Automated Segmentation of Knee Menisci Using U-Net Deep Learning Model: Preliminary Results.

Objectives: The present study describes the initial findings of the detection and segmentation of the knee meniscus in magnetic resonance imaging (MRI) scans using the U-Net deep learning model. The primary goal was to develop a model that automatically identified and segmented the meniscus from the region of interest (ROI) in knee MRI scans.

Material and methods: The current study was conducted in two phases. Initially, a U-Net deep learning model was developed to automatically detect the meniscus using a training dataset comprising 104 knee MRI images. In the second phase, the model was fine-tuned with an additional 50 MRI scans featuring manually segmented images to segment the meniscus from the ROI accurately.

Results: After performing 14 training tests, the U-Net model achieved a detection accuracy of 0.91. The average Dice score for ROIs after training at 100 epochs was 0.7259. With training extended to 300 epochs, the Dice score improved to 0.7525. Finally, the model reached a Dice score of 0.7609 after 500 epochs.

Conclusions: The present study introduces a practical deep learning-based approach for segmenting the knee meniscus, which is validated against ground truth annotations from orthopedic surgeons. Despite challenges such as data scarcity and the need for sequence-specific optimization, our method demonstrates significant potential for advancing automated meniscus segmentation in clinical settings.