SCALA: Semi-supervised Cascade for Left Atrial Scar, Cavity, and Multi-Structure CT Segmentation

📄 Paper  |  🌐 Project Page  |  🤗 Checkpoints  |  🧠 Method  |  ⚡ Inference

Atharva Atul Rege, Adinath Madhavrao Dukre, Sarth Santosh Shah, Imran Razzak

🔥 News

• [30 Sep 2026] 🚀 Code, Docker build and checkpoints for all three CARE LeftAtrium tasks are released.
• [24 Aug 2026] 🎉 Our SCALA paper is accepted as an oral presentation (top 15% of all papers) at the MICCAI 2026 CARE workshop and published on OpenReview.

Overview

Official code for SCALA, our solution to the three tasks of the MICCAI 2026 CARE LeftAtrium challenge:

Task	Modality	Target
Task 1	LGE-MRI	Left atrial scar quantification
Task 2	LGE-MRI	Left atrial cavity segmentation
Task 3	CT	Multi-structure segmentation (LA, LAA, PV)

Fig. 1. Overview. One recipe is instantiated three times and trained separately; the only path between tasks is the out-of-fold cavity prediction that conditions the scar model. Image panels are real slices; the 3D shapes and icons are schematic.

flowchart LR
    subgraph T2[Task 2: cavity]
        M1[LGE-MRI] --> C1[ResEnc-L + MedNeXt-M] --> C2[Connected-component cleanup]
    end
    subgraph T1[Task 1: scar]
        M2[LGE-MRI] --> L[Cavity localizer] --> R[ROI: LGE, IIR,<br/>signed distance]
        R --> S1[ResEnc-L + ScarSurface] --> S2[Threshold 0.20<br/>on-wall filter 6 mm]
    end
    subgraph T3[Task 3: CT]
        CT[CT] --> E[ResEnc-L + STU-Net-B] --> V[PV hysteresis<br/>0.50 / 0.25]
    end

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📖 Contents

• 🧠 Method
• 🏆 Results
• ⛏️ Installation
• 🧩 Pretrained Checkpoints
• ⚡ Inference
• 🐳 Docker
• 🏋️ Training
• 🗂️ Repository Structure
• 📝 Citation
• 📚 Acknowledgments
• 📨 Contact

🧠 Method

SCALA uses three task-specific residual-encoder nnU-Net pipelines. They share one training recipe and one deployment path but no parameters.

• Scar cascade (Task 1). An out-of-fold cavity prediction localizes the atrium. The scar network sees a cropped ROI with three channels: the LGE image, the image intensity ratio (IIR, LGE divided by the blood-pool mean), and the signed distance to the endocardial surface. Two members are averaged: a plain ResEnc-L and a ResEnc-L trained with an extra wall-weighted soft-Dice term. An on-wall filter then removes components more than 6 mm from the cavity.
• Self-training on the nested labels. Scar: 40 LGE scans that have a cavity label but no scar label are pseudo-labeled by a five-fold teacher. CT: 99 of the 100 unlabeled whole-chest CTs are pseudo-labeled and cropped to the LA complex. Pseudo-labeled cases only ever enter the training folds.
• Heterogeneous ensembles. Cavity: ResEnc-L + MedNeXt-M. CT: ResEnc-L + STU-Net-B, fine-tuned from TotalSegmentator weights.
• Structure-specific decision rules. Cavity: connected-component cleanup. CT: double-threshold pulmonary-vein hysteresis (0.50/0.25), keeping only vein branches that touch the LA body. Scar: probability threshold 0.20.
• Acquisition-grid correction. The scar models are planned at 2.5 mm through-plane, but the test images are 1 mm isotropic. We predict at several sub-slice z offsets (the number is set by the spacing ratio) and take the voxel-wise median.

🏆 Results

	Scar (Task 1)	Cavity (Task 2)	CT (Task 3)
5-fold cross-validation	surface Dice 0.722	Dice 0.925	mean Dice 0.961
Hidden test set (organizers)	G-DSC 0.363	Dice 0.830, HD 22.55 mm	Dice 0.954, HD 9.33 mm

Fig. 2. (a) Dice contributed by each component against the configuration above it, with each task’s baseline and deployed score in the legend. (b) Surface scar Dice against decision threshold on 15 out-of-fold cases: the evaluation grid costs 0.022 at the deployed threshold and offset voting returns 0.014.

