Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea.
Department of Physics, KAIST, Daejeon, Republic of Korea.
J Biophotonics. 2023 Aug;16(8):e202300067. doi: 10.1002/jbio.202300067. Epub 2023 Jun 5.
For patients with acute ischemic stroke, histological quantification of thrombus composition provides evidence for determining appropriate treatment. However, the traditional manual segmentation of stained thrombi is laborious and inconsistent. In this study, we propose a label-free method that combines optical diffraction tomography (ODT) and deep learning (DL) to automate the histological quantification process. The DL model classifies ODT image patches with 95% accuracy, and the collective prediction generates a whole-slide map of red blood cells and fibrin. The resulting whole-slide composition displays an average error of 1.1% and does not experience staining variability, facilitating faster analysis with reduced labor. The present approach will enable rapid and quantitative evaluation of blood clot composition, expediting the preclinical research and diagnosis of cardiovascular diseases.
对于急性缺血性脑卒中患者，血栓成分的组织学定量为确定合适的治疗方法提供了依据。然而，传统的染色血栓手动分割既繁琐又不一致。在这项研究中，我们提出了一种无标记的方法，结合光学衍射层析（ODT）和深度学习（DL）来实现组织学定量过程的自动化。DL 模型对 ODT 图像块的分类准确率达到 95%，集体预测生成了红细胞和纤维蛋白的全幻灯片图谱。得到的全幻灯片组成显示平均误差为 1.1%，并且不受染色变化的影响，从而可以更快地进行分析，减少工作量。该方法将能够快速定量评估血栓组成，加快心血管疾病的临床前研究和诊断。
