  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
