Clinical Medical Imaging Research Articles

Potential Role of Bone Scintigraphy in the Diagnosis of Calciphylaxis.

Nonosseous abnormalities are often seen on bone scans and can be related to a wide variety of pathology ranging across vascular, infection, and inflammatory etiology. Diffuse soft tissue radiotracer uptake on bone scans is typically attributed to renal or metabolic derangements. Calciphylaxis is the deposition of calcium in small blood vessels, skin, and other organs leading to vascular obstruction and skin necrosis. It is a rare disorder with unknown pathophysiology. Diagnosis of calciphylaxis is challenging and requires an interdisciplinary approach including clinical findings, laboratory results, medical imaging, and skin biopsy. An early diagnosis is important as the disease is associated with high morbidity and mortality. The purpose of this review article is to highlight the role of bone scintigraphy in the evaluation of calciphylaxis and to correlate the findings with other imaging modalities and histopathology.

Artificial Intelligence in Liver Diseases: Recent Advances.

Liver diseases cause a significant burden on public health worldwide. In spite of great advances during recent years, there are still many challenges in the diagnosis and treatment of liver diseases. During recent years, artificial intelligence (AI) has been widely used for the diagnosis, risk stratification, and prognostic prediction of various diseases based on clinical datasets and medical images. Accumulative studies have shown its performance for diagnosing patients with nonalcoholic fatty liver disease and liver fibrosis and assessing their severity, and for predicting treatment response and recurrence of hepatocellular carcinoma, outcomes of liver transplantation recipients, and risk of drug-induced liver injury. Herein, we aim to comprehensively summarize the current evidence regarding diagnostic, prognostic, and/or therapeutic role of AI in these common liver diseases.

Visualising Medical Research: Exploring the Influence of Infographics on Professional Dissemination.

This study explores the impact of infographics on the professional dissemination of medical research. Recognising the burgeoning volume of data in the medical domain, this research aims to bridge the gap by investigating the efficacy of infographics in rendering complex medical concepts understandable to diverse audiences, including policymakers, patients, and the public. The study uses a cross-sectional survey to assess medical professionals' familiarity with infographic design and data visualisation principles. Setting. The research targets medical professionals with published articles across various subfields, including Clinical Medicine, Epidemiology, Pharmacology, Healthcare Management, Medical Imaging, and Public Health. Data collection involves an online survey distributed to potential participants through professional networks and research institutions. The survey encompasses Likert-scale questions and demographic variables. Ethical considerations include obtaining approval from the institutional review board, ensuring participant consent, and maintaining data anonymity and confidentiality. Demographic analysis reveals a diverse participant profile, with 58.7% male and 41.3% female respondents, spanning various age groups, professional experiences, and geographic locations. Assessing familiarity with infographic design and data visualisation principles demonstrates respondents' proficiency in certain areas while highlighting potential areas for improvement. The study underscores the multifaceted benefits of infographics in research dissemination, as medical professionals perceive. Infographics can effectively convey various kinds of medical research information across diverse platforms and channels.

Analysis of DWT-DCT watermarking algorithm on digital medical imaging.

Over the past decade, the diagnostic information of the patients are digitally recorded and transferred. During the transmission of patients data, the security and authenticity of the information has to be ensured. Medical image watermarking technology has recently advanced because it can be used to conceal patient information while ensuring the authenticity. We propose a multiple watermarking method for securing clinical medical images. In this watermarking method, the quality feature property and private label property information are embedded as watermarks in the original image. Initially, medical images are divided into the region of interest (ROI) and non-interest region (NIR). Second, a two-level discrete wavelet transform (DWT) is applied to the ROI and the coefficients LL1 (LL2, LH2, HL2, HH2), LH1, HL1, and HH1 are generated. Watermarks are embedded using the DWT low-frequency sub-band (LL2) by quantizing the low-frequency coefficients. Next, the NIR is separated into non-overlapping blocks, and a discrete cosine transform (DCT) is applied for each block. The DCT coefficients of each block are sorted using the zigzag transform. For embedding, eight intermediate frequency coefficients are used. Finally, the feature information is embedded in the ROI, and the tag information is embedded in the NIR. The performance of the DWT-DCT watermarking method is calculated using the metrics of peak signal-to-noise ratio (PSNR), structural similarity index measure, and mean square error. The proposed method obtained the better PSNR value of 45.76dB. The proposed model works well for clinical medical images when compared with the existing techniques.

