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Bioengineering
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Recent Progress in Biomedical Image Processing
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Recent Progress in Biomedical Image Processing

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

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 8059

Special Issue Editors

Dr. Mingrui Yang

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
Interests: classification; image processing; analysis; applied mathematics; compressed sensing; RIP; deep learning
Dr. Kunio Nakamura

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA
Interests: image analysis; MRI; multiple sclerosis

Special Issue Information

Dear Colleagues,

The aim of this Special Issue on "Recent Progress in Biomedical Image Processing" is to provide a platform for researchers to showcase their latest research findings and advancements in the field of biomedical image processing. The issue will cover a broad range of topics related to biomedical image processing, including image acquisition, analysis, visualization, and interpretation. The goal is to present the latest techniques and methodologies that have been developed to address challenges in biomedical imaging, including those related to diagnosis, treatment, and research. In particular, the Special Issue aims to highlight the use of advanced technologies such as machine learning and artificial intelligence in biomedical image processing. Overall, the Special Issue aims to contribute to the advancement of biomedical imaging technologies and improve the diagnosis and treatment of various diseases. It will provide a valuable resource for researchers, clinicians, and engineers working in the field of biomedical image processing.

Dr. Mingrui Yang
Dr. Kunio Nakamura
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.

Published Papers (5 papers)

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Research

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15 pages, 6144 KiB  
Article
Unsupervised Segmentation of Knee Bone Marrow Edema-like Lesions Using Conditional Generative Models
by Andrew Seohwan Yu, Mingrui Yang, Richard Lartey, William Holden, Ahmet Hakan Ok, Sameed Khan, Jeehun Kim, Carl Winalski, Naveen Subhas, Vipin Chaudhary and Xiaojuan Li
Bioengineering 2024, 11(6), 526; https://doi.org/10.3390/bioengineering11060526 - 22 May 2024
Viewed by 313
Abstract
Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to [...] Read more.
Bone marrow edema-like lesions (BMEL) in the knee have been linked to the symptoms and progression of osteoarthritis (OA), a highly prevalent disease with profound public health implications. Manual and semi-automatic segmentations of BMELs in magnetic resonance images (MRI) have been used to quantify the significance of BMELs. However, their utilization is hampered by the labor-intensive and time-consuming nature of the process as well as by annotator bias, especially since BMELs exhibit various sizes and irregular shapes with diffuse signal that lead to poor intra- and inter-rater reliability. In this study, we propose a novel unsupervised method for fully automated segmentation of BMELs that leverages conditional diffusion models, multiple MRI sequences that have different contrast of BMELs, and anomaly detection that do not rely on costly and error-prone annotations. We also analyze BMEL segmentation annotations from multiple experts, reporting intra-/inter-rater variability and setting better benchmarks for BMEL segmentation performance. Full article
(This article belongs to the Special Issue Recent Progress in Biomedical Image Processing)
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15 pages, 2800 KiB  
Article
Age Encoded Adversarial Learning for Pediatric CT Segmentation
by Saba Heidari Gheshlaghi, Chi Nok Enoch Kan, Taly Gilat Schmidt and Dong Hye Ye
Bioengineering 2024, 11(4), 319; https://doi.org/10.3390/bioengineering11040319 - 27 Mar 2024
Viewed by 656
Abstract
Organ segmentation from CT images is critical in the early diagnosis of diseases, progress monitoring, pre-operative planning, radiation therapy planning, and CT dose estimation. However, data limitation remains one of the main challenges in medical image segmentation tasks. This challenge is particularly huge [...] Read more.
Organ segmentation from CT images is critical in the early diagnosis of diseases, progress monitoring, pre-operative planning, radiation therapy planning, and CT dose estimation. However, data limitation remains one of the main challenges in medical image segmentation tasks. This challenge is particularly huge in pediatric CT segmentation due to children’s heightened sensitivity to radiation. In order to address this issue, we propose a novel segmentation framework with a built-in auxiliary classifier generative adversarial network (ACGAN) that conditions age, simultaneously generating additional features during training. The proposed conditional feature generation segmentation network (CFG-SegNet) was trained on a single loss function and used 2.5D segmentation batches. Our experiment was performed on a dataset with 359 subjects (180 male and 179 female) aged from 5 days to 16 years and a mean age of 7 years. CFG-SegNet achieved an average segmentation accuracy of 0.681 dice similarity coefficient (DSC) on the prostate, 0.619 DSC on the uterus, 0.912 DSC on the liver, and 0.832 DSC on the heart with four-fold cross-validation. We compared the segmentation accuracy of our proposed method with previously published U-Net results, and our network improved the segmentation accuracy by 2.7%, 2.6%, 2.8%, and 3.4% for the prostate, uterus, liver, and heart, respectively. The results indicate that our high-performing segmentation framework can more precisely segment organs when limited training images are available. Full article
(This article belongs to the Special Issue Recent Progress in Biomedical Image Processing)
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19 pages, 4254 KiB  
Article
Enhancing Knee MR Image Clarity through Image Domain Super-Resolution Reconstruction
by Vishal Patel, Alan Wang, Andrew Paul Monk and Marco Tien-Yueh Schneider
Bioengineering 2024, 11(2), 186; https://doi.org/10.3390/bioengineering11020186 - 15 Feb 2024
Viewed by 1105
Abstract
This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline [...] Read more.
This study introduces a hybrid analytical super-resolution (SR) pipeline aimed at enhancing the resolution of medical magnetic resonance imaging (MRI) scans. The primary objective is to overcome the limitations of clinical MRI resolution without the need for additional expensive hardware. The proposed pipeline involves three key steps: pre-processing to re-slice and register the image stacks; SR reconstruction to combine information from three orthogonal image stacks to generate a high-resolution image stack; and post-processing using an artefact reduction convolutional neural network (ARCNN) to reduce the block artefacts introduced during SR reconstruction. The workflow was validated on a dataset of six knee MRIs obtained at high resolution using various sequences. Quantitative analysis of the method revealed promising results, showing an average mean error of 1.40 ± 2.22% in voxel intensities between the SR denoised images and the original high-resolution images. Qualitatively, the method improved out-of-plane resolution while preserving in-plane image quality. The hybrid SR pipeline also displayed robustness across different MRI sequences, demonstrating potential for clinical application in orthopaedics and beyond. Although computationally intensive, this method offers a viable alternative to costly hardware upgrades and holds promise for improving diagnostic accuracy and generating more anatomically accurate models of the human body. Full article
(This article belongs to the Special Issue Recent Progress in Biomedical Image Processing)
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24 pages, 9672 KiB  
Article
Design and Validation of a Deep Learning Model for Renal Stone Detection and Segmentation on Kidney–Ureter–Bladder Images
by Zih-Hao Huang, Yi-Yang Liu, Wei-Juei Wu and Ko-Wei Huang
Bioengineering 2023, 10(8), 970; https://doi.org/10.3390/bioengineering10080970 - 16 Aug 2023
Cited by 2 | Viewed by 2458
Abstract
Kidney–ureter–bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and [...] Read more.
Kidney–ureter–bladder (KUB) imaging is used as a frontline investigation for patients with suspected renal stones. In this study, we designed a computer-aided diagnostic system for KUB imaging to assist clinicians in accurately diagnosing urinary tract stones. The image dataset used for training and testing the model comprised 485 images provided by Kaohsiung Chang Gung Memorial Hospital. The proposed system was divided into two subsystems, 1 and 2. Subsystem 1 used Inception-ResNetV2 to train a deep learning model on preprocessed KUB images to verify the improvement in diagnostic accuracy with image preprocessing. Subsystem 2 trained an image segmentation model using the ResNet hybrid, U-net, to accurately identify the contours of renal stones. The performance was evaluated using a confusion matrix for the classification model. We conclude that the model can assist clinicians in accurately diagnosing renal stones via KUB imaging. Therefore, the proposed system can assist doctors in diagnosis, reduce patients’ waiting time for CT scans, and minimize the radiation dose absorbed by the body. Full article
(This article belongs to the Special Issue Recent Progress in Biomedical Image Processing)
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Review

