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Bioengineering
Special Issues
Applications of Computational Modeling in Biomedical Image and Signal Processing
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Applications of Computational Modeling in Biomedical Image and Signal Processing

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".

Deadline for manuscript submissions: 15 May 2024 | Viewed by 7436

Special Issue Editors

Prof. Dr. Bin Zheng

E-Mail Website
Guest Editor
School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK 73019, USA
Interests: biomedical imaging; computer-aided diagnosis; electrical impedance spectroscopy; computerized cancer biomarkers and statistical prediction models
Dr. Xuxin Chen

E-Mail Website
Guest Editor
Department of Radiation Oncology, Emory University School of Medicine, Emory University, Atlanta, GA 30322, USA
Interests: medical imaging informatics; computer-aided diagnosis

Special Issue Information

Dear Colleagues,

With significant efforts in research and development in recent years, many advanced medical imaging modalities and bio-signal testing methods have emerged and been used in translational medical research projects and/or clinical practice. However, due to the heterogeneity and/or random noise of the acquired images and detected biosignals, methods of identifying and extracting quantitative, robust, and clinically relevant image or biosignal markers remain a major challenge. Thus, more research efforts are needed to develop and test new computational models that can more effectively process biomedical images and/or biosignals, aiming to compute robust and non-redundant features, and then to develop new novel image or biosignal markers or predictive models that can assist clinicians in diagnosing diseases and/or predicting disease prognosis more accurately.          

For this purpose, this Special Issue, entitled “Application of Computational Modeling in Biomedical Image and Signal Processing”, will focus on publishing original research papers and comprehensive review papers related to the development and application of novel computational models that can help facilitate the discovery of new quantitative, robust, and clinically relevant image or biosignal markers for disease detection, diagnosis, and prognosis. The topics of interest for this Special Issue include, but are not limited to, the following:

  1. Image or biosignal data standardization or normalization to improve robustness of deep learning models;
  2. Novel computational models for medical image filtering, segmentation, and feature selection;
  3. Novel computational models for biosignal filtering, normalization, and reduction of feature dimensionality;
  4. New methods to train and test deep learning models using relatively small numbers of medical data/samples;
  5. New methods or models to generate and apply synthetic data to improve accuracy and robustness of deep learning models in biomedical applications;
  6. Novel fusion methods or models to combine both medical image features and biosignal markers to improve accuracy of disease detection and diagnosis;
  7. Observer preference or performance studies to test or validate the potential clinical value or utility of computational models or quantitative image or biosignal markers;

Experience of applying computational model generated quantitative markers in clinical practice of disease diagnosis and treatment planning

Prof. Dr. Bin Zheng
Dr. Xuxin Chen
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • computational modeling
  • quantitative disease markers
  • medical image processing
  • image segmentation
  • biosignal test
  • biosignal process
  • feature selection
  • deep learning
  • computer-aided diagnosis
  • data standardization
  • synthetic data generation

Published Papers (5 papers)

