Gopakumar G, Jagannadh Veerendra Kalyan, Gorthi Sai Siva, Subrahmanyam Gorthi R K Sai
Department of Earth and Space Sciences, Indian Institute of Space Science and Technology, Thiruvananthapuram, Kerala, India.
Department of Instrumentation and Applied Physics, Indian Institute of Science, Bangalore, India.
J Microsc. 2016 Mar;261(3):307-19. doi: 10.1111/jmi.12335. Epub 2015 Oct 15.
Imaging flow cytometry is an emerging technology that combines the statistical power of flow cytometry with spatial and quantitative morphology of digital microscopy. It allows high-throughput imaging of cells with good spatial resolution, while they are in flow. This paper proposes a general framework for the processing/classification of cells imaged using imaging flow cytometer. Each cell is localized by finding an accurate cell contour. Then, features reflecting cell size, circularity and complexity are extracted for the classification using SVM. Unlike the conventional iterative, semi-automatic segmentation algorithms such as active contour, we propose a noniterative, fully automatic graph-based cell localization. In order to evaluate the performance of the proposed framework, we have successfully classified unstained label-free leukaemia cell-lines MOLT, K562 and HL60 from video streams captured using custom fabricated cost-effective microfluidics-based imaging flow cytometer. The proposed system is a significant development in the direction of building a cost-effective cell analysis platform that would facilitate affordable mass screening camps looking cellular morphology for disease diagnosis.
成像流式细胞术是一项新兴技术，它将流式细胞术的统计能力与数字显微镜的空间和定量形态学相结合。它能在细胞流动时，以良好的空间分辨率对细胞进行高通量成像。本文提出了一个用于处理/分类成像流式细胞仪所成像细胞的通用框架。通过找到精确的细胞轮廓来定位每个细胞。然后，提取反映细胞大小、圆形度和复杂度的特征，使用支持向量机进行分类。与传统的迭代式半自动分割算法（如活动轮廓法）不同，我们提出了一种基于非迭代、全自动图形的细胞定位方法。为了评估所提出框架的性能，我们已成功地从使用定制制造的、具有成本效益的基于微流控的成像流式细胞仪捕获的视频流中，对未染色的无标记白血病细胞系MOLT、K562和HL60进行了分类。所提出的系统是朝着构建一个具有成本效益的细胞分析平台方向迈出的重大进展，该平台将有助于开展经济实惠的大规模筛查活动，通过观察细胞形态进行疾病诊断。
