Topic Editors

Dr. Surbhi Bhatia Khan
School of Science, Engineering and Environment, University of Salford, Manchester M5 4WT, UK
Prof. Dr. Mo Saraee
School of Science, Engineering and Environment, University of Salford-Manchester, Salford M5 4NT, UK

Signal Analysis and Biomedical Imaging for Precision Medicine

Abstract submission deadline
closed (30 June 2025)
Manuscript submission deadline
31 August 2025
Viewed by
1707

Topic Information

Dear Colleagues,

This Topic highlights the potential of signal analysis and biomedical imaging to revolutionize healthcare by enabling rapid and accurate diagnoses through meticulously examining biological signals and medical images. Signal analysis uncovers hidden patterns in functional data, such as heartbeats or brainwaves, aiding in early disease detection and monitoring. Biomedical imaging, encompassing techniques like MRI, CT, and ultrasound, provides visual representations of internal structures and processes. Combining these two imprtant tools will empower healthcare professionals to make efficient decisions, with improved patient outcomes. The Topic suggests a focus on practical applications of technology to address a significant global challenge, with a commitment to engineering for a better world. Featured work may involve collaborations between engineers, computer scientists, and medical professionals, thus fostering interdisciplinary research.

Dr. Surbhi Bhatia Khan
Prof. Dr. Mo Saraee
Topic Editors

Keywords

  • biomedical image processing
  • machine learning for biomedical applications
  • deep learning for medical informatics
  • personalized medicine
  • precision medicine
  • privacy-preserving AI for medical data
  • advances in signal analysis in healthcare
  • computer-aided design for telemedicine applications
  • biomedical image and signal retrieval using generative AI

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.5 5.5 2011 19.8 Days CHF 2400 Submit
Bioengineering
bioengineering
3.7 5.3 2014 19.2 Days CHF 2700 Submit
Diagnostics
diagnostics
3.3 5.9 2011 21 Days CHF 2600 Submit
Journal of Imaging
jimaging
3.3 6.7 2015 15.3 Days CHF 1800 Submit
Signals
signals
2.6 4.6 2020 22.9 Days CHF 1200 Submit

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Published Papers (1 paper)

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21 pages, 4707 KiB  
Article
A Real-Time Cell Image Segmentation Method Based on Multi-Scale Feature Fusion
by Xinyuan Zhang, Yang Zhang, Zihan Li, Yujiao Song, Shuhan Chen, Zhe Mao, Zhiyong Liu, Guanglan Liao and Lei Nie
Bioengineering 2025, 12(8), 843; https://doi.org/10.3390/bioengineering12080843 - 5 Aug 2025
Viewed by 157
Abstract
Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing [...] Read more.
Cell confluence and number are critical indicators for assessing cellular growth status, contributing to disease diagnosis and the development of targeted therapies. Accurate and efficient cell segmentation is essential for quantifying these indicators. However, current segmentation methodologies still encounter significant challenges in addressing multi-scale heterogeneity, poorly delineated boundaries under limited annotation, and the inherent trade-off between computational efficiency and segmentation accuracy. We propose an innovative network architecture. First, a preprocessing pipeline combining contrast-limited adaptive histogram equalization (CLAHE) and Gaussian blur is introduced to balance noise suppression and local contrast enhancement. Second, a bidirectional feature pyramid network (BiFPN) is incorporated, leveraging cross-scale feature calibration to enhance multi-scale cell recognition. Third, adaptive kernel convolution (AKConv) is developed to capture the heterogeneous spatial distribution of glioma stem cells (GSCs) through dynamic kernel deformation, improving boundary segmentation while reducing model complexity. Finally, a probability density-guided non-maximum suppression (Soft-NMS) algorithm is proposed to alleviate cell under-detection. Experimental results demonstrate that the model achieves 95.7% mAP50 (box) and 95% mAP50 (mask) on the GSCs dataset with an inference speed of 38 frames per second. Moreover, it simultaneously supports dual-modality output for cell confluence assessment and precise counting, providing a reliable automated tool for tumor microenvironment research. Full article
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