Medical Imaging Research Articles

Low-dose computed tomography perceptual image quality assessment

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists’ assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists’ perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists’ assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096.

Prenatal screening and prenatal diagnosis clinical laboratory diagnostic pathway

Congenital defects and genetic diseases in the fetus are the focus of prenatal screening and prenatal diagnosis. Obstetrics and gynecology, pediatrics, medical imaging (ultrasound and magnetic resonance imaging), clinical laboratory, pathology, and other disciplines are mostly involved in this multidisciplinary work on maternal and infant health care, which aims to prevent birth defects in strict accordance with laws, regulations, and pertinent industry standards, such as the Notice of the National Health Commission on Issuing the Basic Standards for Prenatal Screening Technical Medical Institutions and the Basic Standards for Prenatal Diagnosis Technical Medical Institutions (Guowei Maternal and Child Letter [2019] No. 297). To further support the implementation of prenatal screening and diagnosis work and streamline workflow, this study has compiled the timing, inspection, and testing procedures of various projects in each link from the standpoint of the disease clinical laboratory diagnostic pathway. This approach improves communication amongst various disciplines in prenatal screening and diagnosis work and offers clinical service quality, and it also helps improve the standard of the birth population and prevent and controll severe birth defects.

"Dr. AI Will See You Now": How Do ChatGPT-4 Treatment Recommendations Align With Orthopaedic Clinical Practice Guidelines?

Artificial intelligence (AI) is engineered to emulate tasks that have historically required human interaction and intellect, including learning, pattern recognition, decision-making, and problem-solving. Although AI models like ChatGPT-4 have demonstrated satisfactory performance on medical licensing exams, suggesting a potential for supporting medical diagnostics and decision-making, no study of which we are aware has evaluated the ability of these tools to make treatment recommendations when given clinical vignettes and representative medical imaging of common orthopaedic conditions. As AI continues to advance, a thorough understanding of its strengths and limitations is necessary to inform safe and helpful integration into medical practice. (1) What is the concordance between ChatGPT-4-generated treatment recommendations for common orthopaedic conditions with both the American Academy of Orthopaedic Surgeons (AAOS) clinical practice guidelines (CPGs) and an orthopaedic attending physician's treatment plan? (2) In what specific areas do the ChatGPT-4-generated treatment recommendations diverge from the AAOS CPGs? Ten common orthopaedic conditions with associated AAOS CPGs were identified: carpal tunnel syndrome, distal radius fracture, glenohumeral joint osteoarthritis, rotator cuff injury, clavicle fracture, hip fracture, hip osteoarthritis, knee osteoarthritis, ACL injury, and acute Achilles rupture. For each condition, the medical records of 10 deidentified patients managed at our facility were used to construct clinical vignettes that each had an isolated, single diagnosis with adequate clarity. The vignettes also encompassed a range of diagnostic severity to evaluate more thoroughly adherence to the treatment guidelines outlined by the AAOS. These clinical vignettes were presented alongside representative radiographic imaging. The model was prompted to provide a single treatment plan recommendation. Each treatment plan was compared with established AAOS CPGs and to the treatment plan documented by the attending orthopaedic surgeon treating the specific patient. Vignettes where ChatGPT-4 recommendations diverged from CPGs were reviewed to identify patterns of error and summarized. ChatGPT-4 provided treatment recommendations in accordance with the AAOS CPGs in 90% (90 of 100) of clinical vignettes. Concordance between ChatGPT-generated plans and the plan recommended by the treating orthopaedic attending physician was 78% (78 of 100). One hundred percent (30 of 30) of ChatGPT-4 recommendations for fracture vignettes and hip and knee arthritis vignettes matched with CPG recommendations, whereas the model struggled most with recommendations for carpal tunnel syndrome (3 of 10 instances demonstrated discordance). ChatGPT-4 recommendations diverged from AAOS CPGs for three carpal tunnel syndrome vignettes; two ACL injury, rotator cuff injury, and glenohumeral joint osteoarthritis vignettes; as well as one acute Achilles rupture vignette. In these situations, ChatGPT-4 most often struggled to correctly interpret injury severity and progression, incorporate patient factors (such as lifestyle or comorbidities) into decision-making, and recognize a contraindication to surgery. ChatGPT-4 can generate accurate treatment plans aligned with CPGs but can also make mistakes when it is required to integrate multiple patient factors into decision-making and understand disease severity and progression. Physicians must critically assess the full clinical picture when using AI tools to support their decision-making. ChatGPT-4 may be used as an on-demand diagnostic companion, but patient-centered decision-making should continue to remain in the hands of the physician.

