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Deep Learning Application in Medical Image Analysis
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Deep Learning Application in Medical Image Analysis

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 (20 July 2023) | Viewed by 16899

Special Issue Editor

Prof. Dr. Shengsheng Wang

E-Mail Website
Guest Editor
College of Computer Science and Technology, Jilin University, Changchun 130012, China
Interests: computer vision; medical image analysis; machine learning; deep learning; qualitative spatial reasoning

Special Issue Information

Dear Colleagues,

With the rapid development of computer vision and artificial intelligence, medical image analysis and computer-aided diagnosis have become emerging interdisciplinary subjects in medicine and computer science. The massive data volume and its objective and quantifiable characteristics in medical images are suitable for the application scenario of deep learning, making medical image analysis the most promising field for implementing computer vision. However, there are still many problems to be solved, such as reducing reliance on large-scale data, improving model generalization ability, and enhancing interpretability.

This Special Issue aims to highlight new and innovative work focused on deep learning applications in medical image analysis. We encourage high-quality research in medical image feature extraction, segmentation, detection, classification, registration, 3D reconstruction, and computer-aided medical diagnosis. In addition, quality manuscripts are welcome in medical-image-based radiomics, medical image privacy protection, and other cutting-edge research.

Prof. Dr. Shengsheng Wang
Guest Editor

Manuscript Submission Information

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Keywords

  • deep learning
  • medical image analysis
  • image feature representation and learning
  • image segmentation, detection and classification
  • computer vision

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Published Papers (4 papers)

