论文标题
基于位置优先聚类的基于膝盖软骨的自我发项模块
Position-prior Clustering-based Self-attention Module for Knee Cartilage Segmentation
论文作者
论文摘要
膝盖软骨（尤其是股骨和胫骨软骨）的形态变化与膝关节骨关节炎的进展密切相关，膝关节骨关节炎的进展是通过磁共振（MR）图像表达的，并且对软骨分割结果进行了评估。因此，有必要提出一个有效的自动软骨分割模型，以进行骨关节炎的纵向研究。在这项研究中，为了减轻卷积神经网络中受体有限的接收场导致不准确分割的问题，我们提出了一个新型的基于位置优先聚类的自我发场模块（PCAM）。在PCAM中，通过自我注意力捕获了每个类中心和特征点之间的远距离依赖性，允许重新分配上下文信息以增强相对特征并确保分割结果的连续性。基于浮雕的方法用于估计班级中心，该中心促进了类内的一致性，并进一步提高了分割结果的准确性。位置优先排除误报，使中心估计更加精确。在OAI-ZIB数据集上进行了足够的实验。实验结果表明，与原始模型相比，分割网络和PCAM的组合的分割性能获得了明显的改进，这证明了PCAM在医疗分割任务中的潜在应用。源代码可从链接公开获得：https：//github.com/leongdong/pcamnet
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