⛏️ Installation

git clone https://github.com/adinathdukre/SCALA.git
cd SCALA
pip install -e .

Note

Tested with nnU-Net v2.8.0 and dynamic_network_architectures 0.4.4; PyTorch 2.12 for training and PyTorch 2.8 in the Docker image.

Custom trainers are loaded through nnU-Net's nnUNet_extTrainer hook, so the nnU-Net installation is never modified. scripts/env.sh sets this variable and the nnU-Net paths. Edit the /path/to/... defaults in it, or export SCALA_DATA, SCALA_WORK, nnUNet_raw, nnUNet_preprocessed and nnUNet_results first:

source scripts/env.sh

🧩 Pretrained Checkpoints

All checkpoints are on Hugging Face: adidukre/SCALA

huggingface-cli download adidukre/SCALA --local-dir /path/to/checkpoints

Folder	Task	Training data	Trainer
cavity_resenc	1, 2	Dataset501 (130 LGE-MRI)	nnUNetTrainer (ResEnc-L)
cavity_mednext	2	Dataset501	nnUNetTrainerMedNeXt
scar_resenc	1	Dataset512 (60 labeled + 40 pseudo-labeled)	nnUNetTrainer (ResEnc-L)
scar_surface	1	Dataset512	nnUNetTrainerScarSurface
ct_resenc	3	Dataset522 (50 labeled + 99 pseudo-labeled)	nnUNetTrainer (ResEnc-L)
ct_stunet	3	Dataset522	STUNetTrainer_base_ft

Each folder is a standard nnU-Net result folder: plans.json, dataset.json, and fold_0 to fold_4, each holding checkpoint_best.pth.

⚡ Inference

python -m scala.predict --task task1 -i /path/to/input -o /path/to/output -m /path/to/checkpoints

Argument	Meaning
--task	task1 (scar), task2 (cavity) or task3 (CT)
-i	the challenge layout (input/taskN/<case>/<image>.nii.gz), a folder of case subfolders, or a flat folder of .nii.gz images
--folds	default 0,1,2,3,4 (all five)
--tta	mirror TTA, off in our submission
--device	e.g. cpu

Output follows the challenge naming (<case>/<id>_pred.nii.gz), on the input grid, as uint8. Task 1 and Task 2 write binary masks; Task 3 writes labels {0: background, 1: LA, 2: LAA, 3: PV}.

Tip

Task 1 is end-to-end: the cavity is predicted inside the pipeline, so only the LGE image is needed.

🐳 Docker

bash docker/build.sh task1 /path/to/checkpoints scala-task1
docker run --rm --gpus all -v /path/to/input:/input:ro -v /path/to/output:/output scala-task1

• The build copies only the members the task needs.
• The build runs a self-test that loads every model, so a broken image fails at build time.
• The container falls back to single-process preprocessing when /dev/shm is small, so it also works with Docker's default 64 MB.

🏋️ Training

Place the challenge training data under $SCALA_DATA, which should contain the three released task folders. Then run the stages below in order. In every nnUNetv2_train loop, F runs over folds 0 to 4.

1. Convert the data and write the patient-level splits (cavity and scar share the same splits)

source scripts/env.sh
python -m scala.data.convert --task all
nnUNetv2_plan_and_preprocess -d 501 521 -pl nnUNetPlannerResEncL -c 3d_fullres --verify_dataset_integrity
python -m scala.data.splits --dataset cavity
python -m scala.data.splits --dataset cardiac

2. Train the cavity models (Task 2, and the Task 1 localizer)

nnUNetv2_train 501 3d_fullres F -p nnUNetResEncUNetLPlans
nnUNetv2_train 501 3d_fullres F -p nnUNetResEncUNetLPlans -tr nnUNetTrainerMedNeXt

