Entropy and distance maps-guided segmentation of articular cartilage: data from the Osteoarthritis Initiative.

Abstract

Accurate segmentation of articular cartilage from MR images is crucial for quantitative investigation of pathoanatomical conditions such as osteoarthritis (OA). Recently, deep learning-based methods have made significant progress in hard tissue segmentation. However, it remains a challenge to develop accurate methods for automatic segmentation of articular cartilage. We propose a two-stage method for automatic segmentation of articular cartilage. At the first stage, nnU-Net is employed to get segmentation of both hard tissues and articular cartilage. Based on the initial segmentation, we compute distance maps as well as entropy maps, which encode the uncertainty information about the initial cartilage segmentation. At the second stage, both distance maps and entropy maps are concatenated to the original image. We then crop a sub-volume around the cartilage region based on the initial segmentation, which is used as the input to another nnU-Net for segmentation refinement. We designed and conducted comprehensive experiments on segmenting three different types of articular cartilage from two datasets, i.e., an in-house dataset consisting of 25 hip MR images and a publicly available dataset from Osteoarthritis Initiative (OAI). Our method achieved an average Dice similarity coefficient (DSC) of [Formula: see text] for the combined hip cartilage, [Formula: see text] for the femoral cartilage and [Formula: see text] for the tibial cartilage, respectively. In summary, we developed a new approach for automatic segmentation of articular cartilage from MR images. Comprehensive experiments conducted on segmenting articular cartilage of the knee and hip joints demonstrated the efficacy of the present approach. Our method achieved equivalent or better results than the state-of-the-art methods.