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 (2 papers)

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All Applied Sciences Bioengineering Diagnostics J. Imaging Signals

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15 pages, 15164 KB  

Open AccessArticle

An Innovative Model for Diagnosing Lesions in Coronary Angiography Imagery Using an Improved YOLOv4 Model

by Zhu Chen, Yajie Chen, Jiajia Si, Changhu Xiao, Xiaohan Liu, Chengming Wang, Fengling Chen and Yuan Guo

Bioengineering 2025, 12(11), 1241; https://doi.org/10.3390/bioengineering12111241 - 12 Nov 2025

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Abstract

Percutaneous coronary angiography remains the diagnostic gold standard for coronary artery disease. However, the complex and high-volume nature of the imaging data renders the clinical interpretation of coronary lesions a time-consuming, labor-intensive, and inherently subjective process. This retrospective study collected and preprocessed Coronary [...] Read more.

Percutaneous coronary angiography remains the diagnostic gold standard for coronary artery disease. However, the complex and high-volume nature of the imaging data renders the clinical interpretation of coronary lesions a time-consuming, labor-intensive, and inherently subjective process. This retrospective study collected and preprocessed Coronary artery angiography (CAG) image data from 408 patients with acute myocardial infarction (AMI). An improved YOLOv4 algorithm was developed, validated on standard VOC datasets, and subsequently calibrated via transfer learning on the CAG training set for automated lesion detection and classification. The model-derived lesion characteristics were then statistically correlated with the occurrence of Major Adverse Cardiovascular Events (MACEs) during patient follow-up. The improved model achieved a post-modification mean Average Precision (mAP) of 84.72% (95% CI: 83.44–85.99%) on the VOC dataset. For coronary lesion detection, the model yielded an overall mean Average Precision (mAP) of 55.01%. Importantly, lesion characteristics automatically detected by the model—specifically completely occluded lesions (Log-rank p = 0.003) and multibranching lesions (Log-rank p = 0.033)—demonstrated a significant association with the cumulative incidence of MACEs. The innovative, improved YOLOv4 model exhibits robust performance in effectively and accurately detecting and classifying coronary lesions within AMI patient angiography imagery. This study provides a valuable AI-assisted diagnostic tool and offers preliminary insights for long-term prognostic assessment by seamlessly integrating deep learning-derived anatomical features with MACEs prediction. Full article

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

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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

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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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