Intracranial Aneurysm Detection

A two-stage volumetric deep learning model that detects and localizes brain aneurysms in head CT and MRI scans. Silver medal in the RSNA Intracranial Aneurysm Detection competition on Kaggle.

Published

2025

Python

PyTorch

Lightning

Pandas

NumPy

OpenCV

Repository

Competition

Solution developed for the RSNA Intracranial Aneurysm Detection competition on Kaggle. End-to-end test AUROC: 78.4% (22nd of 1147, silver medal).

What this project does

  • Detects the presence and location of intracranial aneurysms from multimodal head scans (CTA, MRA, MRI T1 post-contrast, T2).
  • Trained on 4,400 real clinical tests; outputs 14 probabilities (13 per-artery + 1 global).
  • Evaluation metric: average of AUROCs across outputs with the competition’s weighting.

Approach in brief

  • Two stages to manage data scale and computational complexity while maintaining sensitivity:
    1. Circle of Willis localizer (Stage 1)
    2. Aneurysm detector (Stage 2)
  • Modified ResNet backbones with affine instance normalization to cope with strong modality shifts.
  • Cross-validated ensembles with test-time augmentation (horizontal flip).

Stage 1 – Circle of Willis localizer

  • Inputs: 2D projections (AIP, MIP, SDP) along XY/XZ/YZ → 9-channel image from a 256³ resampled volume; per-channel clipping (1st-99th percentiles) and z-norm.
  • Outputs: 3D bounding boxes (center and size in [0, 1]); targets derived from segmentation masks.
  • Model: ResNet-like with grouped convolutions and affine instance norm; 6-value linear head.
  • Augmentation: coupled random flips, scale, translation; intensity – brightness, contrast (per-channel), gamma, bias field, noise.
  • Training: Huber loss; Adam; cosine LR with warmup; EMA; 4-fold CV; deterministic.
  • Inference: 4-model ensemble + flip TTA; mean of 8 predictions.
  • Result: mean CV IoU = 76.6% (no TTA); 76.7% with TTA.

Stage 2 – Aneurysm detector

  • Inputs: z-normed 128³ resample of the Stage-1 RoI.
  • Outputs: 13 per-artery probabilities; global value computed as max of the 13; targets derived from localizers within the RoI.
  • Model: 3D ResNet-like with affine instance norm; sigmoid-activated linear head.
  • Augmentation: 3D random flips, scale, rotation (world-aware), translation; intensity – bias field, brightness, contrast (per-slice), gamma, noise.
  • Training: Focal loss; AdamW with weight decay; warmup then constant LR; EMA; small batches; mixed precision; 8-fold CV.
  • Inference: 8-model ensemble + flip TTA; mean of 16 probabilities.
  • Result: mean CV AUROC = 80.0% (no TTA); 80.2% with TTA.

Practical engineering notes

  • DICOM loading: custom Pydicom-based series reader that orients volumes to SPL and tracks voxel↔︎patient affine transforms.
  • Disk cache: HDF5 (h5py) with int16 quantization; optional Zstd compression (helpful on Kaggle’s 20 GB limit).
  • Data hygiene: excluded a small number of corrupted/transposed scans and a few broken segmentation masks; remaining masks corrected for flipping (detected via left/right artery centroid coordinate tests) and distant blobs (detected via DBSCAN).

Key experiments

  • Stage-2 targets: Using derived targets outperformed training on the original targets or masking disagreements in the loss. The model likely picks up partially visible aneurysms; it is nevertheless trained on derived targets for best AUROC.
  • Stage-2 batch size: Smaller batches generalized better. Batch size 8 (with LR scaled accordingly) improved validation AUROC by ~5% vs. 16, and was kept.
  • Stage-2 multi-crop TTA: Running inference on nine offset crops yielded marginal gain (<0.1% AUROC); excluded due to complexity vs benefit.
  • CT windowing/normalization: HU windowing, masking, multichannel windows, percentile clipping, and dataset-level z-norm did not help; per-channel/per-volume z-norm remained.
  • Augmentation library: TorchIO was CPU-bound and slow. A couple of intensity transforms were re-implemented in PyTorch for fast GPU augmentation.
  • Skull stripping: SynthStrip tended to over-strip inferior Circle of Willis; not used.
  • Effect of ensembling: +4% end-to-end test AUROC.

Quick start

  1. Download rsna-intracranial-aneurysm-detection.zip from Kaggle.
  2. Unzip to data/rsna-intracranial-aneurysm-detection.
  3. Create a virtualenv and install requirements.txt.
  4. Train Stage 1 (localizer): bin/ensemble/train_cowloc.sh → models/cowloc.
  5. Optional CV: bin/crossval.py cowloc -n 4.
  6. Generate RoIs: bin/pred_cowloc.py -n 4 → output.
  7. Train Stage 2 (detector): bin/ensemble/train_icadet.sh → models/icadet.
  8. Optional CV: bin/crossval.py icadet -n 8.
  9. Predict: bin/predict.py --nc 4 --ni 8 --ctta --itta <series_path> ....

Notes, errata, and future ideas

  • Stage-2 safety margin: intended +10% box margin was inadvertently disabled during training; config reflects trained setup.
  • Robust loading for hidden test: defaults applied for missing metadata and color→mono conversion improved test by ~2%. Only the training-time loader is released, for simplicity.
  • Possible extension: learn global output with a dedicated head (reported to help by others) instead of max over 13 values.

Acknowledgements

  • Python, NumPy, PyTorch, Pandas, Matplotlib, Jupyter, Linux
  • PyTorch Lightning – flexible training
  • Weights & Biases – experiment tracking
  • Click – CLI framework
  • h5py – caching layer
  • Pydicom – DICOM handling

Special thanks to Kaggle and RSNA for the competition and data.

For source code, please visit the project’s repository.