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Research

12 pages, 2978 KiB  
Article
Evaluating the Effectiveness of 2D and 3D CT Image Features for Predicting Tumor Response to Chemotherapy
by Neman Abdoli, Ke Zhang, Patrik Gilley, Xuxin Chen, Youkabed Sadri, Theresa Thai, Lauren Dockery, Kathleen Moore, Robert Mannel and Yuchen Qiu
Bioengineering 2023, 10(11), 1334; https://doi.org/10.3390/bioengineering10111334 - 20 Nov 2023
Cited by 1 | Viewed by 1008
Abstract
Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This [...] Read more.
Background and Objective: 2D and 3D tumor features are widely used in a variety of medical image analysis tasks. However, for chemotherapy response prediction, the effectiveness between different kinds of 2D and 3D features are not comprehensively assessed, especially in ovarian-cancer-related applications. This investigation aims to accomplish such a comprehensive evaluation. Methods: For this purpose, CT images were collected retrospectively from 188 advanced-stage ovarian cancer patients. All the metastatic tumors that occurred in each patient were segmented and then processed by a set of six filters. Next, three categories of features, namely geometric, density, and texture features, were calculated from both the filtered results and the original segmented tumors, generating a total of 1403 and 1595 features for the 2D and 3D tumors, respectively. In addition to the conventional single-slice 2D and full-volume 3D tumor features, we also computed the incomplete-3D tumor features, which were achieved by sequentially adding one individual CT slice and calculating the corresponding features. Support vector machine (SVM)-based prediction models were developed and optimized for each feature set. Five-fold cross-validation was used to assess the performance of each individual model. Results: The results show that the 2D feature-based model achieved an AUC (area under the ROC curve (receiver operating characteristic)) of 0.84 ± 0.02. When adding more slices, the AUC first increased to reach the maximum and then gradually decreased to 0.86 ± 0.02. The maximum AUC was yielded when adding two adjacent slices, with a value of 0.91 ± 0.01. Conclusions: This initial result provides meaningful information for optimizing machine learning-based decision-making support tools in the future. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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19 pages, 1511 KiB  
Article
PosturAll: A Posture Assessment Software for Children
by Ana Beatriz Neves, Rodrigo Martins, Nuno Matela and Tiago Atalaia
Bioengineering 2023, 10(10), 1171; https://doi.org/10.3390/bioengineering10101171 - 08 Oct 2023
Viewed by 1569
Abstract
From an early age, people are exposed to risk factors that can lead to musculoskeletal disorders like low back pain, neck pain and scoliosis. Medical screenings at an early age might minimize their incidence. The study intends to improve a software that processes [...] Read more.
From an early age, people are exposed to risk factors that can lead to musculoskeletal disorders like low back pain, neck pain and scoliosis. Medical screenings at an early age might minimize their incidence. The study intends to improve a software that processes images of patients, using specific anatomical sites to obtain risk indicators for possible musculoskeletal problems. This project was divided into four phases. First, markers and body metrics were selected for the postural assessment. Second, the software’s capacity to detect the markers and run optimization tests was evaluated. Third, data were acquired from a population to validate the results using clinical software. Fourth, the classifiers’ performance with the acquired data was analyzed. Green markers with diameters of 20 mm were used to optimize the software. The postural assessment using different types of cameras was conducted via the blob detection method. In the optimization tests, the angle parameters were the most influenced parameters. The data acquired showed that the postural analysis results were statistically equivalent. For the classifiers, the study population had 16 subjects with no evidence of postural problems, 25 with mild evidence and 16 with moderate-to-severe evidence. In general, using a binary classification with the train/test split validation method provided better results. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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15 pages, 2382 KiB  
Article
Comparison of Mid-Infrared Handheld and Benchtop Spectrometers to Detect Staphylococcus epidermidis in Bone Grafts
by Richard Lindtner, Alexander Wurm, Katrin Kugel, Julia Kühn, David Putzer, Rohit Arora, Débora Cristina Coraça-Huber, Philipp Zelger, Michael Schirmer, Jovan Badzoka, Christoph Kappacher, Christian Wolfgang Huck and Johannes Dominikus Pallua
Bioengineering 2023, 10(9), 1018; https://doi.org/10.3390/bioengineering10091018 - 29 Aug 2023
Cited by 1 | Viewed by 1090
Abstract
Bone analyses using mid-infrared spectroscopy are gaining popularity, especially with handheld spectrometers that enable on-site testing as long as the data quality meets standards. In order to diagnose Staphylococcus epidermidis in human bone grafts, this study was carried out to compare the effectiveness [...] Read more.