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Research

13 pages, 3100 KiB  
Article
Liver Tumor Computed Tomography Image Segmentation Based on an Improved U-Net Model
by Hefu Li and Binmei Liang
Appl. Sci. 2023, 13(20), 11283; https://doi.org/10.3390/app132011283 - 13 Oct 2023
Cited by 5 | Viewed by 2033
Abstract
An automated segmentation method for computed tomography (CT) images of liver tumors is an urgent clinical need. Tumor areas within liver cancer images are easily missed as they are small and have unclear borders. To address these issues, an improved liver tumor segmentation [...] Read more.
An automated segmentation method for computed tomography (CT) images of liver tumors is an urgent clinical need. Tumor areas within liver cancer images are easily missed as they are small and have unclear borders. To address these issues, an improved liver tumor segmentation method based on U-Net is proposed. This involves incorporating attention mechanisms into the U-Net’s skip connections, giving higher weights to important regions. Through dynamically adjusting the attention recognition characteristics, the method achieves accurate localization that is focused on and discriminates target regions. Testing using the LiTS (liver tumor segmentation) public dataset resulted in a Dice similarity coefficient of 0.69. The experiments demonstrated that this method can accurately segment liver tumors. Full article
(This article belongs to the Special Issue Deep Learning Application in Medical Image Analysis)
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13 pages, 836 KiB  
Article
Comparison of Image Normalization Methods for Multi-Site Deep Learning
by Steffen Albert, Barbara D. Wichtmann, Wenzhao Zhao, Angelika Maurer, Jürgen Hesser, Ulrike I. Attenberger, Lothar R. Schad and Frank G. Zöllner
Appl. Sci. 2023, 13(15), 8923; https://doi.org/10.3390/app13158923 - 3 Aug 2023
Cited by 7 | Viewed by 4079
Abstract
In this study, we evaluate the influence of normalization on the performance of deep learning networks for tumor segmentation and the prediction of the pathological response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy. The techniques were applied to a multicenter and multimodal [...] Read more.
In this study, we evaluate the influence of normalization on the performance of deep learning networks for tumor segmentation and the prediction of the pathological response of locally advanced rectal cancer to neoadjuvant chemoradiotherapy. The techniques were applied to a multicenter and multimodal magnet resonance imaging data set consisting of 201 patients recorded at six centers. We implemented and investigated six different normalization methods (setting the mean and standard deviation, histogram matching, percentiles, combining percentiles and histogram matching, fixed window and an auto-encoder with adversarial loss using the imaging parameters) and evaluated their impact on four deep learning tasks: tumor segmentation, prediction of treatment outcome, and prediction of sex and age. The latter two tasks were implemented as a reference test. We trained a modified U-Net with different normalization methods in multiple configurations: on all images, images from all centers except one, and images from a single center. Our results show that normalization only plays a minor role in segmentation, with a difference in Dice of less than 0.02 between the best and worst performing networks. For the prediction of sex and treatment outcomes, the percentile method combined with histogram matching works best for all scenarios. The biggest difference in performance, depending on the normalization method, occurs for classification. In conclusion, normalization is especially important for small data sets or for generalizing to different data distributions. The deep learning method was superior to the classical methods only in a minority of cases, probably due to the limited amount of training data. Full article
(This article belongs to the Special Issue Deep Learning Application in Medical Image Analysis)
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19 pages, 2935 KiB  
Article
Ghost-ResNeXt: An Effective Deep Learning Based on Mature and Immature WBC Classification
by Sai Sambasiva Rao Bairaboina and Srinivasa Rao Battula
Appl. Sci. 2023, 13(6), 4054; https://doi.org/10.3390/app13064054 - 22 Mar 2023
Cited by 12 | Viewed by 5837
Abstract
White blood cells (WBCs) must be evaluated to determine how well the human immune system performs. Abnormal WBC counts may indicate malignancy, tuberculosis, severe anemia, cancer, and other serious diseases. To get an early diagnosis and to check if WBCs are abnormal or [...] Read more.
White blood cells (WBCs) must be evaluated to determine how well the human immune system performs. Abnormal WBC counts may indicate malignancy, tuberculosis, severe anemia, cancer, and other serious diseases. To get an early diagnosis and to check if WBCs are abnormal or normal, one needs to examine the numbers and determine the shape of the WBCs. To address this problem, computer-aided procedures have been developed because hematologists perform this laborious, expensive, and time-consuming process manually. Resultantly, a powerful deep learning model was developed in the present study to categorize WBCs, including immature WBCs, from the images of peripheral blood smears. A network based on W-Net, a CNN-based method for WBC classification, was developed to execute the segmentation of leukocytes. Thereafter, significant feature maps were retrieved using a deep learning framework built on GhostNet. Then, they were categorized using a ResNeXt with a Wildebeest Herd Optimization (WHO)-based method. In addition, Deep Convolutional Generative Adversarial Network (DCGAN)-based data augmentation was implemented to handle the imbalanced data issue. To validate the model performance, the proposed technique was compared with the existing techniques and achieved 99.16%, 99.24%, and 98.61% accuracy levels for Leukocyte Images for Segmentation and Classification (LISC), Blood Cell Count and Detection (BCCD), and the single-cell morphological dataset, respectively. Thus, we can conclude that the proposed approach is valuable and adaptable for blood cell microscopic analysis in clinical settings. Full article
(This article belongs to the Special Issue Deep Learning Application in Medical Image Analysis)
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30 pages, 7576 KiB  
Article
Lenke Classification of Scoliosis Based on Segmentation Network and Adaptive Shape Descriptor
by Dong Liu, Lingrong Zhang, Jinglin Yang and Anping Lin
Appl. Sci. 2023, 13(6), 3905; https://doi.org/10.3390/app13063905 - 19 Mar 2023
Cited by 2 | Viewed by 4054
Abstract
Scoliosis is a common spinal deformity that seriously affects patients’ physical and mental health. An accurate Lenke classification is greatly significant for evaluating and treating scoliosis. Currently, the clinical diagnosis mainly relies on manual measurement; however, using computer vision assists with an intelligent [...] Read more.
Scoliosis is a common spinal deformity that seriously affects patients’ physical and mental health. An accurate Lenke classification is greatly significant for evaluating and treating scoliosis. Currently, the clinical diagnosis mainly relies on manual measurement; however, using computer vision assists with an intelligent diagnosis. Due to the complex rules of Lenke classification and the characteristics of medical imaging, the fully automated Lenke classification of scoliosis remains a considerable challenge. Herein, a novel Lenke classification method for scoliosis using X-rays based on segmentation networks and adaptive shape descriptors is proposed. Three aspects of our method should be noted in comparison with the previous approaches. We used Unet++ to segment the vertebrae and designed a post-processing operation to improve the segmentation effect. Then, we proposed a new shape descriptor to extract the shape features for segmented vertebrae in greater detail. Finally, we proposed a new Lenke classification framework for scoliosis that contains two schemes based on Cobb angle measurement and shape classification, respectively. After rigorous experimental evaluations on a public dataset, our method achieved the best performance and outperformed other sophisticated approaches. Full article
(This article belongs to the Special Issue Deep Learning Application in Medical Image Analysis)
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