Park Han Sang, Rinehart Matthew T, Walzer Katelyn A, Chi Jen-Tsan Ashley, Wax Adam
Department of Biomedical Engineering, Duke University, Durham, North Carolina, United States of America.
Department of Molecular Genetics and Microbiology, Duke University, Durham, North Carolina, United States of America.
PLoS One. 2016 Sep 16;11(9):e0163045. doi: 10.1371/journal.pone.0163045. eCollection 2016.
Malaria detection through microscopic examination of stained blood smears is a diagnostic challenge that heavily relies on the expertise of trained microscopists. This paper presents an automated analysis method for detection and staging of red blood cells infected by the malaria parasite Plasmodium falciparum at trophozoite or schizont stage. Unlike previous efforts in this area, this study uses quantitative phase images of unstained cells. Erythrocytes are automatically segmented using thresholds of optical phase and refocused to enable quantitative comparison of phase images. Refocused images are analyzed to extract 23 morphological descriptors based on the phase information. While all individual descriptors are highly statistically different between infected and uninfected cells, each descriptor does not enable separation of populations at a level satisfactory for clinical utility. To improve the diagnostic capacity, we applied various machine learning techniques, including linear discriminant classification (LDC), logistic regression (LR), and k-nearest neighbor classification (NNC), to formulate algorithms that combine all of the calculated physical parameters to distinguish cells more effectively. Results show that LDC provides the highest accuracy of up to 99.7% in detecting schizont stage infected cells compared to uninfected RBCs. NNC showed slightly better accuracy (99.5%) than either LDC (99.0%) or LR (99.1%) for discriminating late trophozoites from uninfected RBCs. However, for early trophozoites, LDC produced the best accuracy of 98%. Discrimination of infection stage was less accurate, producing high specificity (99.8%) but only 45.0%-66.8% sensitivity with early trophozoites most often mistaken for late trophozoite or schizont stage and late trophozoite and schizont stage most often confused for each other. Overall, this methodology points to a significant clinical potential of using quantitative phase imaging to detect and stage malaria infection without staining or expert analysis.
通过对染色血涂片进行显微镜检查来检测疟疾是一项诊断挑战，这在很大程度上依赖于训练有素的显微镜检查人员的专业知识。本文提出了一种自动分析方法，用于检测和分期被恶性疟原虫感染处于滋养体或裂殖体阶段的红细胞。与该领域以前的工作不同，本研究使用未染色细胞的定量相衬图像。利用光学相位阈值对红细胞进行自动分割，并重新聚焦以实现相衬图像的定量比较。对重新聚焦的图像进行分析，以基于相位信息提取23个形态学描述符。虽然感染细胞和未感染细胞之间的所有单个描述符在统计学上都有显著差异，但每个描述符都不能在临床实用水平上实现群体分离。为了提高诊断能力，我们应用了各种机器学习技术，包括线性判别分类（LDC）、逻辑回归（LR）和k近邻分类（NNC），来制定算法，将所有计算出的物理参数结合起来，以更有效地区分细胞。结果表明，与未感染的红细胞相比，LDC在检测裂殖体阶段感染细胞方面的准确率最高，可达99.7%。在区分晚期滋养体与未感染的红细胞方面，NNC的准确率（99.5%）略高于LDC（99.0%）或LR（99.1%）。然而，对于早期滋养体，LDC的准确率最高，为98%。感染阶段的判别准确性较低，特异性较高（99.8%），但敏感性仅为45.0%-66.8%，早期滋养体最常被误认为晚期滋养体或裂殖体阶段，晚期滋养体和裂殖体阶段最常相互混淆。总体而言，该方法表明使用定量相衬成像在无需染色或专家分析的情况下检测和分期疟疾感染具有巨大的临床潜力。
