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Mathematics
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Applications of Artificial Intelligence in Biomedical Image Processing
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Applications of Artificial Intelligence in Biomedical Image Processing

A special issue of Mathematics (ISSN 2227-7390). This special issue belongs to the section "E3: Mathematical Biology".

Deadline for manuscript submissions: 9 May 2025 | Viewed by 9965

Special Issue Editors

Dr. James Chung-Wai Cheung

E-Mail Website
Guest Editor
Department of Biomedical Engineering, Faculty of Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: medical imaging; data visualization; artificial intelligence; deep learning; wearable device technologies; robotic technologies; augmented reality; musculoskeletal tissue; signal processing; dysphagia; dementia; biomaterials
Prof. Dr. Yanping Huang

E-Mail Website
Guest Editor
School of Physics and Optoelectronic Engineering, Foshan University, Foshan 528011, China
Interests: OCT; microangiography; medical image processing; high frequency ultrasound; soft tissue elasticity

Special Issue Information

Dear Colleagues,

Biomedical image processing is an interdisciplinary area that is important for the screening, assessment, diagnosis, and tracking of disease progression, and for evaluating the effectiveness of interventions. The significance of biomedical image processing lies in its role in facilitating clinical decisions, and therefore saving lives and reducing suffering. The development of computer-aided diagnosis substantiated by different artificial intelligence approaches further supports clinicians in the automatic detection or segmentation of lesions, computerized extraction of quantitative measurements, interpretation of medical images, and predictions. With the big data of multimodal imaging modalities, the applications of artificial intelligence (machine learning, deep learning, neural networks, image fusion, etc.) are becoming increasingly important and popular.

This Special Issue invites original research and review articles on the development and advancement of artificial intelligence approaches, techniques, and models applied in the field of biomedical image processing.

Papers are solicited on topics including but not limited to the following:

  • State-of-the-art applications of AI-based disease diagnosis;
  • Image processing and segmentation;
  • GAN-based denoising;
  • Super-resolution;
  • DL-based uncertainty management;
  • AutoML’s role in optimizing DL models;
  • The interpretability of DL models’ decisions;
  • Image visualization for explaining DL decisions;
  • Big data fusion and multimodal data;
  • Ensemble deep-learning models.

Dr. James Chung-Wai Cheung
Prof. Dr. Yanping Huang
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. Mathematics is an international peer-reviewed open access semimonthly 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 2600 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

  • artificial intelligence
  • deep learning
  • machine learning
  • convolutional neural networks
  • computer-aided diagnosis
  • radiomics
  • segmentation
  • signal processing

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Published Papers (5 papers)

