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Applied Sciences
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Applications of Computer Vision and Image Processing in Medicine
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Applications of Computer Vision and Image Processing in Medicine

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 1601

Special Issue Editor

Dr. Hae Yong Kim

E-Mail Website
Guest Editor
Department of Engenharia de Sistemas Eletrônicos, Escola Politécnica, Universidade de São Paulo, CEP 05508-900, São Paulo, SP, Brazil
Interests: image processing; machine learning; computer security

Special Issue Information

Dear Colleagues,

Recent advances in computer vision and image processing, particularly the evolution of deep learning models, have allowed us to solve many problems considered unsolvable just two decades ago. Medicine has swiftly embraced these breakthroughs, with their adoption being widespread.

One of the most prominent applications is in radiology, where computer vision has demonstrated its ability to detect disease with remarkable accuracy by examining X-rays, CT scans and MRIs. Image processing assists pathologists in analyzing tissue samples, dermatologists in diagnosing skin cancer, and ophthalmologists in fundus examinations and retinal scans. Beyond diagnosis, computer vision contributes to the remote monitoring of patients. The ethical dimensions and regulatory frameworks surrounding these technologies are evolving in parallel with their rapid integration into healthcare systems.

This Special Issue aims to attract manuscripts at the intersection of healthcare and technology, emphasizing the pivotal role that computer vision and image processing play in the ongoing transformation of medical practices.

Dr. Hae Yong Kim
Guest Editor

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. Applied Sciences 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

  • automated disease diagnosis
  • image-based patient monitoring
  • image-guided intervention planning
  • medical image feature extraction
  • medical image segmentation and registration
  • ethical considerations in medical image processing
  • medical image datasets

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Published Papers (1 paper)

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Research

17 pages, 3232 KiB  
Article
SARFNet: Selective Layer and Axial Receptive Field Network for Multimodal Brain Tumor Segmentation
by Bin Guo, Ning Cao, Peng Yang and Ruihao Zhang
Appl. Sci. 2024, 14(10), 4233; https://doi.org/10.3390/app14104233 - 16 May 2024
Viewed by 1055
Abstract
Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, [...] Read more.
Efficient magnetic resonance imaging (MRI) segmentation, which is helpful for treatment planning, is essential for identifying brain tumors from detailed images. In recent years, various convolutional neural network (CNN) structures have been introduced for brain tumor segmentation tasks and have performed well. However, the downsampling blocks of most existing methods are typically used only for processing the variation in image sizes and lack sufficient capacity for further extraction features. We, therefore, propose SARFNet, a method based on UNet architecture, which consists of the proposed SLiRF module and advanced AAM module. The SLiRF downsampling module can extract feature information and prevent the loss of important information while reducing the image size. The AAM block, incorporated into the bottleneck layer, captures more contextual information. The Channel Attention Module (CAM) is introduced into skip connections to enhance the connections between channel features to improve accuracy and produce better feature expression. Ultimately, deep supervision is utilized in the decoder layer to avoid vanishing gradients and generate better feature representations. Many experiments were performed to validate the effectiveness of our model on the BraTS2018 dataset. SARFNet achieved Dice coefficient scores of 90.40, 85.54, and 82.15 for the whole tumor (WT), tumor core (TC), and enhancing tumor (ET), respectively. The results show that the proposed model achieves state-of-the-art performance compared with twelve or more benchmarks. Full article
(This article belongs to the Special Issue Applications of Computer Vision and Image Processing in Medicine)
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