vertebral-alignment-analysis-platform

Localization and Bayesian Segmentation of Vertebrae

cd ./landmark_segmentation_uncertainty

The approach and implementation of the vertebrae localization and segmentation is based on the paper and its project:

• Coarse to Fine Vertebrae Localization and Segmentation with SpatialConfiguration-Net and U-Net
• MedicalDataAugmentationTool-VerSe

We also used the following toolkits:

• MedicalDataAugmentationTool
• volume-renderer

Method

Vertebrae localization and segmentation are performed by a three-stage fully automatic approach:

1. Spine localization,
2. Vertebrae localization
3. Binary segmentation of each localized and identified vertebrae

Additionally, the segmentation network in the final stage is reformed to the Bayesian 3D U-Net to estimate segmentation uncertainty by multiple test-time MC dropout samples

Models

The models trained by the dataset VerSe 2019 in the repo are from the project MedicalDataAugmentationTool-VerSe.

We also released the new models: (To be updated)

• Spine localization and vertebrae localization: trained by 1180 CT cases (1000 cases from J-MID and 80 cases from VerSe 2019)
• Vertebrae Bayesian segmentation: trained by 180 CT cases (100 cases from J-MID and 80 cases from VerSe 2019)

Requirement

The requirment.txt is provided in the repo.

pip install -r requirements.txt

Inference

Make a new directory named img and put your CT images in it.

cd ./test
mkdir ./img

The added environment variable of the MedicalDataAugmentationTool needs to be revised according to you local path in the following files.

./inference/main_spine_localization.py
./inference/main_vertebrae_localization.py
./inference/main_vertebrae_segmentation.py

Run the bash script for the inference.

bash inference_verse19_models.sh

Visualization

Run the bash script for the visuliazatrion of results.

bash visualization_all.sh

Examples of visuliazation

Spine Alignment Analysis

cd ./alignment_analysis

To be updated.