Open Access

REVIEW

Deep Multi-Scale and Attention-Based Architectures for Semantic Segmentation in Biomedical Imaging

Majid Harouni^1,*, Vishakha Goyal¹, Gabrielle Feldman¹, Sam Michael², Ty C. Voss¹

1 Division of Preclinical Innovation, National Center for Advancing Translational Sciences (NCATS), National Institutes of Health, 9800 Medical Center Drive, Building B, Rockville, MD 20850, USA
2 Data, Automation, and Predictive Sciences (DAPS), Research Technologies, GSK, 2929 Walnut Street, Ste. 1700, Philadelphia, PA 19104, USA

* Corresponding Author: Majid Harouni. Email:

(This article belongs to the Special Issue: Multi-Modal Deep Learning for Advanced Medical Diagnostics)

Computers, Materials & Continua 2025, 85(1), 331-366. https://doi.org/10.32604/cmc.2025.067915

Received 16 May 2025; Accepted 16 July 2025; Issue published 29 August 2025

Abstract

Semantic segmentation plays a foundational role in biomedical image analysis, providing precise information about cellular, tissue, and organ structures in both biological and medical imaging modalities. Traditional approaches often fail in the face of challenges such as low contrast, morphological variability, and densely packed structures. Recent advancements in deep learning have transformed segmentation capabilities through the integration of fine-scale detail preservation, coarse-scale contextual modeling, and multi-scale feature fusion. This work provides a comprehensive analysis of state-of-the-art deep learning models, including U-Net variants, attention-based frameworks, and Transformer-integrated networks, highlighting innovations that improve accuracy, generalizability, and computational efficiency. Key architectural components such as convolution operations, shallow and deep blocks, skip connections, and hybrid encoders are examined for their roles in enhancing spatial representation and semantic consistency. We further discuss the importance of hierarchical and instance-aware segmentation and annotation in interpreting complex biological scenes and multiplexed medical images. By bridging methodological developments with diverse application domains, this paper outlines current trends and future directions for semantic segmentation, emphasizing its critical role in facilitating annotation, diagnosis, and discovery in biomedical research.

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Keywords

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