Advanced Medical Imaging Technologies and Applications

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 (30 October 2023) | Viewed by 4405

Special Issue Editors

Department of Computer Engineering and Applications, University of Electronic Science and Technology of China, Chengdu, China
Interests: pathology image; OCT, MRI, hashing; convolutional neural networks; interpretable; robustness
College of Information Science and Technology, Northwest University, Xi’an 710127, China
Interests: detection; segmentation; pathology image; deep learning

Special Issue Information

Dear Colleagues,

This Special Issue focuses on bridging the gap among medicine, biology, imaging, and computer-aided systems. Its scope not only contains various types of medical imaging achieved by modalities including microscopy, ultrasound, X-rays, magnetic resonance, radionuclides, microwaves, and optical methods, but also consists of novel medical image processing and analysis, visualization and performance, feature extraction, segmentation, detection, pattern recognition, and machine learning methods. Strong application papers with novel methods are extremely encouraged. We also strongly encourage researchers to release their source codes and biomedical image datasets.

Prof. Dr. Xiaoshuang Shi
Dr. Lei Cui
Guest Editors

Manuscript Submission Information

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Keywords

  • medical image processing and analysis
  • machine learning
  • pattern recognition
  • computer-aided system

Published Papers (4 papers)

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Research

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9 pages, 722 KiB  
Communication
Development and Validation of a Prediction Model for Differentiation of Benign and Malignant Fat-Poor Renal Tumors Using CT Radiomics
by Seokhwan Bang, Hee-Hwan Wang, Hokun Kim, Moon Hyung Choi, Jiook Cha, Yeongjin Choi and Sung-Hoo Hong
Appl. Sci. 2023, 13(20), 11345; https://doi.org/10.3390/app132011345 - 16 Oct 2023
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Abstract
Objectives: To develop and validate a machine learning-based CT radiomics classification model for distinguishing benign renal tumors from malignant renal tumors. Methods: We reviewed 499 patients who underwent nephrectomy for solid renal tumors at our institution between 2003 and 2021. In this retrospective [...] Read more.
Objectives: To develop and validate a machine learning-based CT radiomics classification model for distinguishing benign renal tumors from malignant renal tumors. Methods: We reviewed 499 patients who underwent nephrectomy for solid renal tumors at our institution between 2003 and 2021. In this retrospective study, patients who had undergone a computed tomography (CT) scan within 3 months before surgery were included. We randomly divided the dataset in a stratified manner as follows: 75% as the training set and 25% as the test set. By using various feature selection methods and a dimensionality reduction method exclusively for the training set, we selected 160 radiomic features out of 1,288 radiomic features to classify malignant renal tumors. Results: The training set included 396 patients, and the test set included 103 patients. The percentage of extracted radiomic features from patients was 32% (385/1218) after the reproducibility test. In terms of the average Area Under the Receiver Operating Characteristic Curve (AU-ROC) and the average Area Under the Precision-Recall Curve (AU-PRC), the Random Forest model achieved better performance (AU-ROC = 0.725; AU-PRC = 0.899). An average accuracy of 0.778 was obtained on evaluation with the hold-out test set. At the optimal threshold, the Random Forest model showed an F1 score of 0.746, precision of 0.862, sensitivity of 0.657, specificity of 0.651, and Negative Predictive Value (NPV) of 0.364. Conclusions: Our machine learning-based CT radiomics classification model performed well for the independent test set, indicating that it could be a useful tool for discriminating between malignant and benign solid renal tumors. Full article
(This article belongs to the Special Issue Advanced Medical Imaging Technologies and Applications)
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16 pages, 4002 KiB  
Article
Lenke Classification Report Generation Method for Scoliosis Based on Spatial and Context Dual Attention
by Yu Tang, Zhiqin He, Qinmu Wu, Xiao Wang and Yuhang Wang
Appl. Sci. 2023, 13(13), 7981; https://doi.org/10.3390/app13137981 - 07 Jul 2023
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Abstract
The scoliosis report is a diagnosis made by the clinician looking at X-ray images of the spine. However, with numerous images, writing the report can be time-consuming and error-prone. Therefore, this paper proposes an automatic generation model of the end-to-end scoliosis Lenke classification [...] Read more.
