-- 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).

The surge in adoption of digital pathology has the potential to revolutionize medical diagnosis by allowing computerized analysis of tissue images (Pantanowitz 2010; Aljanabi 2012; Hanna2020). Central to this technology is the digitization of formalin-fixed, paraffin-embedded (FFPE) tissue sections mounted on glass slides. This process converts physical tissue samples into high-resolution, gigapixel digital images called whole slide images (WSIs) (Kumar2020; Evans2022). These WSI files contain detailed patterns of tissue morphology, enabling the application of computer-vision algorithms in diagnostic pathology. Pathologists can now analyze tissue images seamlessly on computer screens at various magnifications (Griffin2017). This shift from light microscopes to digital displays allows for easier visual inspection of anatomic clues that may indicate specific diseases.

Artificial intelligence (AI) can be trained to recognise whether a tissue image contains a tumour. However, exactly how it makes its decision has remained a mystery until now. A team from the Research Center for Protein Diagnostics (PRODI) at Ruhr-Universität Bochum is developing a new approach that will render an AI's decision transparent and thus trustworthy. The researchers led by Professor Axel Mosig describe the approach in the journal Medical Image Analysis. For the study, bioinformatics scientist Axel Mosig cooperated with Professor Andrea Tannapfel, head of the Institute of Pathology, oncologist Professor Anke Reinacher-Schick from the Ruhr-Universität's St. Josef Hospital, and biophysicist and PRODI founding director Professor Klaus Gerwert. The group developed a neural network, i.e. an AI, that can classify whether a tissue sample contains tumour or not.

Artificial intelligence (AI) can be trained to recognise whether a tissue image contains a tumour. However, exactly how it makes its decision has remained a mystery until now. A team from the Research Center for Protein Diagnostics (PRODI) at Ruhr-Universität Bochum is developing a new approach that will render an AI's decision transparent and thus trustworthy. The researchers led by Professor Axel Mosig describe the approach in the journal "Medical Image Analysis", published online on 24 August 2022. For the study, bioinformatics scientist Axel Mosig cooperated with Professor Andrea Tannapfel, head of the Institute of Pathology, oncologist Professor Anke Reinacher-Schick from the Ruhr-Universität's St. Josef Hospital, and biophysicist and PRODI founding director Professor Klaus Gerwert.

Application of deep learning in digital pathology shows promise on improving disease diagnosis and understanding. We present a deep generative model that learns to simulate high-fidelity cancer tissue images while mapping the real images onto an interpretable low dimensional latent space. The key to the model is an encoder trained by a previously developed generative adversarial network, PathologyGAN. We study the latent space using 249K images from two breast cancer cohorts. We find that the latent space encodes morphological characteristics of tissues (e.g. patterns of cancer, lymphocytes, and stromal cells). In addition, the latent space reveals distinctly enriched clusters of tissue architectures in the high-risk patient group.

We apply Generative Adversarial Networks (GANs) to the domain of digital pathology. Current machine learning research for digital pathology focuses on diagnosis, but we suggest a different approach and advocate that generative models could help to understand and find fundamental morphological characteristics of cancer tissue. In this paper, we develop a framework which allows GANs to capture key tissue features, and present a vision of how these could link cancer tissue and DNA in the future. To this end, we trained our model on breast cancer tissue from a medium size cohort of 526 patients, producing high fidelity images. We further study how a range of relevant GAN evaluation metrics perform on this task, and propose to evaluate synthetic images with clinically/pathologically meaningful features. Our results show that these models are able to capture key morphological characteristics that link with phenotype, such as survival time and Estrogen-receptor (ER) status. Using an Inception-V1 network as feature extraction, our models achieve a Fréchet Inception Distance (FID) of 18.4. We find that using pathology meaningful features on these metrics show consistent performance, with a FID of 8.21. Furthermore, we asked two expert pathologists to distinguish our generated images from real ones, finding no significant difference between them.