Diagnostic Reference Levels in PET Imaging at Chulabhorn Hospital, Thailand.

Diagnostic reference levels (DRLs) are an important tool for controlling radiation exposure and ensuring safety in medical applications. In Thailand, DRL data have been gathered and established for nuclear medicine diagnostics since 2021. However, there is a lack of information on PET imaging examinations. At the National Cyclotron and PET Scan Centre, Chulabhorn Hospital, radiopharmaceuticals are produced for medical imaging and research, and a wide range of PET/CT and PET/MRI examinations are performed. Our objective was to investigate the administered activity of radiopharmaceuticals in patients undergoing PET imaging, especially the existing data on DRLs in medical diagnostic imaging. Methods: This was a retrospective study on nuclear medicine patients at the National Cyclotron and PET Scan Centre in 2023. Statistical analysis, including the mean and the 75th percentile values, was conducted to determine DRLs according to the International Commission on Radiological Protection guidelines. Results: The center performed 8,711 PET/CT and PET/MRI studies with 13 protocols in 2023. The most commonly administered activity was 18F-FDG in oncology and neurology examinations, with DRLs of 186.11 and 136.16 MBq, respectively. These values were notably almost twice lower than several reports in other countries. Conclusion: There is a lack of comprehensive data on most DRLs for PET imaging at this center because of the nonwidespread use of several radiopharmaceuticals. However, the lower DRLs for 18F-FDG can highlight the need for ongoing investigation toward the establishment of local DRLs, as well as assurance on the safety and efficiency of radiation used in nuclear medicine.

Detection of Alzheimer’s disease using pre-trained deep learning models through transfer learning: a review

Due to the progress in image processing and Artificial Intelligence (AI), it is now possible to develop automated tool for the early detection and diagnosis of Alzheimer’s Disease (AD). Handcrafted techniques developed so far, lack generality, leading to the development of deep learning (DL) techniques, which can extract more relevant features. To cater for the limited labelled datasets and requirement in terms of high computational power, transfer learning models can be adopted as a baseline. In recent years, considerable research efforts have been devoted to developing machine learning-based techniques for AD detection and classification using medical imaging data. This survey paper comprehensively reviews the existing literature on various methodologies and approaches employed for AD detection and classification, with a focus on neuroimaging techniques such as structural MRI, PET, and fMRI. The main objective of this survey is to analyse the different transfer learning models that can be used for the deployment of deep convolution neural network for AD detection and classification. The phases involved in the development namely image capture, pre-processing, feature extraction and selection are also discussed in the view of shedding light on the different phases and challenges that need to be addressed. The research perspectives may provide research directions on the development of automated applications for AD detection and classification.

Multimodal medical image fusion based on interval gradients and convolutional neural networks

Many image fusion methods have been proposed to leverage the advantages of functional and anatomical images while compensating for their shortcomings. These methods integrate functional and anatomical images while presenting physiological and metabolic organ information, making their diagnostic efficiency far greater than that of single-modal images. Currently, most existing multimodal medical imaging fusion methods are based on multiscale transformation, which involves obtaining pyramid features through multiscale transformation. Low-resolution images are used to analyse approximate image features, and high-resolution images are used to analyse detailed image features. Different fusion rules are applied to achieve feature fusion at different scales. Although these fusion methods based on multiscale transformation can effectively achieve multimodal medical image fusion, much detailed information is lost during multiscale and inverse transformation, resulting in blurred edges and a loss of detail in the fusion images. A multimodal medical image fusion method based on interval gradients and convolutional neural networks is proposed to overcome this problem. First, this method uses interval gradients for image decomposition to obtain structure and texture images. Second, deep neural networks are used to extract perception images. Three methods are used to fuse structure, texture, and perception images. Last, the images are combined to obtain the final fusion image after colour transformation. Compared with the reference algorithms, the proposed method performs better in multiple objective indicators of QEN\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{EN}$$\\end{document}, QNIQE\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{NIQE}$$\\end{document}, QSD\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{SD}$$\\end{document}, QSSEQ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{SSEQ}$$\\end{document} and QTMQI\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$Q_{TMQI}$$\\end{document}.

Bacterial siderophores: diversity, uptake pathways and applications.