Artificial intelligence changing the future of healthcare diagnostics

Artificial intelligence (AI) is a computer’s capacity to carry out operations like speech and image recognition and decision-making that ordinarily require human intelligence. Healthcare is using AI to automate tasks such as medical image analysis and diagnosis that require high precision and accuracy. The healthcare industry is significantly impacted by the rapid development of machine learning algorithms, which are frequently implemented using deep learning, as well as the growth of digital data and computing power supported by improvements in hardware technologies. Significant progress has been made in the field of artificial intelligence in recent years and is now widely used in healthcare to automate a variety of tasks, which require a high degree of accuracy and precision. The creation of machine learning algorithms, which can learn from data and make predictions based on that learning, has made it possible to use AI in healthcare. Neural networks are used in deep learning, a subfield of machine learning, to simulate how the human brain functions. Crucial advances have been made in clinical decision support, drug discovery, and medical imaging. Furthermore, the rapid development of hardware technologies, such as graphics processing units, has allowed AI systems to process enormous amounts of data quickly and accurately. Due to this, AI-based tools and platforms can help healthcare professionals with tasks such as patient monitoring, disease diagnosis, and treatment planning.

Alumni, radiographers, clinical placement tutors and industry insights about current radiographers practice, competences and autonomy in western Switzerland

IntroductionRadiographers' profession is constantly evolving, which demands adaptation of education and training programs to build up medical imaging and radiation therapy professionals (MIRTPs) that provide healthcare to improve patient experience and outcomes. This study aimed to map radiographers’ practices, competences, and autonomy level in Western Switzerland. MethodsData was collected by 2 cross-sectional online surveys targeting Alumni, radiographers, clinical placement tutors and medical imaging equipment specialists from industry, with opened and closed-end questions. Descriptive statistics and thematic analysis were used to analyse the data. Results81 Alumni and 93 Chief-Radiographers, clinical tutors, practitioner-radiographers and industry answered the questionnaires. The competences considered as the most “acquired or completely acquired” by the Alumni were: adopt a reflective posture on practice (90.1 %; 73/81), adopt ethical behaviour (90.1 %; 73/81), carrying out and providing radiological services for diagnostic, therapeutic and preventive purposes (81.5 %; 68/81), adapting communication to the other surrounding persons (81.5 %; 66/81), and check compliance of procedures with standards (69.1 %; 56/81). Similar results were referred by Employers. The autonomy of the participant radiographers was considered as average, and it focuses only the preparation of the patient and the protocol optimisation. The development and integration of research is weak as well as the application of competences regarding professionalism. ConclusionsA better link between educational institutions and clinical practice can help on the integration of research and evidence-based on practice, necessary to progress the radiographers' profession in Western Switzerland. The autonomy needs to be further developed and leadership courses must be integrated in the curricula to facilitate the implementation of new approaches to reinforce radiographer's profession. Implications for practicePractice must be revised to integrate evidence-based; to facilitate research development, the managers need to increase support.

43 Machine learning-based differential diagnosis of psoriasis and atopic dermatitis using clinical data and medical images

43 Machine learning-based differential diagnosis of psoriasis and atopic dermatitis using clinical data and medical images

Pulmonary Histoplasmosis in People Living with Human Immunodeficiency Virus in French Guiana: Clinical Epidemiology, Medical Imaging and Prognostic

