ATLAS-Net: Multi-Organ Abdominal CT Segmentation

This repository provides the nnUNet v2 checkpoint for multi-organ abdominal CT segmentation.

The model was trained on a large-scale multi-center CT dataset spanning 145 medical centers, with pseudo-labels automatically corrected using organ-specific anatomical rules across 25 organ classes.

First of all, you need a Linux OS server and basic terminal usage. You also need an NVIDIA GPU with CUDA 11.7 or higher.

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0. Before You Start

You need:

• CT files in .nii.gz format
• Each CT is a 3D volume (not single slices)
• File names must end with _0000.nii.gz

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1. Installation

git clone https://huggingface.co/Koushik45048545309/ATLAS-Net
conda create -n atlasnet python=3.10 -y
conda activate atlasnet
cd ATLAS-Net
pip install nnunetv2

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2. Run Inference on Example CT Scans

Example CT scans are stored in this path: ./nnUNet_eval/Dataset001_ATLASNet/imagesTs/

This is the organization of example CT scans:

nnUNet_eval/
  └── Dataset001_ATLASNet/
        └── imagesTs/
              ├── PanTS_00000001_0000.nii.gz
              ├── PanTS_00000002_0000.nii.gz
              ├── PanTS_00000003_0000.nii.gz
              └── ...

Directly run this command in terminal:

bash inference.sh

If you want to run inference on your own CT scans, place them in the same folder. Make sure the file name ends with _0000.nii.gz.

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3. Output

Predictions are saved to ./nnUNet_predictions/:

nnUNet_predictions/
  ├── PanTS_00000001.nii.gz
  ├── PanTS_00000002.nii.gz
  ├── PanTS_00000003.nii.gz
  └── ...

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4. Label Map

Label	Organ
0	Background
1	Aorta
2	Adrenal gland (L)
3	Adrenal gland (R)
4	Common bile duct
5	Celiac artery
6	Colon
7	Duodenum
8	Gallbladder
9	Inferior vena cava
10	Kidney (L)
11	Kidney (R)
12	Liver
13	Pancreas
14	Pancreatic duct
15	Superior mesenteric artery
16	Small intestine
17	Spleen
18	Stomach
19	Portal/splenic veins
20	Renal vein (L)
21	Renal vein (R)
22	CBD stent
23	Pancreatic PDAC
24	Pancreatic cyst
25	Pancreatic PNET

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License

CC BY 4.0