Iron is an essential nutrient for the growth, survival and virulence of almost all bacteria. To access iron, many bacteria produce siderophores, molecules with a high affinity for iron. Research has highlighted substantial diversity in the chemical structure of siderophores produced by bacteria, as well as remarkable variety in the molecular mechanisms involved in strategies for acquiring iron through these molecules. The metal-chelating properties of siderophores, characterized by their high affinity for iron and ability to chelate numerous other metals (albeit with lower affinity compared with iron), have also generated interest in diverse fields. Siderophores find applications in the environment, such as in bioremediation and agriculture, in which emerging and innovative strategies are being developed to address pollution and enhance nutrient availability for plants. Moreover, in medicine, siderophores could be used asa tool for novel antimicrobial therapiesand medical imaging, as well as in haemochromatosis, thalassemia or cancer treatments. This Review offers insights into the diversity of siderophores, highlighting their potential applications in environmental and medical contexts.

Harnessing AI for precision tonsillitis diagnosis: a revolutionary approach in endoscopic analysis.

Diagnosing and treating tonsillitis pose no significant challenge for otolaryngologists; however, it can increase the infection risk for healthcare professionals amidst the coronavirus pandemic. In recent years, with the advancement of artificial intelligence (AI), its application in medical imaging has also thrived. This research is to identify the optimal convolutional neural network (CNN) algorithm for accurate diagnosis of tonsillitis and early precision treatment. Semi-supervised learning with pseudo-labels used for self-training was adopted to train our CNN, with the algorithm including UNet, PSPNet, and FPN. A total of 485 pharyngoscopic images from 485 participants were included, comprising healthy individuals (133 cases), patients with the common cold (295 cases), and patients with tonsillitis (57 cases). Both color and texture features from 485 images are extracted for analysis. UNet outperformed PSPNet and FPN in accurately segmenting oropharyngeal anatomy automatically, with average Dice coefficient of 97.74% and a pixel accuracy of 98.12%, making it suitable for enhancing the diagnosis of tonsillitis. The normal tonsils generally have more uniform and smooth textures and have pinkish color, similar to the surrounding mucosal tissues, while tonsillitis, particularly the antibiotic-required type, shows white or yellowish pus-filled spots or patches, and shows more granular or lumpy texture in contrast, indicating inflammation and changes in tissue structure. After training with 485 cases, our algorithm with UNet achieved accuracy rates of 93.75%, 97.1%, and 91.67% in differentiating the three tonsil groups, demonstrating excellent results. Our research highlights the potential of using UNet for fully automated semantic segmentation of oropharyngeal structures, which aids in subsequent feature extraction, machine learning, and enables accurate AI diagnosis of tonsillitis. This innovation shows promise for enhancing both the accuracy and speed of tonsillitis assessments.

Bilateral internal carotid artery agenesis coexisting with bovine aortic arch: case report and literature review.

This case study aims to elucidate the rare occurrence of bilateral internal carotid artery agenesis combined with a bovine aortic arch. The main objectives are to explore the incidence, embryological origins, clinical manifestations, and associated anomalies of this unique vascular condition. The study involves a detailed investigation of a 55-year-old male with a history of recurrent transient ischemic attacks (TIAs) using MRI and CT scan. The patient's medical history, clinical examination, and imaging results were systematically analyzed to provide a thorough understanding of the anatomical variations. The main findings include the rare coexistence of bilateral ICA agenesis and a bovine aortic arch, making this the 39th documented occurrence of bilateral ICA agenesis and the first recorded instance of its association with a bovine aortic arch. Also, the study highlighted the demographic characteristics, clinical presentations, and associated anomalies observed in the 38 documented cases of bilateral internal carotid artery agenesis. This case report contributes valuable insights into the rarity of bilateral internal carotid artery agenesis and its unprecedented association with a bovine aortic arch. The findings emphasize the importance of heightened anatomical awareness in clinical practice, particularly. Recognizing and understanding such variations is crucial for accurate diagnosis, appropriate management, and improved patient outcomes. Further research in this area is warranted to deepen our understanding of these complex vascular anomalies.

Optimized Spatial Transformer for Segmenting Pancreas Abnormalities.