BackgroundHistoplasmosis is mainly described as a disseminated disease in people living with HIV (PLHIV). Compared to historical descriptions in immunocompetent individuals, knowledge is lacking on the detailed clinical and radiological findings and outcomes of pulmonary histoplasmosis (PH). Overlooked or misdiagnosed with other AIDS-defining condition, prognostic of PLHIV may be at risk because of inappropriate care.MethodsA retrospective multicentric study was conducted in PLHIV from French Guiana between January 1988 and October 2019. Proven PH were documented through mycological direct examination, culture, or histology. Patients with concomitant respiratory infections were excluded.ResultsAmong 65 patients, sex ratio M:F was 2.4 with a median age of 39 years [IQR 25–75%: 34–44]. Median CD4 count was 24 cells/mm3 [11–71], with histoplasmosis as the AIDS-defining condition in 88% and concomitant AIDS-defining conditions in 29%. Clinical findings were fever (89%), cough (58%), dyspnea (35%), expectoration (14%), and hemoptysis (5%). Sixty-one X-rays and 24 CT-scans were performed. On X-rays, an interstitial lung disease was mainly found (77%). On CT-scans, a nodular pattern was predominant (83%): mostly miliary disease (63%), but also excavated nodules (35%). Consolidations were present in 46%, associated with miliary disease in 21%. Thoracic lymphadenopathies were found in 58%, mainly hilar and symmetric (33%). Despite antifungal treatment, case-fatality rate at one month was 22%.ConclusionWhen faced with an interstitial lung disease on X-rays or a miliary pattern on CT-scans in advanced PLHIV, physicians in endemic areas, apart from tuberculosis or pneumocystosis, should include histoplasmosis as part of their differential diagnoses.

Head and neck cancer treatment outcome prediction: a comparison between machine learning with conventional radiomics features and deep learning radiomics.

Radiomics can provide in-depth characterization of cancers for treatment outcome prediction. Conventional radiomics rely on extraction of image features within a pre-defined image region of interest (ROI) which are typically fed to a classification algorithm for prediction of a clinical endpoint. Deep learning radiomics allows for a simpler workflow where images can be used directly as input to a convolutional neural network (CNN) with or without a pre-defined ROI. The purpose of this study was to evaluate (i) conventional radiomics and (ii) deep learning radiomics for predicting overall survival (OS) and disease-free survival (DFS) for patients with head and neck squamous cell carcinoma (HNSCC) using pre-treatment 18F-fluorodeoxuglucose positron emission tomography (FDG PET) and computed tomography (CT) images. FDG PET/CT images and clinical data of patients with HNSCC treated with radio(chemo)therapy at Oslo University Hospital (OUS; n = 139) and Maastricht University Medical Center (MAASTRO; n = 99) were collected retrospectively. OUS data was used for model training and initial evaluation. MAASTRO data was used for external testing to assess cross-institutional generalizability. Models trained on clinical and/or conventional radiomics features, with or without feature selection, were compared to CNNs trained on PET/CT images without or with the gross tumor volume (GTV) included. Model performance was measured using accuracy, area under the receiver operating characteristic curve (AUC), Matthew's correlation coefficient (MCC), and the F1 score calculated for both classes separately. CNNs trained directly on images achieved the highest performance on external data for both endpoints. Adding both clinical and radiomics features to these image-based models increased performance further. Conventional radiomics including clinical data could achieve competitive performance. However, feature selection on clinical and radiomics data lead to overfitting and poor cross-institutional generalizability. CNNs without tumor and node contours achieved close to on-par performance with CNNs including contours. High performance and cross-institutional generalizability can be achieved by combining clinical data, radiomics features and medical images together with deep learning models. However, deep learning models trained on images without contours can achieve competitive performance and could see potential use as an initial screening tool for high-risk patients.

MRI Image Fusion Based on Sparse Representation with Measurement of Patch-Based Multiple Salient Features

Multimodal medical image fusion is a fundamental, but challenging, problem in the fields of brain science research and brain disease diagnosis, as it is challenging for sparse representation (SR)-based fusion to characterize activity levels with a single measurement and not lose effective information. In this study, the Kronecker-criterion-based SR framework was applied for medical image fusion with a patch-based activity level, integrating salient features of multiple domains. Inspired by the formation process of vision systems, the spatial saliency was characterized by textural contrast (TC), composed of luminance and orientation contrasts, to promote the participation of more highlighted textural information in the fusion process. As a substitute for the conventional l1-norm-based sparse saliency, the sum of sparse salient features (SSSF) was used as a metric for promoting the participation of more significant coefficients in the composition of the activity level measurement. The designed activity level measurement was verified to be more conducive to maintaining the integrity and sharpness of detailed information. Various experiments on multiple groups of clinical medical images verified the effectiveness of the proposed fusion method in terms of both visual quality and objective assessment. Furthermore, this study will be helpful for the further detection and segmentation of medical images.

