- [30 Sep 2026] 🚀 Code, Grand Challenge containers and weights for both ATM'26 tracks are released.
CaliTree is a two-track solution to the ATM'26 challenge (MICCAI 2026):
| Track | Task |
|---|---|
| Track 1 | Binary airway segmentation from chest CT |
| Track 2 | Branch-wise anatomical labelling of the airway tree into 20 classes (trachea plus 19 segmental bronchi) |
flowchart LR
CT[Chest CT] --> S[Calibre-band nnU-Net<br/>ResEnc-M, 5 folds]
S --> M[Binary airway mask]
M --> T1[Track 1 output]
CT --> V[21-class voxel nnU-Net<br/>warm-started]
M --> K[TEASAR skeleton<br/>branch split]
V --> D[Branch vote<br/>tree-prior decoding]
K --> D
D --> T2[Track 2 output]
- Backbone. nnU-Net v2 with a residual encoder (ResEnc-M,
3d_fullres), 5-fold ensemble. - Calibre-band targets. Each airway voxel gets the calibre of its nearest centerline point (large, mid or distal) plus a centerline-tube flag. The network is trained on four nested regions: airway, mid+distal, distal and centerline.
- Loss on the airway channel:
- region BCE + Dice
- General Union Loss (alpha 0.1)
- Skeleton Recall on the centerline region
- a soft-skeleton precision term (the precision half of clDice, weight 0.25)
- Inference. Sliding window (tile step 0.75) with single-axis flip TTA. The airway, mid+distal and distal channels are each thresholded at 0.3 and their union is taken, then a largest-connected-component filter is applied.
- Segment. A Track-1 style calibre-band segmenter produces the binary airway mask.
- Label voxels. A 21-class nnU-Net (
3d_fullres_SAS, no mirroring) labels voxels. It was warm-started from the official ATM'26 Track-2 baseline weights and fine-tuned for 1000 epochs. - Decode branches. A TEASAR skeleton of the airway mask splits it into branches. Each branch gets one label from a vote over the voxel predictions, decoded under a tree prior, and the labels are rasterised back to the mask.
git clone https://github.com/adinathdukre/CaliTree.git
cd CaliTree
pip install -r requirements.txthuggingface-cli download adidukre/CaliTree --local-dir /path/to/weights/path/to/weights/
├── track1/ postproc.json, nnUNet_ckpts/ (5 folds)
└── track2/ postproc_t2.json, nnUNet_ckpts/ (segmenter, 5 folds), nnUNet_t2warm/ (21-class model, 5 folds)
The input folder holds CT volumes as .mha files (<case>_0000.mha or <case>.mha).
# Track 1: writes /path/to/output/images/binary-airway-segmentation/<case>.mha
ATM_RESOURCES=/path/to/weights/track1 ATM_INPUT=/path/to/ct_folder ATM_OUTPUT=/path/to/output \
python docker/T1/inference.py
# Track 2: writes /path/to/output/images/multi-class-airway-segmentation/<case>.mha
ATM_RESOURCES=/path/to/weights/track2 ATM_INPUT=/path/to/ct_folder ATM_OUTPUT=/path/to/output \
python docker/T2/inference.pyNote
With one input case, the output is named output.mha.
Optional arguments:
| Variable | Default | Purpose |
|---|---|---|
ATM_DEVICE |
cuda |
set to cpu to run without a GPU |
ATM_FOLDS |
all | segmentation folds, e.g. 0,1 |
ATM_TILE_STEP |
0.75 |
sliding-window step |
ATM_WARM_FOLDS |
0 |
Track-2 21-class model folds |
The images follow the ATM'26 Grand Challenge and batch contract: they read /input/images/lung-ct/*.mha and write to /output/images/<slug>/. They build on the organisers' CUDA 11.8 nnU-Net base image, atm26-nnunetv2:2.6.4-cuda11.8.
bash docker/T1/build.sh /path/to/weights/track1 atm26-track1
bash docker/T2/build.sh /path/to/weights/track2 atm26-track2
docker run --rm --gpus all -v /path/to/input:/input:ro -v /path/to/output:/output atm26-track1Tip
To also export a .tar.gz of the image, set ATM_EXPORT=1 when you build.
export ATM_DATA=/path/to/workdir
source scripts/env.shTrack 1 (Dataset501, calibre bands)
python scripts/prepare_nnunet_track1.py \
--images /path/to/ATM26/Track-1/imagesTr --labels /path/to/ATM26/Track-1/labelsTr
bash scripts/plan_preprocess_t1.sh
for f in 0 1 2 3 4; do bash scripts/train_t1.sh $f; done # Track-1 model
for f in 0 1 2 3 4; do bash scripts/train_t1.sh $f nnUNetTrainerAirwayCB; done # Track-2 stage-1 segmenterTrack 2 (Dataset502, 21 classes)
[!IMPORTANT] This track needs the official ATM'26 Track-2 baseline weights, which are used for the warm start.
python scripts/prepare_nnunet_track2.py \
--images /path/to/ATM26/Track-1/imagesTr --labels /path/to/ATM26/Track-2/labelsTr
nnUNetv2_extract_fingerprint -d 502
python scripts/prepare_t2_plans.py --baseline-plans /path/to/t2_baseline/nnUNet_ckpts/plans.json
nnUNetv2_preprocess -d 502 -plans_name nnUNetPlansOrg -c 3d_fullres_SAS
export ATM_T2_INIT=/path/to/t2_baseline/nnUNet_ckpts
for f in 0 1 2 3 4; do bash scripts/train_t2.sh $f; done
python scripts/patch_t2_resampling.py $nnUNet_results/Dataset502_ATM26Track2Seg/nnUNetTrainerT2Warm__nnUNetPlansOrg__3d_fullres_SAS/plans.jsonpatch_t2_resampling.py switches the 21-class probability resampling to nnU-Net's torch resampler. This keeps inference memory well below 32 GB.
CaliTree/
├── airway/
│ ├── losses/ # General Union, Skeleton Recall and soft-skeleton precision losses
│ ├── inference/ # post-processing, batch I/O, Track-2 voxel model and tree decoding
│ ├── graph/ # TEASAR branch extraction and the 21-class taxonomy
│ └── eval/ # connected-component and skeleton utilities
├── nnunet_trainers/ # custom nnU-Net trainers (nnUNetTrainerAirwayCB_clprec025, nnUNetTrainerT2Warm)
├── scripts/ # dataset preparation, planning and training
└── docker/
├── T1/ # Track-1 submission container and entry point
└── T2/ # Track-2 submission container and entry point
For questions or collaboration, please open an issue or reach out to Adinath Madhavrao Dukre.
Important
CaliTree is intended for research only. It is not approved for clinical use and must not inform any diagnostic or treatment decision.