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17 pages, 10599 KiB  
Review
Application of Deep Learning for Prediction of Alzheimer’s Disease in PET/MR Imaging
by Yan Zhao, Qianrui Guo, Yukun Zhang, Jia Zheng, Yang Yang, Xuemei Du, Hongbo Feng and Shuo Zhang
Bioengineering 2023, 10(10), 1120; https://doi.org/10.3390/bioengineering10101120 - 24 Sep 2023
Cited by 5 | Viewed by 2491
Abstract
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is a promising technique that combines the advantages of PET and MR to provide both functional and structural information of the brain. Deep [...] Read more.
Alzheimer’s disease (AD) is a progressive neurodegenerative disorder that affects millions of people worldwide. Positron emission tomography/magnetic resonance (PET/MR) imaging is a promising technique that combines the advantages of PET and MR to provide both functional and structural information of the brain. Deep learning (DL) is a subfield of machine learning (ML) and artificial intelligence (AI) that focuses on developing algorithms and models inspired by the structure and function of the human brain’s neural networks. DL has been applied to various aspects of PET/MR imaging in AD, such as image segmentation, image reconstruction, diagnosis and prediction, and visualization of pathological features. In this review, we introduce the basic concepts and types of DL algorithms, such as feed forward neural networks, convolutional neural networks, recurrent neural networks, and autoencoders. We then summarize the current applications and challenges of DL in PET/MR imaging in AD, and discuss the future directions and opportunities for automated diagnosis, predictions of models, and personalized medicine. We conclude that DL has great potential to improve the quality and efficiency of PET/MR imaging in AD, and to provide new insights into the pathophysiology and treatment of this devastating disease. Full article
(This article belongs to the Special Issue Recent Progress in Biomedical Image Processing)
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