Quantitative and Intelligent Analysis of Medical Imaging, 2nd Edition

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: closed (31 October 2024) | Viewed by 4406

Special Issue Editor


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Guest Editor
1. UJM-Saint-Etienne, INSA, CNRS UMR 5520, INSERM U1206, CREATIS, University of Lyon, F-42023 Saint-Etienne, France
2. UCBL, INSA, UJM-Saint Etienne, CNRS UMR 5520, INSERM U1206, CREATIS, University of Lyon, F-69100 Lyon, France
Interests: magnetic resonance imaging; radiology; cardiology; sports; nutrition
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Special Issue Information

Dear Colleagues,

Medical imaging allows the observation of the internal characteristics of a body through images for clinical analysis and medical interventions.

This field is undergoing rapid development, resulting in improvement in the quality of the images as well as in the quantity of the observed features. Moreover, its democratization is leading to a wide availability of medical image data in almost all pathologies. However, it remains crucial to be able to extract useful and robust information for targeted medical analysis and decisions. Faced with this afflux of data, these treatments allowing extraction must be as automatic as possible, robust, and in line with the needs of physicians in order to empower their efficiency on medical analysis.

Nevertheless, if hundreds of articles are published each year describing semi-automatic or automatic quantification methods, they are unfortunately without a reference implementation (i.e., without source code). The authors or vendors are by essence reluctant to share their algorithms, because there is simply no practical way to (1) share the algorithms and (2) evaluate their performance in a fair way on the same database elaborated from realistic data derived from routine examinations. As a consequence, and as pointed out by many researchers, all available methods, from simple to sophisticated algorithms, are “not as objective as one might think”, requiring user inputs or final supervision to distinguish some artifact and/or noise voxels, i.e., useless information.

As a consequence, despite the huge number of papers that describe over-performing isolated solutions and an increasing number of black box services, there is still a large community of physicians or clinical researchers that are missing satisfactory automatic quantification tools to segment the anatomy and extract quantitative indicators with available quality control to determine the advances or limitations. State-of-the-art and a priori solutions are published but unavailable and unsuitable for worldwide deployment in the clinical (or clinical research) environment where they could be tested in broader patient populations, improved, and made rapidly available for the entire physician and developer community. Widely available clinical databases and common numerical datasets are also missing that could enable the community to easily and rapidly test and evaluate new algorithms, especially in a world of limited resources, where an urgent need therefore emerges for more durable and coordinated research.

In this Special Issue, I would like to invite all colleagues and researchers who share these concerns and who develop approaches attempting to address them to submit their important papers describing their solutions to achieve more reproducible, useful research that can be quickly transferred to clinical research.

The objective of this Special Issue is to collect papers of paramount importance for our future that offer solutions to this critical need: (i) methods that can be used on any image data acquired independently of the scanner manufacturer and that address the abovementioned concerns, (ii) intelligent methods that can both allow unified performance tests on numerical datasets and confidentiality, (iii) smart ways to create shared databases with expert referenced knowledge that the community could feed into and use to demonstrate the performance of new algorithms, and (iv) computer processing methods that are able to enrich diagnosis by extracting objective and clinically useful information from medical images.

Prof. Dr. Magalie Viallon
Guest Editor

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Keywords

  • medical imaging
  • quantitative
  • intelligent analysis
  • diagnosis
  • image segmentation
  • image registration
  • data mining
  • reproducible and open research
  • AI
  • algorithms
  • computer processing methods

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Related Special Issue

Published Papers (3 papers)

