Quantitative Biomedical Research Center, Peter O'Donnell Jr. School of Public Health, UT Southwestern Medical Center, Dallas, Texas.
Department of Pathology, UT Southwestern Medical Center, Dallas, Texas.
Mod Pathol. 2023 Aug;36(8):100196. doi: 10.1016/j.modpat.2023.100196. Epub 2023 Apr 24.
Microscopic examination of pathology slides is essential to disease diagnosis and biomedical research. However, traditional manual examination of tissue slides is laborious and subjective. Tumor whole-slide image (WSI) scanning is becoming part of routine clinical procedures and produces massive data that capture tumor histologic details at high resolution. Furthermore, the rapid development of deep learning algorithms has significantly increased the efficiency and accuracy of pathology image analysis. In light of this progress, digital pathology is fast becoming a powerful tool to assist pathologists. Studying tumor tissue and its surrounding microenvironment provides critical insight into tumor initiation, progression, metastasis, and potential therapeutic targets. Nucleus segmentation and classification are critical to pathology image analysis, especially in characterizing and quantifying the tumor microenvironment (TME). Computational algorithms have been developed for nucleus segmentation and TME quantification within image patches. However, existing algorithms are computationally intensive and time consuming for WSI analysis. This study presents Histology-based Detection using Yolo (HD-Yolo), a new method that significantly accelerates nucleus segmentation and TME quantification. We demonstrate that HD-Yolo outperforms existing WSI analysis methods in nucleus detection, classification accuracy, and computation time. We validated the advantages of the system on 3 different tissue types: lung cancer, liver cancer, and breast cancer. For breast cancer, nucleus features by HD-Yolo were more prognostically significant than both the estrogen receptor status by immunohistochemistry and the progesterone receptor status by immunohistochemistry. The WSI analysis pipeline and a real-time nucleus segmentation viewer are available at https://github.com/impromptuRong/hd_wsi.
显微镜检查病理学切片对于疾病诊断和生物医学研究至关重要。然而，传统的组织切片手动检查既费力又主观。肿瘤全切片图像（WSI）扫描正成为常规临床程序的一部分，产生的大量数据以高分辨率捕获肿瘤组织学细节。此外，深度学习算法的快速发展极大地提高了病理图像分析的效率和准确性。有鉴于此，数字病理学正在迅速成为辅助病理学家的有力工具。研究肿瘤组织及其周围的微观环境为了解肿瘤的发生、进展、转移和潜在的治疗靶点提供了关键的洞察力。核分割和分类对于病理图像分析至关重要，特别是在表征和量化肿瘤微环境（TME）方面。已经开发了用于图像斑块内核分割和 TME 量化的计算算法。然而，现有的算法对于 WSI 分析来说计算量很大且耗时。本研究提出了基于组织学的 Yolo 检测（HD-Yolo），这是一种显著加速核分割和 TME 量化的新方法。我们证明了 HD-Yolo 在核检测、分类准确性和计算时间方面优于现有的 WSI 分析方法。我们在 3 种不同的组织类型（肺癌、肝癌和乳腺癌）上验证了该系统的优势。对于乳腺癌，HD-Yolo 的核特征比免疫组织化学检测的雌激素受体状态和免疫组织化学检测的孕激素受体状态更具有预后意义。WSI 分析管道和实时核分割查看器可在 https://github.com/impromptuRong/hd_wsi 上获得。
