Regional Achilles Tendon Segmentation for Quantitative Magnetic Resonance Imaging at 1.5 Tesla

Changes in the Achilles tendon composition are associated with an increased risk of tendinopathy which is common in middle-aged overweight patients and is one of the most common sports injuries. However, measuring and quantifying such properties is a challenging task. The purpose of this paper is to introduce an end-to-end pipeline for segmenting Achilles tendon using deep convolutional neural networks and automatic segmentation into three segments. Our model shows promising results outperforming state-of-the-art approaches (Dice 90.6% and Jaccard 84.0%). This is one of the key steps for short and long T2* value analysis from 1.5T data.