A Deep Learning Framework for Biomarker Segmentation and Classification in Traumatic Brain Injury

Abstract

Traumatic brain injury (TBI) triggers widespread biomarker activation, including astrocytic markers such as glial fibrillary acidic protein (GFAP) and microglia markers such as ionized calcium-binding adapter molecule 1 (IBA1). Quantifying and analyzing these biomarkers are critical for understanding injury impact; however, current methods are labor-intensive and time-consuming. In this study, we propose an automated deep learning framework for dual-biomarker segmentation and TBI classification using GFAP and IBA1 immunofluorescent images. Four U-Net variants: Baseline U-Net, U-Net++, MANet, and LinkNet were trained for segmentation. Three classification models, ResNet50, Swin_T, and MaxViT, were trained to distinguish TBI from control images under single- and dual-biomarker conditions. The baseline U-Net achieved the highest segmentation Dice score for GFAP (0.9259), while the U-Net++ achieved the highest Dice score for IBA1 (0.9676). Trained segmentation models demonstrated significantly better performance compared to QuPath alternatives. While GFAP alone supported high classification accuracy, IBA1 alone was less effective. Multimodal fusion of GFAP and IBA1 significantly improved classification performance across all models, with Swin_T achieving the highest overall accuracy (0.9489), and ResNet50 achieving the highest F1-score (0.9499). These findings demonstrate that integrating complementary biomarkers enhances automated TBI classification, and deep learning offers a robust alternative to manual analysis for immunofluorescent brain injury imaging. This framework is scalable to additional biomarkers and injury models, offering a reproducible approach to accelerate biomarker research.