The precise delineation of the pancreas from clinical images poses a substantial obstacle in the realm of medical image analysis and surgical procedures. Challenges arise from the complexities of clinical image analysis and complications in clinical practice related to the pancreas. To tackle these challenges, a novel approach called the Spatial Horned Lizard Attention Approach (SHLAM) has been developed. As a result, a preprocessing function has been developed to examine and eliminate noise barriers from the trained MRI data. Furthermore, an assessment of the current attributes is conducted, followed by the identification of essential elements for forecasting the impacted region. Once the affected region has been identified, the images undergo segmentation. Furthermore, it is crucial to emphasize that the present study assigns 80% of the data for training and 20% for testing purposes. The optimal parameters were assessed based on precision, accuracy, recall, F-measure, error rate, Dice, and Jaccard. The performance improvement has been demonstrated by validating the method on various existing models. The SHLAM method proposed demonstrated an accuracy rate of 99.6%, surpassing that of all alternative methods.

Artificial intelligence techniques in liver cancer.

Hepatocellular Carcinoma (HCC), the most common primary liver cancer, is a significant contributor to worldwide cancer-related deaths. Various medical imaging techniques, including computed tomography, magnetic resonance imaging, and ultrasound, play a crucial role in accurately evaluating HCC and formulating effective treatment plans. Artificial Intelligence (AI) technologies have demonstrated potential in supporting physicians by providing more accurate and consistent medical diagnoses. Recent advancements have led to the development of AI-based multi-modal prediction systems. These systems integrate medical imaging with other modalities, such as electronic health record reports and clinical parameters, to enhance the accuracy of predicting biological characteristics and prognosis, including those associated with HCC. These multi-modal prediction systems pave the way for predicting the response to transarterial chemoembolization and microvascular invasion treatments and can assist clinicians in identifying the optimal patients with HCC who could benefit from interventional therapy. This paper provides an overview of the latest AI-based medical imaging models developed for diagnosing and predicting HCC. It also explores the challenges and potential future directions related to the clinical application of AI techniques.

Shape-intensity-guided U-net for medical image segmentation

Medical image segmentation has achieved impressive results thanks to U-Net or its alternatives. Yet, most existing methods perform segmentation by classifying individual pixels, tending to ignore the shape-intensity prior information. This may yield implausible segmentation results. Besides, the segmentation performance often drops greatly on unseen datasets. One possible reason is that the model is biased towards texture information, which varies more than shape information across different datasets. In this paper, we introduce a novel Shape-Intensity-Guided U-Net (SIG-UNet) for improving the generalization ability of variants of U-Net in segmenting medical images. Specifically, we adopt the U-Net architecture to reconstruct class-wisely averaged images that only contain the shape-intensity information. We then add an extra similar decoder branch with the reconstruction decoder for segmentation, and apply skip fusion between them. Since the class-wisely averaged image has no any texture information, the reconstruction decoder focuses more on shape and intensity features than the encoder on the original image. Therefore, the final segmentation decoder has less texture bias. Extensive experiments on three segmentation tasks of medical images with different modalities demonstrate that the proposed SIG-UNet achieves promising intra-dataset results while significantly improving the cross-dataset segmentation performance. The source code will be publicly available after acceptance.

Methylfenidaat als behandeling voor hypersomnolentie als gevolg van een thalamusinfarct

Methylphenidate as a treatment for hypersomnolence due to a thalamic infarction Fatigue after a stroke can persist for a long time and has consequences for the patient’s rehabilitation. Fatigue is also associated with a higher mortality and a reduced quality of life. Hypersomnia (or hypersomnolence) is much less common after a stroke, but can seriously hinder a person’s recovery and treatment. In this article, 2 cases of severely disabling hypersomnolence after an ischemic stroke in the thalamus are presented and the typical features and treatment options are discussed. The clinical course of thalamic lesions, as well as the contribution of the electroencephalogram and medical imaging are reported. The stimulating effect of methylphenidate on the central nervous system explains the beneficial influence on the hypersomnolence in the 2 cases described here. Treatment of hypersomnia improves a patient’s recovery and quality of life after a stroke.

