3mm)",
                                "Fastest (resolution 6mm)"
                            ],
                            "group": "",
                            "name": "Fast",
                            "readOnly": false,
                            "type": "enum",
                            "unit": ""
                        },
                        {
                            "defaultValue": false,
                            "description": "Generate Meshes using Reconstruction Action",
                            "group": "",
                            "name": "Generate Meshes",
                            "readOnly": false,
                            "type": "bool",
                            "unit": ""
                        }
                    ]
                }
            ],
            "alwaysRebuild": true,
            "applications": [
            ],
            "components": [
            ],
            "contact": {
                "author": "CamiTK team",
                "email": "timc-camitk-contact@univ-grenoble-alpes.fr"
            },
            "description": "Segment CT or MRI using <a href='https://github.com/wasserth/TotalSegmentator'>TotalSegmentator</a>.\n\nIf you use this tool please cite it as follows:\nWasserthal, J., Breit, H.-C., Meyer, M.T., Pradella, M., Hinck, D., Sauter, A.W., Heye, T., Boll, D., Cyriac, J., Yang, S., Bach, M., Segeroth, M., 2023. TotalSegmentator: Robust Segmentation of 104 Anatomic Structures in CT Images. Radiology: Artificial Intelligence. https://doi.org/10.1148/ryai.230024\n\nTotalSegmentator is heavily based on nnUnet (https://github.com/MIC-DKFZ/nnUNet).\n\nNote: as TotalSegmentator only use nifti format for I/O and that nifti format, this extension depends on the imaging CEP (for now).",
            "generationType": "Standard",
            "libraries": [
            ],
            "license": "LGPL-3 CamiTK",
            "name": "TotalSegmentator",
            "viewers": [
            ]
        },
        "timestamp": "2025-05-09T17:39:37",
        "version": "camitk-6.0"
    }
}
