Signal Analysis and Biomedical Imaging for Precision Medicine

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

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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All Applied Sciences Bioengineering Diagnostics J. Imaging Signals

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21 pages, 4707 KB  

Open AccessArticle

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 520

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

(This article belongs to the Topic Signal Analysis and Biomedical Imaging for Precision Medicine)

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