An integrated automated deep learning framework for annotating tumor-infiltrating lymphocytes in lung adenocarcinoma pathology.

Objective: Quantitative analysis of tumor-infiltrating lymphocytes (TILs) is crucial in computational pathology studies of lung adenocarcinoma. However, acquiring large-scale, fully annotated datasets remains a major obstacle for the supervised learning approaches that currently dominate high-precision modeling. To address this data bottleneck, we developed a fully automated pipeline for the precise annotation of tissue contours, tumor parenchyma, and lymphocytes in whole-slide images (WSIs). Methods: This study utilized WSI data from The Cancer Genome Atlas (TCGA) cohort, with comprehensive manual annotations performed by two pathologists using QuPath software, with all annotations subsequently reviewed by a third senior pathologist. The resulting training dataset comprised over 20,000 annotated units. These annotated data were used to train three core modules consisting of an OpenCV-based image processing pipeline for tissue contour detection, a lightweight U Results: The pipeline demonstrated robust and generalizable performance. For tissue contour detection, the OpenCV-based pipeline achieved a Dice coefficient of 90.90% on the test set. For the core learning-based tasks, the tumor parenchyma segmentation model achieved a Dice coefficient of 87.17% on the internal test set and maintained consistent accuracy on the external cohort, with Dice coefficients ranging from 0.8509 to 0.9178. In the particularly challenging task of lymphocyte detection, the YOLOv7-based model attained an F1-score of 78.84% and mAP@0.5 of 81.16% on the test set, with performance sustained on external data. Critically, the automated TILs quantifications showed excellent agreement with independent pathologist assessments (ICC >0.96). The implementation of optimized lightweight architectures enables the pipeline to serve as an accessible solution for large-scale WSIs analysis in computational pathology. Conclusion: This study has successfully developed a fully automated annotation pipeline for lung adenocarcinoma WSIs. By generating high-quality annotations of stromal TILs, this pipeline establishes a reliable data foundation for subsequent computational pathology research and facilitates the advancement of artificial intelligence applications in pathology.