Bone analyses using mid-infrared spectroscopy are gaining popularity, especially with handheld spectrometers that enable on-site testing as long as the data quality meets standards. In order to diagnose Staphylococcus epidermidis in human bone grafts, this study was carried out to compare the effectiveness of the Agilent 4300 Handheld Fourier-transform infrared with the Perkin Elmer Spectrum 100 attenuated-total-reflectance infrared spectroscopy benchtop instrument. The study analyzed 40 non-infected and 10 infected human bone samples with Staphylococcus epidermidis, collecting reflectance data between 650 cm−1 and 4000 cm−1, with a spectral resolution of 2 cm−1 (Agilent 4300 Handheld) and 0.5 cm−1 (Perkin Elmer Spectrum 100). The acquired spectral information was used for spectral and unsupervised classification, such as a principal component analysis. Both methods yielded significant results when using the recommended settings and data analysis strategies, detecting a loss in bone quality due to the infection. MIR spectroscopy provides a valuable diagnostic tool when there is a tissue shortage and time is of the essence. However, it is essential to conduct further research with larger sample sizes to verify its pros and cons thoroughly. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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19 pages, 8001 KiB  
Article
Uncertainty-Aware Convolutional Neural Network for Identifying Bilateral Opacities on Chest X-rays: A Tool to Aid Diagnosis of Acute Respiratory Distress Syndrome
by Mehak Arora, Carolyn M. Davis, Niraj R. Gowda, Dennis G. Foster, Angana Mondal, Craig M. Coopersmith and Rishikesan Kamaleswaran
Bioengineering 2023, 10(8), 946; https://doi.org/10.3390/bioengineering10080946 - 08 Aug 2023
Viewed by 1559
Abstract
Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine [...] Read more.
Acute Respiratory Distress Syndrome (ARDS) is a severe lung injury with high mortality, primarily characterized by bilateral pulmonary opacities on chest radiographs and hypoxemia. In this work, we trained a convolutional neural network (CNN) model that can reliably identify bilateral opacities on routine chest X-ray images of critically ill patients. We propose this model as a tool to generate predictive alerts for possible ARDS cases, enabling early diagnosis. Our team created a unique dataset of 7800 single-view chest-X-ray images labeled for the presence of bilateral or unilateral pulmonary opacities, or ‘equivocal’ images, by three blinded clinicians. We used a novel training technique that enables the CNN to explicitly predict the ‘equivocal’ class using an uncertainty-aware label smoothing loss. We achieved an Area under the Receiver Operating Characteristic Curve (AUROC) of 0.82 (95% CI: 0.80, 0.85), a precision of 0.75 (95% CI: 0.73, 0.78), and a sensitivity of 0.76 (95% CI: 0.73, 0.78) on the internal test set while achieving an (AUROC) of 0.84 (95% CI: 0.81, 0.86), a precision of 0.73 (95% CI: 0.63, 0.69), and a sensitivity of 0.73 (95% CI: 0.70, 0.75) on an external validation set. Further, our results show that this approach improves the model calibration and diagnostic odds ratio of the hypothesized alert tool, making it ideal for clinical decision support systems. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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20 pages, 7056 KiB  
Article
Direct Multi-Material Reconstruction via Iterative Proximal Adaptive Descent for Spectral CT Imaging
by Xiaohuan Yu, Ailong Cai, Ningning Liang, Shaoyu Wang, Zhizhong Zheng, Lei Li and Bin Yan
Bioengineering 2023, 10(4), 470; https://doi.org/10.3390/bioengineering10040470 - 12 Apr 2023
Viewed by 1150
Abstract
Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification [...] Read more.
Spectral computed tomography (spectral CT) is a promising medical imaging technology because of its ability to provide information on material characterization and quantification. However, with an increasing number of basis materials, the nonlinearity of measurements causes difficulty in decomposition. In addition, noise amplification and beam hardening further reduce image quality. Thus, improving the accuracy of material decomposition while suppressing noise is pivotal for spectral CT imaging. This paper proposes a one-step multi-material reconstruction model as well as an iterative proximal adaptive decent method. In this approach, a proximal step and a descent step with adaptive step size are designed under the forward–backward splitting framework. The convergence analysis of the algorithm is further discussed according to the convexity of the optimization objective function. For simulation experiments with different noise levels, the peak signal-to-noise ratio (PSNR) obtained by the proposed method increases approximately 23 dB, 14 dB, and 4 dB compared to those of other algorithms. Magnified areas of thorax data further demonstrated that the proposed method has a better ability to preserve details in tissues, bones, and lungs. Numerical experiments verify that the proposed method efficiently reconstructed the material maps, and reduced noise and beam hardening artifacts compared with the state-of-the-art methods. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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