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Bioengineering
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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: 31 October 2024 | Viewed by 15297

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

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Published Papers (11 papers)

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16 pages, 5420 KiB  
Article
A Comparative Analysis of U-Net and Vision Transformer Architectures in Semi-Supervised Prostate Zonal Segmentation
by Guantian Huang, Bixuan Xia, Haoming Zhuang, Bohan Yan, Cheng Wei, Shouliang Qi, Wei Qian and Dianning He
Bioengineering 2024, 11(9), 865; https://doi.org/10.3390/bioengineering11090865 - 26 Aug 2024
Viewed by 577
Abstract
The precise segmentation of different regions of the prostate is crucial in the diagnosis and treatment of prostate-related diseases. However, the scarcity of labeled prostate data poses a challenge for the accurate segmentation of its different regions. We perform the segmentation of different [...] Read more.
The precise segmentation of different regions of the prostate is crucial in the diagnosis and treatment of prostate-related diseases. However, the scarcity of labeled prostate data poses a challenge for the accurate segmentation of its different regions. We perform the segmentation of different regions of the prostate using U-Net- and Vision Transformer (ViT)-based architectures. We use five semi-supervised learning methods, including entropy minimization, cross pseudo-supervision, mean teacher, uncertainty-aware mean teacher (UAMT), and interpolation consistency training (ICT) to compare the results with the state-of-the-art prostate semi-supervised segmentation network uncertainty-aware temporal self-learning (UATS). The UAMT method improves the prostate segmentation accuracy and provides stable prostate region segmentation results. ICT plays a more stable role in the prostate region segmentation results, which provides strong support for the medical image segmentation task, and demonstrates the robustness of U-Net for medical image segmentation. UATS is still more applicable to the U-Net backbone and has a very significant effect on a positive prediction rate. However, the performance of ViT in combination with semi-supervision still requires further optimization. This comparative analysis applies various semi-supervised learning methods to prostate zonal segmentation. It guides future prostate segmentation developments and offers insights into utilizing limited labeled data in medical imaging. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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18 pages, 4149 KiB  
Article
Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach
by Ionela Manole, Alexandra-Irina Butacu, Raluca Nicoleta Bejan and George-Sorin Tiplica
Bioengineering 2024, 11(8), 810; https://doi.org/10.3390/bioengineering11080810 - 9 Aug 2024
Viewed by 601
Abstract
Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep learning application for computer-aided diagnosis in dermatology. Methods: Using [...] Read more.
Background: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep learning application for computer-aided diagnosis in dermatology. Methods: Using a custom model based on EfficientNetB3 and deep learning, we propose an approach for skin lesion classification that offers superior results with smaller, cheaper, and faster inference times compared to other models. The skin images dataset used for this research includes 8222 files selected from the authors’ collection and the ISIC 2019 archive, covering six dermatological conditions. Results: The model achieved 95.4% validation accuracy in four categories—melanoma, basal cell carcinoma, benign keratosis-like lesions, and melanocytic nevi—using an average of 1600 images per category. Adding two categories with fewer images (about 700 each)—squamous cell carcinoma and actinic keratoses—reduced the validation accuracy to 88.8%. The model maintained accuracy on new clinical test images taken under the same conditions as the training dataset. Conclusions: The custom model demonstrated excellent performance on the diverse skin lesions dataset, with significant potential for further enhancements. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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11 pages, 2686 KiB  
Article
Utilizing a Pathomics Biomarker to Predict the Effectiveness of Bevacizumab in Ovarian Cancer Treatment
by Patrik Gilley, Ke Zhang, Neman Abdoli, Youkabed Sadri, Laura Adhikari, Kar-Ming Fung and Yuchen Qiu
Bioengineering 2024, 11(7), 678; https://doi.org/10.3390/bioengineering11070678 - 3 Jul 2024
Viewed by 757
Abstract
The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information [...] Read more.
The purpose of this investigation is to develop and initially assess a quantitative image analysis scheme that utilizes histopathological images to predict the treatment effectiveness of bevacizumab therapy in ovarian cancer patients. As a widely accessible diagnostic tool, histopathological slides contain copious information regarding underlying tumor progression that is associated with tumor prognosis. However, this information cannot be readily identified by conventional visual examination. This study utilizes novel pathomics technology to quantify this meaningful information for treatment effectiveness prediction. Accordingly, a total of 9828 features were extracted from segmented tumor tissue, cell nuclei, and cell cytoplasm, which were categorized into geometric, intensity, texture, and subcellular structure features. Next, the best performing features were selected as the input for SVM (support vector machine)-based prediction models. These models were evaluated on an open dataset containing a total of 78 patients and 288 whole slides images. The results indicated that the sufficiently optimized, best-performing model yielded an area under the receiver operating characteristic (ROC) curve of 0.8312. When examining the best model’s confusion matrix, 37 and 25 cases were correctly predicted as responders and non-responders, respectively, achieving an overall accuracy of 0.7848. This investigation initially validated the feasibility of utilizing pathomics techniques to predict tumor responses to chemotherapy at an early stage. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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12 pages, 2065 KiB  
Article
Deep Learning-Based Automated Measurement of Murine Bone Length in Radiographs
