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Electronics
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Applications of Artificial Intelligence in Biomedicine
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Applications of Artificial Intelligence in Biomedicine

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 4689

Special Issue Editors

Prof. Dr. Xiaojun Chen

E-Mail Website
Guest Editor
Institute of Biomedical Manufacturing and Life Quality Engineering, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
Interests: biomedical image analysis; image-guided interventions; artificial intelligence in biomedical physics and analysis; VR/AR/MR technology in medicine; medical robotics; biomedical manufacturing
Prof. Dr. Xiaoyi Jiang

E-Mail Website
Co-Guest Editor
Department of Computer Science, University of Münster, 48165 Münster, Germany
Interests: image analysis (biomedical, 3D); pattern recognition; machine learning
Prof. Dr. Alejandro F Frangi

E-Mail Website
Co-Guest Editor
The School of Computing and the School of Medicine, The University of Leeds, Leeds, UK
Interests: computational medicine with emphasis on computational medical imaging; image-based biomechanics; machine learning; big health data analytics
Dr. Shuo Li

E-Mail Website
Co-Guest Editor
Department of Medical Imaging and Department of Medical Biophysics, University of Western Ontario (Western University, UWO), London, ON N6A 5B8, Canada
Interests: artificial intelligence; big data; machine learning; medical image analysis
Prof. Dr. Dinggang Shen

E-Mail Website
Co-Guest Editor
School of Biomedical Engineering, Shanghai Tech University, Shanghai 200135, China
Interests: medical image analysis; artificial intelligence; machine learning

Special Issue Information

Dear Colleagues,

Artificial intelligence (AI) has been applied to many aspects of biomedicine in recent years, including the detection, diagnosis, and treatment of disease. Many remarkable achievements have been made through AI, especially deep learning (DL) technology (e.g., UNet-like neural networks for fast and accurate medical image segmentation and AI systems for efficient treatment designing and surgical planning). AI can also be used to detect disease (e.g., Alzheimer’s) in early stages and control robot arms in an interactive and intelligent way during surgical operation. In the future, AI will be used throughout the health care system and benefit not only patients but also doctors.

The main aim of this Special Issue is to publish works that propose novel AI methods applicable to solve biomedical problems like medical image segmentation and multimodality image registration, and cutting-edge AI systems used to assist disease detection, diagnostic decision, surgical planning and so on. We are also looking for groundbreaking discoveries obtained using AI methods.

The topics of interest include, but are not limited to:

  • AI-based medical image segmentation.
  • AI-based multimodality image registration.
  • AI-based early-stage disease detection.
  • AI-based surgical planning.
  • Intelligent diagnosis systems.
  • Intelligent robot arm control during surgical operation.

Prof. Dr. Xiaojun Chen
Prof. Dr. Xiaoyi Jiang
Prof. Dr. Alejandro F Frangi
Dr. Shuo Li 
Prof. Dr. Dinggang Shen
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. Electronics 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 2400 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
  • medical image segmentation
  • multimodality image registration
  • image-guided surgery
  • surgical planning
  • surgical robotics

Published Papers (2 papers)

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Research

11 pages, 2852 KiB  
Article
A Fast Automatic Reconstruction Method for Panoramic Images Based on Cone Beam Computed Tomography
by Jianguo Zhang, Yichuan Jiang, Fei Gao, Sheng Zhao, Fan Yang and Liang Song
Electronics 2022, 11(15), 2404; https://doi.org/10.3390/electronics11152404 - 01 Aug 2022
Cited by 6 | Viewed by 2519
Abstract
Panoramic images have been widely used in the diagnosis of dental diseases. In the process of panoramic image reconstruction, the position of the dental arch curve usually affects the quality of display content, especially the completion level of the panoramic image. In addition, [...] Read more.
Panoramic images have been widely used in the diagnosis of dental diseases. In the process of panoramic image reconstruction, the position of the dental arch curve usually affects the quality of display content, especially the completion level of the panoramic image. In addition, the metal implants in the patient’s mouth often lead the contrast of the panoramic image to decrease. This paper describes a method to automatically synthesize panoramic images from dental cone beam computed tomography (CBCT) data. The proposed method has two essential features: the first feature is that the method can detect the dental arch curve through axial maximum intensity projection images over different ranges, and the second feature is that our method is able to adjust the intensity distribution of the implant in critical areas, to reduce the impact of the implant on the contrast of the panoramic image. The proposed method was tested on 50 CBCT datasets; the panoramic images generated by this method were compared with images attained from three other commonly used approaches and then subjectively scored by three experienced dentists. In the comprehensive image contrast score, the method in this paper has the highest score of 11.16 ± 2.64 points. The results show that the panoramic images generated by this method have better image contrast. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedicine)
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14 pages, 2723 KiB  
Article
LGMSU-Net: Local Features, Global Features, and Multi-Scale Features Fused the U-Shaped Network for Brain Tumor Segmentation
by Xuejiao Pang, Zijian Zhao, Yuli Wang, Feng Li and Faliang Chang
Electronics 2022, 11(12), 1911; https://doi.org/10.3390/electronics11121911 - 19 Jun 2022
Cited by 2 | Viewed by 1517
Abstract
Brain tumors are one of the deadliest cancers in the world. Researchers have conducted a lot of research work on brain tumor segmentation with good performance due to the rapid development of deep learning for assisting doctors in diagnosis and treatment. However, most [...] Read more.
Brain tumors are one of the deadliest cancers in the world. Researchers have conducted a lot of research work on brain tumor segmentation with good performance due to the rapid development of deep learning for assisting doctors in diagnosis and treatment. However, most of these methods cannot fully combine multiple feature information and their performances need to be improved. This study developed a novel network fusing local features representing detailed information, global features representing global information, and multi-scale features enhancing the model’s robustness to fully extract the features of brain tumors and proposed a novel axial-deformable attention module for modeling global information to improve the performance of brain tumor segmentation to assist clinicians in the automatic segmentation of brain tumors. Moreover, positional embeddings were used to make the network training faster and improve the method’s performance. Six metrics were used to evaluate the proposed method on the BraTS2018 dataset. Outstanding performance was obtained with Dice score, mean Intersection over Union, precision, recall, params, and inference time of 0.8735, 0.7756, 0.9477, 0.8769, 69.02 M, and 15.66 millisecond, respectively, for the whole tumor. Extensive experiments demonstrated that the proposed network obtained excellent performance and was helpful in providing supplementary advice to the clinicians. Full article
(This article belongs to the Special Issue Applications of Artificial Intelligence in Biomedicine)
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