A Systematic Review on Caries Detection, Classification, and Segmentation from X-Ray Images: Methods, Datasets, Evaluation, and Open Opportunities
AbstractDental caries occurs from the interaction between oral bacteria and sugars, generating acids that damage teeth over time. The importance of X-ray images for detecting oral problems is undeniable in dentistry. With technological advances, it is feasible to identify these lesions using techniques such as deep learning, machine learning, and image processing. Therefore, the survey and systematization of these methods are essential to determining the main computational approaches for identifying caries in X-ray images. In this systematic review, we investigated the primary computational methods used for classifying, de...
Source: Journal of Digital Imaging - March 1, 2024 Category: Radiology Source Type: research
Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs —A Preliminary Study
AbstractThus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLI...
Source: Journal of Digital Imaging - March 1, 2024 Category: Radiology Source Type: research
Auto-BCS: A Hybrid System for Real-Time Breast Cancer Screening from Pathological Images
AbstractBreast cancer is recognized as a prominent cause of cancer-related mortality among women globally, emphasizing the critical need for early diagnosis resulting improvement in survival rates. Current breast cancer diagnostic procedures depend on manual assessments of pathological images by medical professionals. However, in remote or underserved regions, the scarcity of expert healthcare resources often compromised the diagnostic accuracy. Machine learning holds great promise for early detection, yet existing breast cancer screening algorithms are frequently characterized by significant computational demands, renderi...
Source: Journal of Digital Imaging - March 1, 2024 Category: Radiology Source Type: research
Correlation Between Cognitive Impairment and Lenticulostriate Arteries: A Clinical and Radiomics Analysis
This study aims to investigate correlations between cognitive impairment and LSA through clinical and radiomics features analysis. We retrospectively included 102 patients (mean age 62.5 ±10.3 years, 60 males), including 58 with mild cognitive impairment (MCI) and 44 with moderate or severe cognitive impairment (MSCI). The MRI images of these patients were subjected toz-score preprocessing, manual regions of interest (ROI) outlining, feature extraction (pyradiomics), feature selection [max-relevance and min-redundancy (mRMR), least absolute shrinkage and selection operator (LASSO), and univariate analysis], model construc...
Source: Journal of Digital Imaging - March 1, 2024 Category: Radiology Source Type: research
From Pixels to Prognosis: A Survey on AI-Driven Cancer Patient Survival Prediction Using Digital Histology Images
AbstractSurvival analysis is an integral part of medical statistics that is extensively utilized to establish prognostic indices for mortality or disease recurrence, assess treatment efficacy, and tailor effective treatment plans. The identification of prognostic biomarkers capable of predicting patient survival is a primary objective in the field of cancer research. With the recent integration of digital histology images into routine clinical practice, a plethora ofArtificialIntelligence (AI)-based methods for digital pathology has emerged in scholarly literature, facilitating patient survival prediction. These methods ha...
Source: Journal of Digital Imaging - March 1, 2024 Category: Radiology Source Type: research
SC-Unext: A Lightweight Image Segmentation Model with Cellular Mechanism for Breast Ultrasound Tumor Diagnosis
AbstractAutomatic breast ultrasound image segmentation plays an important role in medical image processing. However, current methods for breast ultrasound segmentation suffer from high computational complexity and large model parameters, particularly when dealing with complex images. In this paper, we take the Unext network as a basis and utilize its encoder-decoder features. And taking inspiration from the mechanisms of cellular apoptosis and division, we design apoptosis and division algorithms to improve model performance. We propose a novel segmentation model which integrates the division and apoptosis algorithms and i...
Source: Journal of Digital Imaging - February 29, 2024 Category: Radiology Source Type: research
A Multiparametric MRI-based Radiomics Model for Stratifying Postoperative Recurrence in Luminal B Breast Cancer
This study aims to develop an MRI-based radiomics model to assess the likelihood of recurrence in luminal B breast cancer. The study analyzed medical images and clinical data from 244 patients with luminal B breast cancer. Of 244 patients, 35 had experienced recurrence and 209 had not. The patients were randomly divided into the training set (51.5 ± 12.5 years old;n = 171) and the test set (51.7 ± 11.3 years old;n = 73) in a ratio of 7:3. The study employed univariate and multivariate Cox regression along with the least absolute shrinkage and selection operator (LASSO) regression methods to select rad...
Source: Journal of Digital Imaging - February 29, 2024 Category: Radiology Source Type: research
Privacy-Preserving Breast Cancer Classification: A Federated Transfer Learning Approach
This study proposes a novel transfer learning approach integrated into a federated learning framework to solve the limitations of limited labeled data and data privacy in collaborative healthcare settings. For breast cancer classification, the mammography and MRO images were gathered from three different medical centers. Federated learning, an emerging privacy-preserving paradigm, empowers multiple medical institutions to jointly train the global model while maintaining data decentralization. Our proposed methodology capitalizes on the power of pre-trained ResNet, a deep neural network architecture, as a feature extractor....
