ROIsGAN: A Region Guided Generative Adversarial Framework for Murine Hippocampal Subregion Segmentation

-- The hippocampus, a critical brain structure involved in memory processing and various neurodegenerative and psychiatric disorders, comprises three key subregions: the dentate gyrus (DG), Cornu Ammonis 1 (CA1), and Cornu Ammonis 3 (CA3). Accurate segmentation of these subregions from histol ogical tissue images is essential for advancing our understanding of disease mechanisms, developmental dynamics, and therapeutic interventions. However, no existing methods address the automated segmentation of hippocampal subregions from tissue images, pa rticularly from immunohistochemistry (IHC) images. To bridge this gap, we introduce a novel set of four comprehensive murine hippocampal IHC datasets featuring distinct staining modalities: cFos, NeuN, and multiplexed stains combining cFos, NeuN, and eithe r ΔFosB or GAD 67, capturing structural, neuronal activity, and plasticity associated information. Additionally, we propose ROIsGAN, a region - guided U - Net - based generative adversarial network tailored for hippocampal subregion segmentation. By leveraging ad versarial learning, ROIsGAN enhances boundary delineation and structural detail refinement through a novel region guided discriminator loss combining Dice and binary cross - entropy loss. Evaluated across DG, CA1, and CA3 subregions, ROIsGAN consistently out performs conventional segmentation models, achieving performance gains ranging from 1 - 10% in Dice score and up to 11% in Intersection over Union (IoU), particularly under challenging staining conditions. Our work establishes foundational datasets and metho ds for automated hippocampal segmentation, enabling scalable, high - precision analysis of tissue images in neuroscience research. I. INTRODUCTION The hippocampus is one of the most extensively studied areas in the brain because of its significant functional role in memory processing, its remarkable plasticity, and its involvement in This paper is submitted for review on May 13, 2025. Sayed Mehedi Azim is with the Center for Computational and Integrative Biology, Rutgers University, Camden, NJ 18103, USA (e - mail: sayedmehedi.azim@rutgers.edu).