The scoliosis report is a diagnosis made by the clinician looking at X-ray images of the spine. However, with numerous images, writing the report can be time-consuming and error-prone. Therefore, this paper proposes an automatic generation model of the end-to-end scoliosis Lenke classification report. The model automatically generates a short diagnostic text to explain the results of the classifiers’ Lenke classification diagnosis of scoliosis. Instead of reproducing the original diagnostic report, the original diagnostic report is described as a short sentence with diagnostic significance. In the model, the CBAM attention module is added to the residual’s path of ResNet-50 to extract key regional features of the image, and the improved Long Term and Short Term Memory Network (M-LSTM) fusion attention mechanism with additional gated operations is used as the decoder to generate more relevant description statements. The model was verified on the scoliosis dataset from Guizhou Orthopaedic Hospital, and the generated diagnostic text obtained good scores on BLEU and CIDEr evaluation indexes, and also satisfactory scores on the evaluation criteria of five professional clinicians. Therefore, the diagnostic text generated by this method had good performance in accuracy and semantic expression. Full article
(This article belongs to the Special Issue Advanced Medical Imaging Technologies and Applications)
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12 pages, 1804 KiB  
Article
A Radiomics Approach Based on Follow-Up CT for Pathological Subtypes Classification of Pulmonary Ground Glass Nodules
by Chenchen Ma, Shihong Yue and Chang Sun
Appl. Sci. 2022, 12(20), 10587; https://doi.org/10.3390/app122010587 - 20 Oct 2022
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Abstract
Preoperative, non-invasive, and accurate identification of the pathological subtypes of pulmonary ground glass nodules (GGNs) play an important role in the precise selection of clinical surgical operations and individualized treatment plans. Efforts have been made for the classification of pathological subtypes of GGNs, [...] Read more.
Preoperative, non-invasive, and accurate identification of the pathological subtypes of pulmonary ground glass nodules (GGNs) play an important role in the precise selection of clinical surgical operations and individualized treatment plans. Efforts have been made for the classification of pathological subtypes of GGNs, but most existing methods focus on benign or malignant diagnosis of GGNs by means of a one-time computed tomography image (CTI), which fails to capture the nodule development based on follow-up CTI. In this paper, a novel method for subtype classification based on follow-up CTIs is presented as a viable option to the existing one-time CTI-based approach. A total of 383 follow-up CTIs with GGNs from 146 patients was collected and retrospectively labeled via posterior surgical pathology. Feature extraction is performed individually to the follow-up CTIs. The extracted feature differences were represented as a vector, which was then used to construct a set of vectors for all the patients. Finally, a subspace K-nearest neighbor classifier was built to predict the pathological subtypes of GGNs. Experimental validation confirmed the efficacy of the new method over the existing method. Results showed that the accuracy of the new method could reach 72.5%, while the existing methods had an upper bound of 67.5% accuracy. Subsequent three-category comparison experiments were also performed to demonstrate that the new method could increase the accuracy up to 21.33% compared to the existing methods that use one-time CTI. Full article
(This article belongs to the Special Issue Advanced Medical Imaging Technologies and Applications)
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7 pages, 872 KiB  
Brief Report
Minimum Sample Size Estimate for Classifying Invasive Lung Adenocarcinoma
by Chenchen Ma and Shihong Yue
Appl. Sci. 2022, 12(17), 8469; https://doi.org/10.3390/app12178469 - 24 Aug 2022
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Abstract
Statistical Learning Theory (SLT) plays an important role in prediction estimation and machine learning when only limited samples are available. At present, determining how many samples are necessary under given circumstances for prediction accuracy is still an unknown. In this paper, the medical [...] Read more.
Statistical Learning Theory (SLT) plays an important role in prediction estimation and machine learning when only limited samples are available. At present, determining how many samples are necessary under given circumstances for prediction accuracy is still an unknown. In this paper, the medical diagnosis on lung cancer is taken as an example to solve the problem. Invasive adenocarcinoma (IA) is a main type of lung cancer, often presented as ground glass nodules (GGNs) in patient’s CT images. Accurately discriminating IA from non-IA based on GGNs has important implications for taking the right approach to treatment and cure. Support Vector Machine (SVM) is an SLT application and is used to classify GGNs, wherein the interrelation between the generalization and the lower bound of necessary sampling numbers can be effectively recovered. In this research, to validate the interrelation, 436 GGNs were collected and labeled using surgical pathology. Then, a feature vector was constructed for each GGN sample through the fully connected layer of AlexNet. A 10-dimensional feature subset was then selected with the p-value calculated using Analysis of Variance (ANOVA). Finally, four sets with different sample sizes were used to construct an SVM classifier. Experiments show that a theoretical estimate of minimum sample size is consistent with actual values, and the lower bound on sample size can be solved under various generalization requirements. Full article
(This article belongs to the Special Issue Advanced Medical Imaging Technologies and Applications)
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