Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation

TL;DR

This paper introduces a novel position-prior clustering-based self-attention module (PCAM) that enhances knee cartilage segmentation accuracy by capturing long-range dependencies and ensuring intra-class consistency, demonstrated through experiments on a medical dataset.

Contribution

The paper proposes a new PCAM module that improves cartilage segmentation by integrating position-prior clustering with self-attention to address limited receptive fields in CNNs.

Findings

Significant improvement in segmentation accuracy with PCAM integration.

Enhanced intra-class consistency and continuity in segmentation results.

Potential applicability of PCAM in various medical image segmentation tasks.

Abstract

The morphological changes in knee cartilage (especially femoral and tibial cartilages) are closely related to the progression of knee osteoarthritis, which is expressed by magnetic resonance (MR) images and assessed on the cartilage segmentation results. Thus, it is necessary to propose an effective automatic cartilage segmentation model for longitudinal research on osteoarthritis. In this research, to relieve the problem of inaccurate discontinuous segmentation caused by the limited receptive field in convolutional neural networks, we proposed a novel position-prior clustering-based self-attention module (PCAM). In PCAM, long-range dependency between each class center and feature point is captured by self-attention allowing contextual information re-allocated to strengthen the relative features and ensure the continuity of segmentation result. The clutsering-based method is used to estimate class centers, which fosters intra-class consistency and further improves the accuracy of segmentation results. The position-prior excludes the false positives from side-output and makes center estimation more precise. Sufficient experiments are conducted on OAI-ZIB dataset. The experimental results show that the segmentation performance of combination of segmentation network and PCAM obtains an evident improvement compared to original model, which proves the potential application of PCAM in medical segmentation tasks. The source code is publicly available from link: https://github.com/LeongDong/PCAMNet