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AnkleNet: ankle-specific model for chronic ankle instability diagnosis

Background

Chronic ankle instability (CAI), characterized by decreased ankle stability and recurrent injuries, occurs in around 40% of ankle sprain patients. The most severe complication of CAI is end-stage traumatic arthritis, which cannot be completely restored through surgical interventions. Therefore, prompt diagnosis and early intervention are essential.

Lateral collateral ligament injuries

AnkleNet

To facilitate the diagnosis of CAI, we developed a transformer-based model, named AnkleNet. It can detect the injuries of lateral and medial collateral ligaments simultaneously based on MRI, aiding classifying of CAI patients in a detailed way: Normal, LCAI, MCAI, and RCAI.

To train the model, follow the steps below.

Step 1

Preprocess your MRI and make a csv files of your (images label) pairs.

The demo csv files can be found in data/.

  • AxialPath: the image path of axial mri

  • CoronalPath: the image path of coronal mri

  • label1: lateral collateral ligament injury

  • label2: medial collateral ligament injury

Step 2

Modify your training configs.

The config templete can be cound in config/.

Step 3

Train the model. Run:

python main_train.py --opt config/anklenet.yaml

Acknowlegements