Medical imaging in rheumatoid arthritis: A review on deep learning approach.

Arthritis is a musculoskeletal disorder. Millions of people have arthritis, making it one of the most common joint disorders. Osteoarthritis (OA) and rheumatoid arthritis (RA) are the most common types of arthritis among the many different types available. Pain, stiffness, and inflammation are among the early signs of arthritis, which can progress to severe immobility at a later stage if left untreated. Although arthritis cannot be cured at any point in time, it can be managed if diagnosed and treated correctly. Clinical diagnostic and medical imaging methods are currently used to evaluate OA and RA, both debilitating conditions. This review is focused on deep learning approaches used by taking medical imaging (X-rays and magnetic resonance imaging) as input for the detection of RA.

Radiation-induced lung injury after breast cancer treatment: incidence in the CANTO-RT cohort and associated clinical and dosimetric risk factors.

Radiation-induced lung injury (RILI) is strongly associated with various clinical conditions and dosimetric parameters. Former studies have led to reducing radiotherapy (RT) doses to the lung and have favored the discontinuation of tamoxifen during RT. However, the monocentric design and variability of dosimetric parameters chosen have limited further improvement. The aim of our study was to assess the incidence of RILI in current practice and to determine clinical and dosimetric risk factors associated with RILI occurrence. Data from 3 out of the 10 top recruiting centers in CANTO-RT, a subset of the CANTO prospective longitudinal cohort (NCT01993498), were retrospectively analyzed for RILI occurrence. This cohort, which recruited invasive cT0-3 cN0-3 M0 breast cancer patients from 2012 to 2018, prospectively recorded the occurrence of adverse events by questionnaires and medical visits at the end of, and up to 60 months after treatment. RILI adverse events were defined in all patients by the association of clinical symptoms and compatible medical imaging. RILI was found in 38/1565 (2.4%) patients. Grade II RILI represented 15/38 events (39%) and grade III or IV 2/38 events (6%). There were no grade V events. The most frequently used technique for treatment was 3D conformational RT (96%). In univariable analyses, we confirmed the association of RILI occurrence with pulmonary medical history, absence of cardiovascular disease medical history, high pT and pN, chemotherapy use, nodal RT. All dosimetric parameters were highly correlated and had close predictive value. In the multivariable analysis adjusted for chemotherapy use and nodal involvement, pulmonary medical history (OR=3.05, p<0.01) and high V30 Gy (OR=1.06, p=0.04) remained statistically significant risk factors for RILI occurrence. V30 Gy >15% was significantly associated with RILI occurrence in a multivariable analysis (OR=3.07, p=0.03). Our study confirms the pulmonary safety of breast 3D RT in CANTO-RT. Further analyses with modern radiation therapy techniques such as IMRT are needed. Our results argue in favor of a dose constraint to the ipsilateral lung using V30 Gy not exceeding 15%, especially in patients presenting pulmonary medical history. Pulmonary disease records should be taken into account for RT planning.

Clinical review and analysis of artery of Percheron infarction

Artery of Percheron infarction is a rare one of the neurovascular structure variants of acute ischemic stroke characterized by bilateral paramedian thalamic infarcts (BTPI), with or without mesencephalic infarction. Due to the low occurrence rate and various clinical manifestations, the early diagnosis of this disease is often missed. In addition, it is also difficult to diagnose this disease in an early implementation phase because cranial imaging and intracranial vascular imaging may show negative results. So far, its clinical cases have been rarely reported. We systematically reviewed the clinical manifestations, imaging characteristics, anatomical basis, and differentiation diagnosis of the artery of Percheron infarction and reported on three patients and their clinical and radiological medical imaging characteristic findings. We found that most of the infarct lesions in patients with an AOP infraction could not be displayed within a few hours or could not be fully displayed, even the embolism events, most of which showed typical imaging lesions at late review. The decrease of transient consciousness was obvious over the course of the three patients, and the decrease of active communication was also a major feature. Among the three cases, one patient had unilateral upper eyelid ptosis and miosis; the initial symptom of another patient was dizziness; and the other person had decreased computing power after infarction. These clinical symptoms are easily ignored in the diagnosis and treatment of patients with AOP infarction. Therefore, reporting the three clinical cases mentioned above will provide assistance for subsequent research by increasing clinical data.