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Research

17 pages, 12365 KiB  
Article
A Quantitative Measurement Method for Nuclear-Pleomorphism Scoring in Breast Cancer
by Chai Ling Teoh, Xiao Jian Tan, Khairul Shakir Ab Rahman, Ikmal Hisyam Bakrin, Kam Meng Goh, Joseph Jiun Wen Siet and Wan Zuki Azman Wan Muhamad
Diagnostics 2024, 14(18), 2045; https://doi.org/10.3390/diagnostics14182045 - 14 Sep 2024
Viewed by 534
Abstract
Background/Objectives: Nuclear pleomorphism, a crucial determinant of breast cancer grading under the Nottingham Histopathology Grading (NHG) system, remains inadequately quantified in the existing literature. Motivated by this gap, our study seeks to investigate and establish correlations among morphological features across various scores of [...] Read more.
Background/Objectives: Nuclear pleomorphism, a crucial determinant of breast cancer grading under the Nottingham Histopathology Grading (NHG) system, remains inadequately quantified in the existing literature. Motivated by this gap, our study seeks to investigate and establish correlations among morphological features across various scores of nuclear pleomorphism, as per the NHG system. We aim to quantify nuclear pleomorphism across these scores and validate our proposed measurement method against ground-truth data. Methods: Initially, we deconstruct the descriptions of nuclear pleomorphism into three core elements: size, shape, and appearance. These elements are subsequently mathematically modeled into equations, termed ESize, EShape, and EAppearance. These equations are then integrated into a unified model termed Harmonic Mean (HM). The HM equation yields a value approaching 1 for nuclei demonstrating characteristics of score-3 nuclear pleomorphism and near 0 for those exhibiting features of score-1 nuclear pleomorphism. Results: The proposed HM model demonstrates promising performance metrics, including Accuracy, Recall, Specificity, Precision, and F1-score, with values of 0.97, 0.96, 0.97, 0.94, and 0.95, respectively. Conclusions: In summary, this study proposes the HM equation as a novel feature for the precise quantification of nuclear pleomorphism in breast cancer. Full article
(This article belongs to the Special Issue Quantitative and Intelligent Analysis of Medical Imaging, 2nd Edition)
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26 pages, 3348 KiB  
Article
Hybrid Feature Mammogram Analysis: Detecting and Localizing Microcalcifications Combining Gabor, Prewitt, GLCM Features, and Top Hat Filtering Enhanced with CNN Architecture
by Miguel Alejandro Hernández-Vázquez, Yazmín Mariela Hernández-Rodríguez, Fausto David Cortes-Rojas, Rafael Bayareh-Mancilla and Oscar Eduardo Cigarroa-Mayorga
Diagnostics 2024, 14(15), 1691; https://doi.org/10.3390/diagnostics14151691 - 5 Aug 2024
Cited by 1 | Viewed by 1176
Abstract
Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable [...] Read more.
Breast cancer is a prevalent malignancy characterized by the uncontrolled growth of glandular epithelial cells, which can metastasize through the blood and lymphatic systems. Microcalcifications, small calcium deposits within breast tissue, are critical markers for early detection of breast cancer, especially in non-palpable carcinomas. These microcalcifications, appearing as small white spots on mammograms, are challenging to identify due to potential confusion with other tissues. This study hypothesizes that a hybrid feature extraction approach combined with Convolutional Neural Networks (CNNs) can significantly enhance the detection and localization of microcalcifications in mammograms. The proposed algorithm employs Gabor, Prewitt, and Gray Level Co-occurrence Matrix (GLCM) kernels for feature extraction. These features are input to a CNN architecture designed with maxpooling layers, Rectified Linear Unit (ReLU) activation functions, and a sigmoid response for binary classification. Additionally, the Top Hat filter is used for precise localization of microcalcifications. The preprocessing stage includes enhancing contrast using the Volume of Interest Look-Up Table (VOI LUT) technique and segmenting regions of interest. The CNN architecture comprises three convolutional layers, three ReLU layers, and three maxpooling layers. The training was conducted using a balanced dataset of digital mammograms, with the Adam optimizer and binary cross-entropy loss function. Our method achieved an accuracy of 89.56%, a sensitivity of 82.14%, and a specificity of 91.47%, outperforming related works, which typically report accuracies around 85–87% and sensitivities between 76 and 81%. These results underscore the potential of combining traditional feature extraction techniques with deep learning models to improve the detection and localization of microcalcifications. This system may serve as an auxiliary tool for radiologists, enhancing early detection capabilities and potentially reducing diagnostic errors in mass screening programs. Full article
(This article belongs to the Special Issue Quantitative and Intelligent Analysis of Medical Imaging, 2nd Edition)
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19 pages, 4933 KiB  
Article
Deep Learning Model Based on You Only Look Once Algorithm for Detection and Visualization of Fracture Areas in Three-Dimensional Skeletal Images
by Young-Dae Jeon, Min-Jun Kang, Sung-Uk Kuh, Ha-Yeong Cha, Moo-Sub Kim, Ju-Yeon You, Hyeon-Joo Kim, Seung-Han Shin, Yang-Guk Chung and Do-Kun Yoon
Diagnostics 2024, 14(1), 11; https://doi.org/10.3390/diagnostics14010011 - 20 Dec 2023
Cited by 1 | Viewed by 2008
Abstract
Utilizing “You only look once” (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the [...] Read more.
Utilizing “You only look once” (YOLO) v4 AI offers valuable support in fracture detection and diagnostic decision-making. The purpose of this study was to help doctors to detect and diagnose fractures more accurately and intuitively, with fewer errors. The data accepted into the backbone are diversified through CSPDarkNet-53. Feature maps are extracted using Spatial Pyramid Pooling and a Path Aggregation Network in the neck part. The head part aggregates and generates the final output. All bounding boxes by the YOLO v4 are mapped onto the 3D reconstructed bone images after being resized to match the same region as shown in the 2D CT images. The YOLO v4-based AI model was evaluated through precision–recall (PR) curves and the intersection over union (IoU). Our proposed system facilitated an intuitive display of the fractured area through a distinctive red mask overlaid on the 3D reconstructed bone images. The high average precision values (>0.60) were reported as 0.71 and 0.81 from the PR curves of the tibia and elbow, respectively. The IoU values were calculated as 0.6327 (tibia) and 0.6638 (elbow). When utilized by orthopedic surgeons in real clinical scenarios, this AI-powered 3D diagnosis support system could enable a quick and accurate trauma diagnosis. Full article
(This article belongs to the Special Issue Quantitative and Intelligent Analysis of Medical Imaging, 2nd Edition)
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