Automated segmentation of knee menisci from magnetic resonance images by using ATTU-Net: a pilot study on small datasets

We proposed a neural network model trained with a small amount of meniscus data (only 144 MR images) to improve the segmentation performance of CNNs, such as U-Net, by overcoming the challenges caused by surrounding tissues. We trained and tested the proposed model on 204 T2-weighted MR images of the knee from 181 patients. The trained model provided excellent segmentation performance for lateral menisci with a mean Dice similarity coefficient of 0.864 (range, 0.743-0.990; SD, ±0.077). The results were superior to those of contemporarily published meniscus segmentation methods based on CNNs.