Generative adversarial network with radiomic feature reproducibility analysis for computed tomography denoising

Background:Most computed tomography (CT) denoising algorithms have been evaluated using image quality analysis (IQA) methods developed for natural image, which do not adequately capture the texture details in medical imaging. Radiomics is an emerging image analysis technique that extracts texture information to provide a more objective basis for medical imaging diagnostics, overcoming the subjective nature of traditional methods. By utilizing the difficulty of reproducing radiomics features under different imaging protocols, we can more accurately evaluate the performance of CT denoising algorithms. Method:We introduced radiomic feature reproducibility analysis as an evaluation metric for a denoising algorithm. Also, we proposed a low-dose CT denoising method based on a generative adversarial network (GAN), which outperformed well-known CT denoising methods. Results:Although the proposed model produced excellent results visually, the traditional image assessment metrics such as peak signal-to-noise ratio and structural similarity failed to show distinctive performance differences between the proposed method and the conventional ones. However, radiomic feature reproducibility analysis provided a distinctive assessment of the CT denoising performance. Furthermore, radiomic feature reproducibility analysis allowed fine-tuning of the hyper-parameters of the GAN. Conclusion:We demonstrated that the well-tuned GAN architecture outperforms the well-known CT denoising methods. Our study is the first to introduce radiomics reproducibility analysis as an evaluation metric for CT denoising. We look forward that the study may bridge the gap between traditional objective and subjective evaluations in the clinical medical imaging field.

A survey on adversarial attack and defense of deep learning models for medical image recognition

The advancement of hardware and computing power has enabled deep learning to be used in a variety of fields, particularly in AI medical applications in intelligent medicine and medical metaverse. Deep learning models are aiding in many clinical medical image analysis tasks, including fusion, registration, detection, classification and segmentation. In recent years, many deep learning-based approaches have been developed for medical image recognition, including classification and segmentation. However, these models are susceptible to adversarial samples, posing a threat to their real world application and making them unsuitable for clinical use. This paper provides an overview of adversarial attack strategies that have been proposed against medical image models and the defense methods used to protect them. We assessed the advantages and disadvantages of these strategies and compared their efficiency. We then examined the existing state and restrictions of research methods involving the adversarial attack and defense of deep learning models for medical image recognition. Additionally, several suggestions were given on how to enhance the robustness of medical image deep learning models in intelligent medicine and medical metaverse.

A comprehensive interpretation for medical VQA: Datasets, techniques, and challenges

Recent technological developments and improvement in the medical domain demands advancement, to address the issue of early disease detection. Also, the current pandemic has resulted in considerable progress of improvement in the medical domain, through online consultation by physicians for different diseases using clinical reports and medical images. A similar process is adopted in developing a Visual Question Answering (VQA) system in the medical field. In this paper, existing medical VQA datasets, appropriate techniques, suitable quantitative metrics, real time challenges and, the implementation of one VQA approach with algorithms and performance evaluation are discussed. The medical VQA datasets collected from multiple sources are represented in different perspectives (organwise, planewise, modality-type and abnormality-type) for a better understanding and visualization. Then the techniques used in VQA are subsequently grouped and explained, based on evolution, complexity in the dataset and the need for semantics in understanding the questions. In addition, the implementation of a VQA approach using VGGNet and LSTM is carried out for existing and improved datasets, and analyzed with accuracy and BLEU score metrics. The improved datasets, created through dataset reduction and augmentation approaches, resulted in better performance than the existing datasets. Finally, the challenges of the medical VQA domain are examined in terms of datasets, combining techniques, and modifying the parameters of existing performance metrics for future research.