3. Build the scar dataset from out-of-fold cavity predictions, then train the scar teacher

python -m scala.data.cavity_oof
python -m scala.data.scar_cavity_map
python -m scala.data.build_scar_dataset
nnUNetv2_plan_and_preprocess -d 511 -pl nnUNetPlannerResEncL -c 3d_fullres --verify_dataset_integrity
python -m scala.data.splits --dataset scar
nnUNetv2_train 511 3d_fullres F -p nnUNetResEncUNetLPlans

4. Scar self-training and the two scar members (Task 1)

python -m scala.semisup.scar_pseudo_label
nnUNetv2_plan_and_preprocess -d 512 -pl nnUNetPlannerResEncL -c 3d_fullres --verify_dataset_integrity
python -m scala.data.splits --dataset scar_ssl
nnUNetv2_train 512 3d_fullres F -p nnUNetResEncUNetLPlans
nnUNetv2_train 512 3d_fullres F -p nnUNetResEncUNetLPlans -tr nnUNetTrainerScarSurface

5. CT teacher, self-training and the two CT members (Task 3)

[!IMPORTANT] For the STU-Net member, first download the TotalSegmentator-pretrained STU-Net-B weights (base_ep4k.model) from the STU-Net repository.

nnUNetv2_train 521 3d_fullres F -p nnUNetResEncUNetLPlans
python -m scala.semisup.ct_pseudo_label
nnUNetv2_plan_and_preprocess -d 522 -pl nnUNetPlannerResEncL -c 3d_fullres --verify_dataset_integrity
python -m scala.data.splits --dataset cardiac_ssl
nnUNetv2_train 522 3d_fullres F -p nnUNetResEncUNetLPlans
python scripts/train_stunet.py 522 3d_fullres F -p nnUNetResEncUNetLPlans \
    -tr STUNetTrainer_base_ft -pretrained_weights /path/to/base_ep4k.model

6. Export slim checkpoints in the layout scala.predict expects

python scripts/export_checkpoints.py --out /path/to/checkpoints

Training schedule. We stopped each fold once its EMA pseudo-Dice had plateaued, then kept checkpoint_best.pth:

Members	Epochs per fold
Scar	101 to 151
Cavity	250 to 418 (MedNeXt: 250-epoch budget)
CT	155 to 443

All models were trained on a single 96 GB NVIDIA RTX PRO 6000 Blackwell GPU.

🗂️ Repository Structure

SCALA/
├── scala/
│   ├── config.py        # paths, dataset names, constants
│   ├── predict.py       # inference entry point (CLI and Docker)
│   ├── data/            # conversion, splits, IIR, signed distance, scar dataset builder
│   ├── semisup/         # scar and CT pseudo-labeling
│   ├── nnunet_ext/      # custom nnU-Net trainers (ScarSurface, MedNeXt, STU-Net)
│   ├── postprocess/     # cavity cleanup, CT vein hysteresis, scar on-wall filter
│   └── inference/       # predictors, scar cascade, ensemble pipelines
├── scripts/             # environment, STU-Net fine-tuning launcher, checkpoint export
└── docker/              # Dockerfile and build script

📝 Citation

If you find our paper and code useful in your research, please cite:

@inproceedings{rege2026scala,
  title={SCALA: Semi-supervised Cascade for Left Atrial Scar, Cavity, and Multi-Structure CT Segmentation},
  author={Rege, Atharva Atul and Dukre, Adinath Madhavrao and Shah, Sarth Santosh and Razzak, Imran},
  booktitle={CARE 2026: Comprehensive Analysis of REal-world medical images-heart and liver},
  year={2026},
  url={https://openreview.net/forum?id=WylqMXauzj}
}

📚 Acknowledgments

This work builds on:

• nnU-Net: the self-configuring segmentation framework behind every pipeline.
• MedNeXt: the architecture code in scala/nnunet_ext/mednext is adapted from this repository.
• STU-Net: the architecture in scala/nnunet_ext/stunet.py is adapted from this repository.

We thank the CARE 2026 organizers for the data and the evaluation.

📨 Contact

For questions or collaboration, please open an issue or reach out to Adinath Madhavrao Dukre.

Important

SCALA is intended for research only. It is not approved for clinical use and must not inform any diagnostic or treatment decision.