IEEE J Biomed Health Inform. 2018 May;22(3):942-954. doi: 10.1109/JBHI.2017.2694344. Epub 2017 Apr 13.
Idiopathic inflammatory myopathy (IIM) is a common skeletal muscle disease that relates to weakness and inflammation of muscle. Early diagnosis and prognosis of different types of IIMs will guide the effective treatment. Interpretation of digitized images of the cross-section muscle biopsy, which is currently done manually, provides the most reliable diagnostic information. With the increasing volume of images, the management and manual interpretation of the digitized muscle images suffer from low efficiency and high interobserver variabilities. In order to address these problems, we propose the first complete framework of automatic IIM diagnosis system for the management and interpretation of digitized skeletal muscle histopathology images. The proposed framework consists of several key components: (1) Automatic cell segmentation, perimysium annotation, and nuclei detection; (2) histogram-based feature extraction and quantification; (3) content-based image retrieval to search and retrieve similar cases in the database for comparative study; and (4) majority voting-based classification to provide decision support for computer-aided clinical diagnosis. Experiments show that the proposed diagnosis system provides efficient and robust interpretation of the digitized muscle image and computer-aided diagnosis of IIM.
特发性炎性肌病（IIM）是一种常见的骨骼肌疾病，与肌肉无力和炎症有关。不同类型的特发性炎性肌病的早期诊断和预后将指导有效的治疗。目前，对肌肉活检的横截面数字化图像的解释是手动完成的，这提供了最可靠的诊断信息。随着图像数量的增加，数字化肌肉图像的管理和手动解释效率低下，观察者间差异较大。为了解决这些问题，我们提出了第一个用于管理和解释数字化骨骼肌组织病理学图像的自动特发性炎性肌病诊断系统的完整框架。该框架由几个关键组件组成：（1）自动细胞分割、肌束膜注释和核检测；（2）基于直方图的特征提取和量化；（3）基于内容的图像检索，以在数据库中搜索和检索类似病例进行比较研究；（4）基于多数投票的分类，为计算机辅助临床诊断提供决策支持。实验表明，所提出的诊断系统为数字化肌肉图像的高效和稳健解释以及特发性炎性肌病的计算机辅助诊断提供了支持。