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Research

21 pages, 38700 KiB  
Article
Transformative Noise Reduction: Leveraging a Transformer-Based Deep Network for Medical Image Denoising
by Rizwan Ali Naqvi, Amir Haider, Hak Seob Kim, Daesik Jeong and Seung-Won Lee
Mathematics 2024, 12(15), 2313; https://doi.org/10.3390/math12152313 - 24 Jul 2024
Cited by 3 | Viewed by 1384
Abstract
Medical image denoising has numerous real-world applications. Despite their widespread use, existing medical image denoising methods fail to address complex noise patterns and typically generate artifacts in numerous cases. This paper proposes a novel medical image denoising method that learns denoising using an [...] Read more.
Medical image denoising has numerous real-world applications. Despite their widespread use, existing medical image denoising methods fail to address complex noise patterns and typically generate artifacts in numerous cases. This paper proposes a novel medical image denoising method that learns denoising using an end-to-end learning strategy. Furthermore, the proposed model introduces a novel deep–wider residual block to capture long-distance pixel dependencies for medical image denoising. Additionally, this study proposes leveraging multi-head attention-guided image reconstruction to effectively denoise medical images. Experimental results illustrate that the proposed method outperforms existing qualitative and quantitative evaluation methods for numerous medical image modalities. The proposed method can outperform state-of-the-art models for various medical image modalities. It illustrates a significant performance gain over its counterparts, with a cumulative PSNR score of 8.79 dB. The proposed method can also denoise noisy real-world medical images and improve clinical application performance such as abnormality detection. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Image Processing)
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22 pages, 10496 KiB  
Article
Deep Learning for Motion Artifact-Suppressed OCTA Image Generation from Both Repeated and Adjacent OCT Scans
by Zhefan Lin, Qinqin Zhang, Gongpu Lan, Jingjiang Xu, Jia Qin, Lin An and Yanping Huang
Mathematics 2024, 12(3), 446; https://doi.org/10.3390/math12030446 - 30 Jan 2024
Viewed by 2205
Abstract
Optical coherence tomography angiography (OCTA) is a popular technique for imaging microvascular networks, but OCTA image quality is commonly affected by motion artifacts. Deep learning (DL) has been used to generate OCTA images from structural OCT images, yet limitations persist, such as low [...] Read more.
Optical coherence tomography angiography (OCTA) is a popular technique for imaging microvascular networks, but OCTA image quality is commonly affected by motion artifacts. Deep learning (DL) has been used to generate OCTA images from structural OCT images, yet limitations persist, such as low label image quality caused by motion and insufficient use of neighborhood information. In this study, an attention-based U-Net incorporating both repeated and adjacent structural OCT images in network input and high-quality label OCTA images in training was proposed to generate high-quality OCTA images with motion artifact suppression. A sliding-window correlation-based adjacent position (SWCB-AP) image fusion method was proposed to generate high-quality OCTA label images with suppressed motion noise. Six different DL schemes with various configurations of network inputs and label images were compared to demonstrate the superiority of the proposed method. Motion artifact severity was evaluated by a motion noise index in B-scan (MNI-B) and in en-face (MNI-C) OCTA images, which were specifically defined in this study for the purpose of evaluating various DL models’ capability in motion noise suppression. Experimental results on a nailfold OCTA image dataset showed that the proposed DL method generated the best results with a peak signal-to-noise ratio (PSNR) of 32.666 ± 7.010 dB, structural similarity (SSIM) of 0.926 ± 0.051, mean absolute error (MAE) of 1.798 ± 1.575, and MNI-B of 0.528 ± 0.124 in B-scan OCTA images and a contrast-to-noise ratio (CNR) of 1.420 ± 0.291 and MNI-C of 0.156 ± 0.057 in en-face OCTA images. Our proposed DL approach generated OCTA images with improved blood flow contrast and reduced motion artifacts, which could be used as a fundamental signal processing module in generating high-quality OCTA images from structural OCT images. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Image Processing)
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16 pages, 4072 KiB  
Article
OCT Retinopathy Classification via a Semi-Supervised Pseudo-Label Sub-Domain Adaptation and Fine-Tuning Method
by Zhicong Tan, Qinqin Zhang, Gongpu Lan, Jingjiang Xu, Chubin Ou, Lin An, Jia Qin and Yanping Huang
Mathematics 2024, 12(2), 347; https://doi.org/10.3390/math12020347 - 21 Jan 2024
Cited by 1 | Viewed by 1622
Abstract
Conventional OCT retinal disease classification methods primarily rely on fully supervised learning, which requires a large number of labeled images. However, sometimes the number of labeled images in a private domain is small but there exists a large annotated open dataset in the [...] Read more.