by Ruichen Rong, Kristin Denton, Kevin W. Jin, Peiran Quan, Zhuoyu Wen, Julia Kozlitina, Stephen Lyon, Aileen Wang, Carol A. Wise, Bruce Beutler, Donghan M. Yang, Qiwei Li, Jonathan J. Rios and Guanghua Xiao
Bioengineering 2024, 11(7), 670; https://doi.org/10.3390/bioengineering11070670 - 1 Jul 2024
Viewed by 794
Abstract
Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that [...] Read more.
Genetic mouse models of skeletal abnormalities have demonstrated promise in the identification of phenotypes relevant to human skeletal diseases. Traditionally, phenotypes are assessed by manually examining radiographs, a tedious and potentially error-prone process. In response, this study developed a deep learning-based model that streamlines the measurement of murine bone lengths from radiographs in an accurate and reproducible manner. A bone detection and measurement pipeline utilizing the Keypoint R-CNN algorithm with an EfficientNet-B3 feature extraction backbone was developed to detect murine bone positions and measure their lengths. The pipeline was developed utilizing 94 X-ray images with expert annotations on the start and end position of each murine bone. The accuracy of our pipeline was evaluated on an independent dataset test with 592 images, and further validated on a previously published dataset of 21,300 mouse radiographs. The results showed that our model performed comparably to humans in measuring tibia and femur lengths (R2 > 0.92, p-value = 0) and significantly outperformed humans in measuring pelvic lengths in terms of precision and consistency. Furthermore, the model improved the precision and consistency of genetic association mapping results, identifying significant associations between genetic mutations and skeletal phenotypes with reduced variability. This study demonstrates the feasibility and efficiency of automated murine bone length measurement in the identification of mouse models of abnormal skeletal phenotypes. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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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 1354
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 - 8 Oct 2023
Viewed by 2481
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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20 pages, 6625 KiB  
Article
Direct Estimation of Equivalent Bioelectric Sources Based on Huygens’ Principle
by Georgia Theodosiadou, Dimitrios G. Arnaoutoglou, Ioannis Nannis, Sotirios Katsimentes, Georgios Ch. Sirakoulis and George A. Kyriacou
Bioengineering 2023, 10(9), 1063; https://doi.org/10.3390/bioengineering10091063 - 9 Sep 2023
Viewed by 1083
Abstract
An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens’ Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart’s surface in real time. The main scope of this work was to [...] Read more.
An estimation of the electric sources in the heart was conducted using a novel method, based on Huygens’ Principle, aiming at a direct estimation of equivalent bioelectric sources over the heart’s surface in real time. The main scope of this work was to establish a new, fast approach to the solution of the inverse electrocardiography problem. The study was based on recorded electrocardiograms (ECGs). Based on Huygens’ Principle, measurements obtained from the surfaceof a patient’s thorax were interpolated over the surface of the employed volume conductor model and considered as secondary Huygens’ sources. These sources, being non-zero only over the surface under study, were employed to determine the weighting factors of the eigenfunctions’ expansion, describing the generated voltage distribution over the whole conductor volume. With the availability of the potential distribution stemming from measurements, the electromagnetics reciprocity theorem is applied once again to yield the equivalent sources over the pericardium. The methodology is self-validated, since the surface potentials calculated from these equivalent sources are in very good agreement with ECG measurements. The ultimate aim of this effort is to create a tool providing the equivalent epicardial voltage or current sources in real time, i.e., during the ECG measurements with multiple electrodes. 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 2 | Viewed by 1400
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 - 8 Aug 2023
Cited by 1 | Viewed by 2063
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 1498
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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16 pages, 907 KiB  
Systematic Review
Deep Learning for Nasopharyngeal Carcinoma Segmentation in Magnetic Resonance Imaging: A Systematic Review and Meta-Analysis
by Chih-Keng Wang, Ting-Wei Wang, Ya-Xuan Yang and Yu-Te Wu
Bioengineering 2024, 11(5), 504; https://doi.org/10.3390/bioengineering11050504 - 17 May 2024
Cited by 1 | Viewed by 1186
Abstract
Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective [...] Read more.
Nasopharyngeal carcinoma is a significant health challenge that is particularly prevalent in Southeast Asia and North Africa. MRI is the preferred diagnostic tool for NPC due to its superior soft tissue contrast. The accurate segmentation of NPC in MRI is crucial for effective treatment planning and prognosis. We conducted a search across PubMed, Embase, and Web of Science from inception up to 20 March 2024, adhering to the PRISMA 2020 guidelines. Eligibility criteria focused on studies utilizing DL for NPC segmentation in adults via MRI. Data extraction and meta-analysis were conducted to evaluate the performance of DL models, primarily measured by Dice scores. We assessed methodological quality using the CLAIM and QUADAS-2 tools, and statistical analysis was performed using random effects models. The analysis incorporated 17 studies, demonstrating a pooled Dice score of 78% for DL models (95% confidence interval: 74% to 83%), indicating a moderate to high segmentation accuracy by DL models. Significant heterogeneity and publication bias were observed among the included studies. Our findings reveal that DL models, particularly convolutional neural networks, offer moderately accurate NPC segmentation in MRI. This advancement holds the potential for enhancing NPC management, necessitating further research toward integration into clinical practice. Full article
(This article belongs to the Special Issue Applications of Computational Modeling in Biomedical Image and Signal Processing)
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