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Recent Advances in Biomedical Imaging Sensors and Processing

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Dear Colleagues,

The analysis of bioimages and their clinical-related extracted features are very useful in supporting physicians in the early detection, investigation, diagnosis, classification, management, and treatment of many pathological conditions and diseases.

Recent advances in sensor technology have allowed the development of many interesting solutions to extract and process clinical information and data useful to discover significant hidden patterns, aiming to define predictive algorithms, methodologies, and tools for the prediction of anomalous behavior, for the study and the classification of diseases.

Bioimaging sensors can furnish quantitative measurements to physicians that they can use in a decision support system for scientific hypotheses and medical diagnoses, treatment, and follow-up.

This Special Issue aims to present and report recent advances and trends concerning novel solutions and technologies in bioimaging sensors and processing, with applications in clinical practice.

Both contributions about the results of recent developments and applications and comprehensive review articles are welcome for submission.

Potential topics include but are not limited to:

Bioimage processing;
Bioimage sensors;
Image segmentation;
Clinical decision support systems;
Early disease detection;
Biomedical computer-aided applications.

Dr. Patrizia Vizza
Dr. Giuseppe Tradigo
Guest Editors

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Research

13 pages, 1686 KiB

Open AccessArticle

Characterization of Heart Diseases per Single Lead Using ECG Images and CNN-2D

by Lerina Aversano, Mario Luca Bernardi, Marta Cimitile, Debora Montano and Riccardo Pecori

Sensors 2024, 24(11), 3485; https://doi.org/10.3390/s24113485 - 28 May 2024

Viewed by 498

Abstract

Cardiopathy has become one of the predominant global causes of death. The timely identification of different types of heart diseases significantly diminishes mortality risk and enhances the efficacy of treatment. However, fast and efficient recognition necessitates continuous monitoring, encompassing not only specific clinical conditions but also diverse lifestyles. Consequently, an increasing number of studies are striving to automate and progress in the identification of different cardiopathies. Notably, the assessment of electrocardiograms (ECGs) is crucial, given that it serves as the initial diagnostic test for patients, proving to be both the simplest and the most cost-effective tool. This research employs a customized architecture of Convolutional Neural Network (CNN) to forecast heart diseases by analyzing the images of both three bands of electrodes and of each single electrode signal of the ECG derived from four distinct patient categories, representing three heart-related conditions as well as a spectrum of healthy controls. The analyses are conducted on a real dataset, providing noteworthy performance (recall greater than 80% for the majority of the considered diseases and sometimes even equal to 100%) as well as a certain degree of interpretability thanks to the understanding of the importance a band of electrodes or even a single ECG electrode can have in detecting a specific heart-related pathology. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)

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17 pages, 4430 KiB

Open AccessArticle

Hybrid Approach to Colony-Forming Unit Counting Problem Using Multi-Loss U-Net Reformulation

by Vilen Jumutc, Artjoms Suponenkovs, Andrey Bondarenko, Dmitrijs Bļizņuks and Alexey Lihachev

Sensors 2023, 23(19), 8337; https://doi.org/10.3390/s23198337 - 9 Oct 2023

Cited by 1 | Viewed by 1566

Abstract

Colony-Forming Unit (CFU) counting is a complex problem without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by researchers. However, U-Net remains the most frequently cited and used deep learning method in these domains. The latter approach provides a segmentation output map and requires an additional counting procedure to calculate unique segmented regions and detect microbial colonies. However, due to pixel-based targets, it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, this paper proposes a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. Firstly, a unique innovation lies in the multi-loss U-Net reformulation. An additional loss term is introduced in the bottleneck U-Net layer, focusing on the delivery of an auxiliary signal that indicates where to look for distinct CFUs. Secondly, the novel localization algorithm automatically incorporates an agar plate and its bezel into the CFU counting techniques. Finally, the proposition is further enhanced by the integration of a fully automated solution, which comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application directly receives images from the camera, processes them, and sends the segmentation results to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the deep learning model. Through extensive experimentation, the authors of this paper have found that all probed multi-loss U-Net architectures incorporated into the proposed hybrid approach consistently outperformed their single-loss counterparts, as well as other comparable models such as self-normalized density maps and YOLOv6, by at least 1% to 3% in mean absolute and symmetric mean absolute percentage errors. Further significant improvements were also reported through the means of the novel localization algorithm. This reaffirms the effectiveness of the proposed hybrid solution in addressing contemporary challenges of precise in vitro CFU counting. Full article

(This article belongs to the Special Issue Recent Advances in Biomedical Imaging Sensors and Processing)

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