Conventional OCT retinal disease classification methods primarily rely on fully supervised learning, which requires a large number of labeled images. However, sometimes the number of labeled images in a private domain is small but there exists a large annotated open dataset in the public domain. In response to this scenario, a new transfer learning method based on sub-domain adaptation (TLSDA), which involves a first sub-domain adaptation and then fine-tuning, was proposed in this study. Firstly, a modified deep sub-domain adaptation network with pseudo-label (DSAN-PL) was proposed to align the feature spaces of a public domain (labeled) and a private domain (unlabeled). The DSAN-PL model was then fine-tuned using a small amount of labeled OCT data from the private domain. We tested our method on three open OCT datasets, using one as the public domain and the other two as the private domains. Remarkably, with only 10% labeled OCT images (~100 images per category), TLSDA achieved classification accuracies of 93.63% and 96.59% on the two private datasets, significantly outperforming conventional transfer learning approaches. With the Gradient-weighted Class Activation Map (Grad-CAM) technique, it was observed that the proposed method could more precisely localize the subtle lesion regions for OCT image classification. TLSDA could be a potential technique for applications where only a small number of images is labeled in a private domain and there exists a public database having a large number of labeled images with domain difference. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Image Processing)
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16 pages, 6858 KiB  
Article
One-Shot Learning for Optical Coherence Tomography Angiography Vessel Segmentation Based on Multi-Scale U2-Net
by Shudong Liu, Shuai Guo, Jia Cong, Yue Yang, Zihui Guo and Boyu Gu
Mathematics 2023, 11(24), 4890; https://doi.org/10.3390/math11244890 - 6 Dec 2023
Cited by 1 | Viewed by 1220
Abstract
Vessel segmentation in optical coherence tomography angiography (OCTA) is crucial for the detection and diagnosis of various eye diseases. However, it is hard to distinguish intricate vessel morphology and quantify the density of blood vessels due to the large variety of vessel sizes, [...] Read more.
Vessel segmentation in optical coherence tomography angiography (OCTA) is crucial for the detection and diagnosis of various eye diseases. However, it is hard to distinguish intricate vessel morphology and quantify the density of blood vessels due to the large variety of vessel sizes, significant background noise, and small datasets. To this end, a retinal angiography multi-scale segmentation network, integrated with the inception and squeeze-and-excitation modules, is proposed to address the above challenges under the one-shot learning paradigm. Specifically, the inception module extends the receptive field and extracts multi-scale features effectively to handle diverse vessel sizes. Meanwhile, the squeeze-and-excitation module modifies channel weights adaptively to improve the vessel feature extraction ability in complex noise backgrounds. Furthermore, the one-shot learning paradigm is adapted to alleviate the problem of the limited number of images in existing retinal OCTA vascular datasets. Compared with the classic U2-Net, the proposed model gains improvements in the Dice coefficient, accuracy, precision, recall, and intersection over union by 3.74%, 4.72%, 8.62%, 4.87%, and 4.32% respectively. The experimental results demonstrate that the proposed one-shot learning method is an effective solution for retinal angiography image segmentation. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Image Processing)
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21 pages, 643 KiB  
Article
Quality Assessment Assistance of Lateral Knee X-rays: A Hybrid Convolutional Neural Network Approach
by Simon Lysdahlgaard, Sandi Baressi Šegota, Søren Hess, Ronald Antulov, Martin Weber Kusk and Zlatan Car
Mathematics 2023, 11(10), 2392; https://doi.org/10.3390/math11102392 - 22 May 2023
Cited by 1 | Viewed by 2395
Abstract
A common issue with X-ray examinations (XE) is the erroneous quality classification of the XE, implying that the process needs to be repeated, thus delaying the diagnostic assessment of the XE and increasing the amount of radiation the patient receives. The authors propose [...] Read more.
A common issue with X-ray examinations (XE) is the erroneous quality classification of the XE, implying that the process needs to be repeated, thus delaying the diagnostic assessment of the XE and increasing the amount of radiation the patient receives. The authors propose a system for automatic quality classification of XE based on convolutional neural networks (CNN) that would simplify this process and significantly decrease erroneous quality classification. The data used for CNN training consist of 4000 knee images obtained via radiography procedure (KXE) in total, with 2000 KXE labeled as acceptable and 2000 as unacceptable. Additionally, half of the KXE belonging to each label are right knees and left knees. Due to the sensitivity to image orientation of some CNNs, three approaches are discussed: (1) Left-right-knee (LRK) classifies XE based just on their label, without taking into consideration their orientation; (2) Orientation discriminator (OD) for the left knee (LK) and right knee (RK) analyses images based on their orientation and inserts them into two separate models regarding orientation; (3) Orientation discriminator combined with knee XRs flipped to the left or right (OD-LFK)/OD-RFK trains the models with all images being horizontally flipped to the same orientation and uses the aforementioned OD to determine whether the image needs to be flipped or not. All the approaches are tested with five CNNs (AlexNet, ResNet50, ResNet101, ResNet152, and Xception), with grid search and k-fold cross-validation. The best results are achieved using the OD-RFK hybrid approach with the Xception network architecture as the classifier and ResNet152 as the OD, with an average AUC of 0.97 (±0.01). Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedical Image Processing)
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