Lv Meng, Chen Tianhong, Yang Yue, Tu Tianqi, Zhang Nianrong, Li Wenge, Li Wei
School of Information and Electronics, Beijing Institute of Technology, and Beijing Key Laboratory of Fractional Signals and Systems, Beijing 100081, China.
Department of Kidney Disease, China-Japan Friendship Hospital, Beijing 100029, China.
Biomed Opt Express. 2021 Apr 28;12(5):2968-2978. doi: 10.1364/BOE.421345. eCollection 2021 May 1.
Optical kidney biopsy, serological examination, and clinical symptoms are the main methods for membranous nephropathy (MN) diagnosis. However, false positives and undetectable biochemical components in the results of optical inspections lead to unsatisfactory diagnostic sensitivity and pose obstacles to pathogenic mechanism analysis. In order to reveal detailed component information of immune complexes of MN, microscopic hyperspectral imaging technology is employed to establish a hyperspectral database of 68 patients with two types of MN. Based on the characteristic of the medical HSI, a novel framework of tensor patch-based discriminative linear regression (TDLR) is proposed for MN classification. Experimental results show that the classification accuracy of the proposed model for MN identification is 98.77%. The combination of tensor-based classifiers and hyperspectral data analysis provides new ideas for the research of kidney pathology, which has potential clinical value for the automatic diagnosis of MN.
光学肾活检、血清学检查和临床症状是膜性肾病（MN）诊断的主要方法。然而，光学检查结果中的假阳性和无法检测到的生化成分导致诊断敏感性不尽人意，并对致病机制分析造成障碍。为了揭示MN免疫复合物的详细成分信息，采用显微高光谱成像技术建立了68例两种类型MN患者的高光谱数据库。基于医学高光谱成像（HSI）的特点，提出了一种基于张量补丁的判别线性回归（TDLR）新框架用于MN分类。实验结果表明，所提出模型用于MN识别的分类准确率为98.77%。基于张量的分类器与高光谱数据分析相结合，为肾脏病理学研究提供了新思路，对MN的自动诊断具有潜在的临床价值。