Source: Journal of Digital Imaging - February 29, 2024 Category: Radiology Source Type: research
Deep Learning Radiomics Analysis of CT Imaging for Differentiating Between Crohn ’s Disease and Intestinal Tuberculosis
This study aimed to develop and evaluate a CT-based deep learning radiomics model for differentiating between Crohn ’s disease (CD) and intestinal tuberculosis (ITB). A total of 330 patients with pathologically confirmed as CD or ITB from the First Affiliated Hospital of Zhengzhou University were divided into the validation dataset one (CD: 167; ITB: 57) and validation dataset two (CD: 78; ITB: 28). Based on th e validation dataset one, the synthetic minority oversampling technique (SMOTE) was adopted to create balanced dataset as training data for feature selection and model construction. The handcrafted and deep learni...
Source: Journal of Digital Imaging - February 29, 2024 Category: Radiology Source Type: research
A Hybrid Framework of Dual-Domain Signal Restoration and Multi-depth Feature Reinforcement for Low-Dose Lung CT Denoising
AbstractLow-dose computer tomography (LDCT) has been widely used in medical diagnosis. Various denoising methods have been presented to remove noise in LDCT scans. However, existing methods cannot achieve satisfactory results due to the difficulties in (1) distinguishing the characteristics of structures, textures, and noise confused in the image domain, and (2) representing local details and global semantics in the hierarchical features. In this paper, we propose a novel denoising method consisting of (1) a 2D dual-domain restoration framework to reconstruct noise-free structure and texture signals separately, and (2) a 3...
Source: Journal of Digital Imaging - February 29, 2024 Category: Radiology Source Type: research
Deep Learning Glioma Grading with the Tumor Microenvironment Analysis Protocol for Comprehensive Learning, Discovering, and Quantifying Microenvironmental Features
AbstractGliomas are primary brain tumors that arise from neural stem cells, or glial precursors. Diagnosis of glioma is based on histological evaluation of pathological cell features and molecular markers. Gliomas are infiltrated by myeloid cells that accumulate preferentially in malignant tumors, and their abundance inversely correlates with survival, which is of interest for cancer immunotherapies. To avoid time-consuming and laborious manual examination of images, a deep learning approach for automatic multiclass classification of tumor grades was proposed. As an alternative way of investigating characteristics of brain...
Source: Journal of Digital Imaging - February 27, 2024 Category: Radiology Source Type: research
URI-CADS: A Fully Automated Computer-Aided Diagnosis System for Ultrasound Renal Imaging
AbstractUltrasound is a widespread imaging modality, with special application in medical fields such as nephrology. However, automated approaches for ultrasound renal interpretation still pose some challenges: (1) the need for manual supervision by experts at various stages of the system, which prevents its adoption in primary healthcare, and (2) their limited considered taxonomy (e.g., reduced number of pathologies), which makes them unsuitable for training practitioners and providing support to experts. This paper proposes a fully automated computer-aided diagnosis system for ultrasound renal imaging addressing both of t...
Source: Journal of Digital Imaging - February 27, 2024 Category: Radiology Source Type: research
HBMD-Net: Feature Fusion Based Breast Cancer Classification with Class Imbalance Resolution
AbstractBreast cancer, a widespread global disease, represents a significant threat to women ’s health and lives, ranking as one of the most vulnerable malignant tumors they face. Many researchers have proposed their computer-aided diagnosis systems for classifying breast cancer. The majority of these approaches primarily utilize deep learning (DL) methods, which are not entirely reliable . These approaches overlook the crucial necessity of incorporating both local and global information for precise tumor detection, despite the fact that the subtle nuances are crucial for precise breast cancer classification. In addition...
Source: Journal of Digital Imaging - February 26, 2024 Category: Radiology Source Type: research
OralEpitheliumDB: A Dataset for Oral Epithelial Dysplasia Image Segmentation and Classification
This study introduces an annotated public dataset of oral epithelial dysplasia tissue images. The dataset includes 456 images acquired from 30 mouse tongues. The images were categorized among the lesion grades, with nuclear structures manually marked by a trained specialist and validated by a pathologist. Also, experiments were carried out in order to illustrate the potential of the proposed dataset in classification and segmentation processes commonly explored in the literature. Convolutional neural network (CNN) models for semantic and instance segmentation were employed on the images, which were pre-processed with stain...
Source: Journal of Digital Imaging - February 26, 2024 Category: Radiology Source Type: research
LAMA: Lesion-Aware Mixup Augmentation for Skin Lesion Segmentation
AbstractDeep learning can exceed dermatologists ’ diagnostic accuracy in experimental image environments. However, inaccurate segmentation of images with multiple skin lesions can be seen with current methods. Thus, information present in multiple-lesion images, available to specialists, is not retrievable by machine learning. While skin lesion images generally capture a single lesion, there may be cases in which a patient’s skin variation may be identified as skin lesions, leading to multiple false positive segmentations in a single image. Conversely, image segmentation methods may find only one region and may not cap...
Source: Journal of Digital Imaging - February 26, 2024 Category: Radiology Source Type: research
