Effect of Color-Normalization on Deep Learning Segmentation Models for Tumor-Infiltrating Lymphocytes Scoring Using Breast Cancer Histopathology Images

Studies have shown that the increased presence of tumor-infiltrating lymphocytes (TILs) is associated with better long-term clinical outcomes and survival, which makes TILs a potentially useful quantitative biomarker. In clinics, pathologists' visual assessment of TILs in biopsies and surgical resections result in a quantitative score (TILs-score). The Tumor-infiltrating lymphocytes in breast cancer (TiGER) challenge is the first public challenge on automated TILs-scoring algorithms using whole slide images of hematoxylin and eosin-stained (H&E) slides of human epidermal growth factor receptor-2 positive (HER2+) and triple-negative breast cancer (TNBC) patients. We participated in the TiGER challenge and developed algorithms for tumor-stroma segmentation, TILs cell detection, and TILs-scoring. The whole slide images in this challenge are from three sources, each with apparent color variations. We hypothesized that color-normalization may improve the cross-source generalizability of our deep learning models. Here, we expand our initial work by implementing a color-normalization technique and investigate its effect on the performance of our segmentation model. We compare the segmentation performance before and after color-normalization by cross validating the models on the three datasets. Our results show a substantial increase in the performance of the segmentation model after color-normalization when trained and tested on different sources. This might potentially improve the model's generalizability and robustness when applied to the external sequestered test set from the TiGER challenge.