The official 3DSlicer Extension of MOOSE 3.0

MOOSE (Multi-organ objective segmentation) is a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.

Using the Extension

1. Find the extension in the Segmentation category and open it.
2. When you use the extension the first time, only the Install Dependencies button will be enabled. Please click it to perform the initial setup of dependencies, which includes PyTorch and MOOSE.
   • For Windows users: Please make sure that the PyTorch extension is also installed.
   • For Linux/Unix users: The PyTorch extension is not required, as native torch works in most cases.
   • The installation will take a while, and it is normal that 3DSlicer might become unresponsive during the installation.
3. After a successful installation, the Input Volume and Model selector section will be enabled.
4. Select a volume (which has to be a CT image) and a model and click on Run Segmentation and wait for the process to finish.
   • Our dataset consists mostly of total/whole body CT images. We strongly recommend to use only CT images with a similar field of view.
   • The clin_ct_body_composition model requires the L3 region of vertebrae to be in the field of view. If the model does not generate a segmentation, the field of view of the CT might be not suitable.
5. After the process finished, you will be able to select the segmentation and display/work with/modify it as you please.
   • The expected compute time of total/whole body CTs with a GPU is around 60 seconds.
   • With only CPU enabled, it might take up to 40 minutes.

Available Segmentation Models 🧬

MOOSE 3.0 offers a wide range of segmentation models catering to various clinical and preclinical needs. Here are the models currently available:

Model Name	Intensities and Regions
clin_ct_cardiac	1: heart_myocardium, 2: heart_atrium_left, 3: heart_atrium_right, 4: heart_ventricle_left, 5: heart_ventricle_right, 6: aorta, 7: iliac_artery_left, 8: iliac_artery_right, 9: iliac_vena_left, 10: iliac_vena_right, 11: inferior_vena_cava, 12: portal_splenic_vein, 13: pulmonary_artery
clin_ct_digestive	1: colon, 2: duodenum, 3: esophagus, 4: small_bowel
clin_ct_lungs	1:lung_upper_lobe_left, 2:lung_lower_lobe_left, 3:lung_upper_lobe_right, 4:lung_middle_lobe_right, 5:lung_lower_lobe_right
clin_ct_muscles	1: autochthon_left, 2: autochthon_right, 3: gluteus_maximus_left, 4: gluteus_maximus_right, 5: gluteus_medius_left, 6: gluteus_medius_right, 7: gluteus_minimus_left, 8: gluteus_minimus_right, 9: iliopsoas_left, 10: iliopsoas_right
clin_ct_organs	1: adrenal_gland_left, 2: adrenal_gland_right, 3: bladder, 4: brain, 5: gallbladder, 6: kidney_left, 7: kidney_right, 8: liver, 9: lung_lower_lobe_left, 10: lung_lower_lobe_right, 11: lung_middle_lobe_right, 12: lung_upper_lobe_left, 13: lung_upper_lobe_right, 14: pancreas, 15: spleen, 16: stomach, 17: thyroid_left, 18: thyroid_right, 19: trachea
clin_ct_peripheral_bones	1: carpal_left, 2: carpal_right, 3: clavicle_left, 4: clavicle_right, 5: femur_left, 6: femur_right, 7: fibula_left, 8: fibula_right, 9: fingers_left, 10: fingers_right, 11: humerus_left, 12: humerus_right, 13: metacarpal_left, 14: metacarpal_right, 15: metatarsal_left, 16: metatarsal_right, 17: patella_left, 18: patella_right, 19: radius_left, 20: radius_right, 21: scapula_left, 22: scapula_right, 23: skull, 24: tarsal_left, 25: tarsal_right, 26: tibia_left, 27: tibia_right, 28: toes_left, 29: toes_right, 30: ulna_left, 31: ulna_right
clin_ct_ribs	1: rib_left_1, 2: rib_left_2, 3: rib_left_3, 4: rib_left_4, 5: rib_left_5, 6: rib_left_6, 7: rib_left_7, 8: rib_left_8, 9: rib_left_9, 10: rib_left_10, 11: rib_left_11, 12: rib_left_12, 13: rib_left_13, 14: rib_right_1, 15: rib_right_2, 16: rib_right_3, 17: rib_right_4, 18: rib_right_5, 19: rib_right_6, 20: rib_right_7, 21: rib_right_8, 22: rib_right_9, 23: rib_right_10, 24: rib_right_11, 25: rib_right_12, 26: rib_right_13, 27: sternum
clin_ct_vertebrae	1: vertebra_C1, 2: vertebra_C2, 3: vertebra_C3, 4: vertebra_C4, 5: vertebra_C5, 6: vertebra_C6, 7: vertebra_C7, 8: vertebra_T1, 9: vertebra_T2, 10: vertebra_T3, 11: vertebra_T4, 12: vertebra_T5, 13: vertebra_T6, 14: vertebra_T7, 15: vertebra_T8, 16: vertebra_T9, 17: vertebra_T10, 18: vertebra_T11, 19: vertebra_T12, 20: vertebra_L1, 21: vertebra_L2, 22: vertebra_L3, 23: vertebra_L4, 24: vertebra_L5, 25: vertebra_L6, 26: hip_left, 27: hip_right, 28: sacrum
clin_ct_body_composition	1: skeletal_muscle, 2: subcutaneous_fat, 3: visceral_fat

More on MOOSE

For more information, visit the package repository of MOOSE. There, you will find installation instructions for the CLI version and the package variant of MOOSE as well as licensing information.

Citation

If you like MOOSE3.0, please cite it's original publication

Shiyam Sundar LK, et al. Fully Automated, Semantic Segmentation of Whole-Body 18F-FDG PET/CT Images Based on Data-Centric Artificial Intelligence. Journal of Nuclear Medicine. 2022 Dec;63(12):1941-1948. doi:10.2967/jnumed.122.264063

Also, please cite nnU-Net:

Isensee F, et al. nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nature Methods. 2021;18(2):203-211. doi:10.1038/s41592-020-01008-z

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