Machine Learning Platform for Disease Diagnosis with Contrast CT Scans

Machine learning has gained significant recognition as a powerful approach for medical diagnosis using medical images. Among various medical imaging modalities, contrast-enhanced CT (CECT) is utilized to obtain additional diagnostic information that improves visualization and evaluation of certain abnormalities in the human body, as well as to observe temporal changes in lesions and tumors across different time phases. However, developing such medical diagnostic systems presents two significant challenges: high technical complexity and substantial development effort. This paper presents a software platform that effectively addresses these challenges. Specifically, we propose a unified software process that fully automates contrast-enhanced CT (CECT)-specific disease diagnosis, with key tasks performed by leveraging task-specific machine learning models to enhance accuracy. The platform incorporates a suite of specialized machine learning models into the diagnostic process, enabling precise diagnosis of lesions, malignancies, tumors, tumor characteristics, and temporal changes over phases. Moreover, the platform has been designed according to the Open–Closed Principle, allowing it to be applicable to a wide range of CECT-based diagnostic systems. The platform has been implemented in Python using the Scikit-learn and TensorFlow libraries. To validate its applicability and reusability, a hepatocellular carcinoma (HCC) diagnosis system has been implemented.

The Promise use of Hybrid PET/MRI

Hybrid PET/MRI is a cutting-edge imaging modality that integrates the molecular imaging capabilities of Positron Emission Tomography (PET) with the superior anatomical and functional imaging strengths of Magnetic Resonance Imaging (MRI). This integration offers significant promise in enhancing diagnostic accuracy, reducing radiation exposure, and providing comprehensive imaging in a single session. Clinically, hybrid PET/MRI has shown particular value in oncology, neurology, and cardiology, offering improved tumor characterization, early detection of neurodegenerative diseases, and detailed cardiac assessments. In the research domain, it provides critical insights into physiological and pathological processes, aiding drug development and the study of disease mechanisms. Despite its high cost, complexity, and limited availability, ongoing technological advancements and the integration of artificial intelligence are expected to expand its clinical applications and improve its practicality. Hybrid PET/MRI thus represents a promising advancement in medical imaging, with the potential to significantly impact personalized and precision medicine.

Building a Stronger Backbone: 3D Printing's Role in Treating Spinal Cord Conditions

AbstractSpinal cord injuries (SCIs) pose significant challenges as complete nerve regeneration remains limited. The demand for improved technologies in SCI treatment is evident. One such emerging technology is three-dimensional printing (3DP), which, coupled with advancements in medical imaging and bioengineering, has significantly enhanced precision in surgical procedures. This systematic review aims to explore 3DP as a treatment option for SCIs, examining its cost, efficacy, safety, and the associated technological constraints. A systematic search of Medline was conducted through PubMed for literature published since 2019. The search results were exported to Rayyan for abstract and full-text screening following predefined criteria. The risk of bias in the selected studies was assessed using the RoB2 tool and the Newcastle-Ottawa Scale. From a total of 89 articles screened, 11 studies met the eligibility criteria, collectively assessing 237 individuals with various types of SCIs, including lumbar degeneration, en bloc resection of thoracolumbar metastasis, adult spinal deformity, and cervical degeneration. These studies examined the utilization of 3DP devices such as hand orthosis, interbody fusion cages, lamellar titanium cages, artificial vertebral bodies, and others. Most of the reviewed studies reported positive treatment outcomes, with the actual procedure costs varying from $65 to $5,000. Recent literature shows positive outcomes in the use of 3DP technologies for SCIs, highlighting its potential for enhancing both surgical and nonsurgical interventions. These advancements usher in a new era in SCI treatment, providing enhanced precision and a wider range of treatment options, ultimately leading to more comprehensive and effective patient care.

Genomic profiling and immune phenotyping of neuroendocrine bladder cancer.

Neuroendocrine bladder cancer (NEBC) poses a formidable clinical challenge and attracts keen interests to explore immunotherapy as a viable treatment option. However, a comprehensive immunogenomic landscape has yet to be thoroughly investigated. Leveraging a long-term cohort of natural NEBC cases, we employed a multimodal approach integrating genomic (n = 19), transcriptomic (n = 3), single-cell RNA sequencing (n = 1), and immunohistochemical analyses (n = 34) to meticulously characterize the immunogenicity and immunotypes of primary NEBC tumors. Clinical, pathological, medical imaging, and treatment information was retrospectively retrieved and analyzed. Our study unveiled that despite a considerable mutational burden, NEBC was typically immunologically inactive, as manifested by 'immune-excluded' or 'immune-desert' microenvironment. Interestingly, a subset of mixed NEBC with concurrent urothelial bladder cancer (UBC) histology displayed an 'immune-infiltrated' phenotype with prognostic relevance. When compared to UBC, NEBC lesions were distinguished by a denser cellular composition and augmented peritumoral extracellular matrix, which might collectively impede lymphatic infiltration. As a result, single-agent immune checkpoint inhibitors demonstrated limited efficacy against NEBC, while pharmacologic immunostimulation with combination chemotherapy conferred a more favorable response. These new insights derived from genomic profiling and immune phenotyping pave the way for rational immunotherapeutic interventions in NEBC patients, with the potential to ultimately reduce mortality from this otherwise fatal disease.

Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection

The heightened prevalence of respiratory disorders, particularly exacerbated by a significant upswing in fatalities due to the novel coronavirus, underscores the critical need for early detection and timely intervention. This imperative is paramount, possessing the potential to profoundly impact and safeguard numerous lives. Medically, chest radiography stands out as an essential and economically viable medical imaging approach for diagnosing and assessing the severity of diverse Respiratory Disorders. However, their detection in Chest X-Rays is a cumbersome task even for well-trained radiologists owing to low contrast issues, overlapping of the tissue structures, subjective variability, and the presence of noise. To address these issues, a novel analytical model termed Exponential Pixelating Integral is introduced for the automatic detection of infections in Chest X-Rays in this work. Initially, the presented Exponential Pixelating Integral enhances the pixel intensities to overcome the low-contrast issues that are then polar-transformed followed by their representation using the locally invariant Mandelbrot and Julia fractal geometries for effective distinction of structural features. The collated features labeled Exponential Pixelating Integral with dually characterized fractal features are then classified by the non-parametric multivariate adaptive regression splines to establish an ensemble model between each pair of classes for effective diagnosis of diverse diseases. Rigorous analysis of the proposed classification framework on large medical benchmarked datasets showcases its superiority over its peers by registering a higher classification accuracy and F1 scores ranging from 98.46 to 99.45 % and 96.53–98.10 % respectively, making it a precise and interpretable automated system for diagnosing respiratory disorders.

Automatic detection and segmentation of lesions in 18F-FDG PET/CT imaging of patients with Hodgkin lymphoma using 3D dense U-Net.

The accuracy of automatic tumor segmentation in PET/computed tomography (PET/CT) images is crucial for the effective treatment and monitoring of Hodgkin lymphoma. This study aims to address the challenges faced by certain segmentation algorithms in accurately differentiating lymphoma from normal organ uptakes due to PET image resolution and tumor heterogeneity. Variants of the encoder-decoder architectures are state-of-the-art models for image segmentation. Among these kinds of architectures, U-Net is one of the most famous and predominant for medical image segmentation. In this study, we propose a fully automatic approach for Hodgkin lymphoma segmentation that combines U-Net and DenseNet architectures to reduce network loss for very small lesions, which is trained using the Tversky loss function. The hypothesis is that the fusion of these two deep learning models can improve the accuracy and robustness of Hodgkin lymphoma segmentation. A dataset with 141 samples was used to train our proposed network. Also, to test and evaluate the proposed network, we allocated two separate datasets of 20 samples. We achieved 0.759 as the mean Dice similarity coefficient with a median value of 0.767, and interquartile range (0.647-0.837). A good agreement was observed between the ground truth of test images against the predicted volume with precision and recall scores of 0.798 and 0.763, respectively. This study demonstrates that the integration of U-Net and DenseNet architectures, along with the Tversky loss function, can significantly enhance the accuracy of Hodgkin lymphoma segmentation in PET/CT images compared to similar studies.

CT and MRI Image Fusion via Coupled Feature-Learning GAN

The fusion of multimodal medical images, particularly CT and MRI, is driven by the need to enhance the diagnostic process by providing clinicians with a single, comprehensive image that encapsulates all necessary details. Existing fusion methods often exhibit a bias towards features from one of the source images, making it challenging to simultaneously preserve both structural information and textural details. Designing an effective fusion method that can preserve more discriminative information is therefore crucial. In this work, we propose a Coupled Feature-Learning GAN (CFGAN) to fuse the multimodal medical images into a single informative image. The proposed method establishes an adversarial game between the discriminators and a couple of generators. First, the coupled generators are trained to generate two real-like fused images, which are then used to deceive the two coupled discriminators. Subsequently, the two discriminators are devised to minimize the structural distance to ensure the abundant information in the original source images is well-maintained in the fused image. We further empower the generators to be robust under various scales by constructing a discriminative feature extraction (DFE) block with different dilation rates. Moreover, we introduce a cross-dimension interaction attention (CIA) block to refine the feature representations. The qualitative and quantitative experiments on common benchmarks demonstrate the competitive performance of the CFGAN compared to other state-of-the-art methods.