MOOSE
MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet and has the capability to segment 120 unique tissue classes from a whole-body 18F-FDG PET/CT image.
Install / Use
/learn @ENHANCE-PET/MOOSEREADME

MOOSE 3.0 🦌- Furiously Fast. Brutally Efficient. Unmatched Precision. 💪
Welcome to the new and improved MOOSE (v3.0), where speed and efficiency aren't just buzzwords—they're a way of life.
💨 3x Faster Than Before
Like a moose sprinting through the woods (okay, maybe not that fast), MOOSE 3.0 is built for speed. It's 3x faster than its older sibling, MOOSE 2.0, which was already no slouch. Blink and you'll miss it. ⚡
💻 Memory: Light as a Feather, Strong as a Bull
Forget "Does it fit on my laptop?" The answer is YES. 🕺 Thanks to Dask wizardry, all that data stays in memory. No disk writes, no fuss. Run total-body CT on that 'decent' laptop you bought three years ago and feel like you’ve upgraded. 🥳
🛠️ Any OS, Anytime, Anywhere
Windows, Mac, Linux—we don’t play favorites. 🍏 Mac users, you’re in luck: MOOSE runs natively on MPS, getting you GPU-like speeds without the NVIDIA guilt. 🚀
🎯 Trained to Perfection
This is our best model yet, trained on a whopping 1.7k datasets. More data, better results. Plus you can run multiple models at the same time - You'll be slicing through images like a knife through warm butter. (Or tofu, if you prefer.) 🧈🔪
🖥️ The 'Herd' Mode 🖥️
Got a powerhouse server just sitting around? Time to let the herd loose! Flip the Herd Mode switch and watch MOOSE multiply across your compute like... well, like a herd of moose! 🦌🦌🦌 The more hardware you have, the faster your inference gets done. Scale up, speed up, and make every bit of your server earn its oats. 🌾💨
MOOSE 3.0 isn't just an upgrade—it's a lifestyle. A faster, leaner, and stronger lifestyle. Ready to join the herd? 🦌✨
https://github.com/user-attachments/assets/b121a9f5-30b6-4a40-a451-6bad6570eb55
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:
Clinical 👫🏽
| Model Name | Intensities and Regions |
|-----------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| clin_ct_body | 1:Legs, 2:Body, 3:Head, 4:Arms |
| 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 |
Preclinical 🐁
| Model Name | Intensities and Regions
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