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ROIsGAN: A region guided generative adversarial framework for murine hippocampal subregion segmentation.

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Title: ROIsGAN: A region guided generative adversarial framework for murine hippocampal subregion segmentation.
Authors: Azim, Sayed Mehedi1 (AUTHOR) sayedmehedi.azim@rutgers.edu, Suoto, Christina2 (AUTHOR), Corbett, Brian1,2 (AUTHOR), Dehzangi, Iman1,3,4 (AUTHOR) i.dehzangi@rutgers.edu
Source: Expert Systems with Applications. Jul2026, Vol. 318, pN.PAG-N.PAG. 1p.
Subjects: Image segmentation, Generative adversarial networks, Stains & staining (Microscopy), Hippocampus development, Deep learning, Neurodegeneration
Abstract: 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 histological 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, particularly 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 either ΔFosB or GAD67, capturing structural, neuronal activity, and plasticity-associated information. Additionally, we propose ROIsGAN, a region-guided UNet-based generative adversarial network tailored for hippocampal subregion segmentation. By leveraging adversarial 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 outperforms conventional segmentation models, achieving performance gains ranging from 1 to 10% in Dice score and up to 11% in IoU, particularly under challenging staining conditions. Our work establishes foundational datasets and methods for automated hippocampal region segmentation, supporting accurate analysis of tissue images in neuroscience research and demonstrating potential applicability to larger-scale studies. Our generated datasets, proposed model as a standalone tool, and its corresponding source code are publicly available at: https://github.com/MehediAzim/ROIsGAN. [ABSTRACT FROM AUTHOR]
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 193682014
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: 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 histological 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, particularly 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 either ΔFosB or GAD67, capturing structural, neuronal activity, and plasticity-associated information. Additionally, we propose ROIsGAN, a region-guided UNet-based generative adversarial network tailored for hippocampal subregion segmentation. By leveraging adversarial 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 outperforms conventional segmentation models, achieving performance gains ranging from 1 to 10% in Dice score and up to 11% in IoU, particularly under challenging staining conditions. Our work establishes foundational datasets and methods for automated hippocampal region segmentation, supporting accurate analysis of tissue images in neuroscience research and demonstrating potential applicability to larger-scale studies. Our generated datasets, proposed model as a standalone tool, and its corresponding source code are publicly available at: https://github.com/MehediAzim/ROIsGAN. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.eswa.2026.132009
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        Text: English
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      – SubjectFull: Image segmentation
        Type: general
      – SubjectFull: Generative adversarial networks
        Type: general
      – SubjectFull: Stains & staining (Microscopy)
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      – SubjectFull: Hippocampus development
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      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Neurodegeneration
        Type: general
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      – TitleFull: ROIsGAN: A region guided generative adversarial framework for murine hippocampal subregion segmentation.
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            NameFull: Azim, Sayed Mehedi
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              Text: Jul2026
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              Y: 2026
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