Fully automated estimation of the mean linear intercept in histopathology images of mouse lung tissue

作者：Sina Salsabili, Marissa A. Lithopoulos, Shreyas Sreeraman, Arul Vadivel, Bernard Thébaud, Adrian D. C. Chan, Eranga Ukwatta · 发表于：Journal of medical imaging · 年份：2021 · DOI：10.1117/1.jmi.8.2.027501 · 被引用次数：16 · 研究领域：AI in cancer detection、Radiomics and Machine Learning in Medical Imaging、Lung Cancer Diagnosis and Treatment

Purpose: The mean linear intercept (MLI) score is a common metric for quantification of injury in lung histopathology images. The automated estimation of the MLI score is a challenging task because it requires accurate segmentation of different biological components of the lung tissue. Therefore, the most widely used approaches for MLI quantification are based on manual/semi-automated assessment of lung histopathology images, which can be expensive and time-consuming. We describe a fully automated pipeline for MLI estimation, which is capable of producing results comparable to human raters. Approach: We use a convolutional neural network based on U-Net architecture to segment the diagnostically relevant tissue segments in the whole slide images (WSI) of the mouse lung tissue. The proposed method extracts multiple field-of-view (FOV) images from the tissue segments and screen the FOV images, rejecting images based on presence of certain biological structures (i.e., blood vessels and bronchi). We used color slicing and region growing for segmentation of different biological structures in each FOV image. Results: The proposed method was tested on ten WSIs from mice and compared against the scores provided by three human raters. In segmenting the relevant tissue segments, our method obtained a mean accuracy, Dice coefficient, and Hausdorff distance of 98.34%, 98.22%, and 109.68 μm, respectively. Our proposed method yields a mean precision, recall, and F1-score of 93.37%, 83.47%, ...