Deformable 3D medical image registration with convolutional neural network and transformer

Deformable registration of medical images based on deep learning has been the research focus this year. Convolutional Neural Network (CNN) and the transformer are the most common backbone and have been shown to enhance registration accuracy. However, CNN lacks the ability to contact long-distance information, and the transformer lacks the ability to capture local information. Whichever subtle feature loss may lead to disastrous consequences in the analysis of clinical medicine. This paper presented a novel registration network named Information Complementation Network (ICN). We aim to improve the registration accuracy by complementing the lost information. Pure transformers can establish long-distance spatial information about the image. Proposed meshing patch embedding can minimize the loss of local information and expand the receptive field to extract long-distance information. The dual-path decoder in ICN is designed to restore information furthest. We experimented on 3D brain MRI data and quantitatively compared several excellent registration models. Compared with conventional methods, the dice coefficient increased by 3%. Compared with the advanced methods, the dice coefficient increased by 1%. The number of foldings was reduced by about 50% without any loss of registration accuracy. Each evaluation metric of the trained models on liver CT images was higher than other methods. By fully complementing the lost or invalid information, ICN achieved higher registration accuracy and smoother deformation field. The innovation and contribution of this paper have the potential to be applied to clinical research or medical image processing.

Exploration and Evaluation of the Long-term Effects of Diverse Online Teaching Methods of Basic Clinical Courses in Clinical Medicine During the COVID-19 Pandemic

To explore and evaluate the long-term effects of diverse online teaching methods to minimize the negative influences of online teaching of basic courses in clinical medicine during the COVID-19 pandemic. Forty-nine students in grade 2017 majoring in clinical medicine (five-year program) were enrolled in this study (study group). They received online teaching of six basic clinical courses, including diagnostics, basic clinical skills, general surgery, medical imaging, and doctor‒patient communication, from March to July 2020. The study first introduced the process of online teaching, especially basic clinical skills such as history taking, physical examination, internal punctures, electrocardiography and so on. Then, they reviewed the schedules to reinforce these skills when the students returned to school. The final exam scores of four clinical courses, including internal medicine, surgery, obstetrics and gynaecology, and paediatrics, and the scores of graduation examinations were used to evaluate the long-term effects of online teaching. These scores were compared with students in grade 2014 who learned through offline teaching (control group). After graduation examinations, questionnaires were distributed to understand the influence of online teaching on learning clinical courses, internships and postgraduate entrance examinations. The results showed that the scores of internal medicine part I and surgery part II in the study group were higher than those in the control group, and the differences were statistically significant (P &lt; 0.05). Differences were not statistically significant in scores of internal medicine part II, surgery part I, obstetrics and gynaecology, and paediatrics between the two groups (P &gt; 0.05). Two students postponed graduation in grade 2017, so 47 students took part in the graduation examination. The scores for knotting, dressing change, and cardiopulmonary resuscitation in the study group were significantly lower than those in the control group (P &lt; 0.05). For the three items internal punctures, four-step palpation in obstetrics, and assessment of children's growth and development, the scores in the study group were higher than those in the control group, and the differences were statistically significant (P &lt;0.05). There were no statistically significant differences between these two groups in the three items of history taking, physical examination and disinfection (P &gt; 0.05). All 47 participants in the study group completed the questionnaire survey, with a response rate of 100%. Twenty students thought that the online learning experience had a great influence on learning clinical courses such as internal medicine, surgery, obstetrics and gynaecology and paediatrics, whereas 27 students thought it had a slight influence. There were 23 students who thought online learning had a great influence and 24 students who thought it had a slight influence when attending the internship qualification examination. Thirteen students thought that the influence on the internship was great, 30 students thought it was slight, and 4 thought that online learning had no influence. Regarding which course had the most influence on the internship, 36 students thought it was diagnostics, 7 students thought it was general surgery, and 3 students thought it was other courses. Among the 40 students who participated in the entrance examination for a master's degree, 2 thought the online learning experience had a great influence, 19 thought it had a slight influence, and 11 students thought it had no influence. Among the six courses that influenced the re-examination for postgraduate students, 16 students thought the most influential course was diagnostics, 5 students chose general surgery, and 1 student thought it was another course. Exploration of diverse online teaching methods is helpful and successful for mastering clinical knowledge. However, online teaching had a great influence on some basic clinical skills that require more practice. The strategy used to reinforce these skills after returning to school makes up for the shortage of these practical skills in online courses to a certain extent.

E-DU: Deep neural network for multimodal medical image segmentation based on semantic gap compensation

BackgroundU-Net includes encoder, decoder and skip connection structures. It has become the benchmark network in medical image segmentation. However, the direct fusion of low-level and high-level convolution features with semantic gaps by traditional skip connections may lead to problems such as fuzzy generated feature maps and target region segmentation errors. ObjectiveWe use spatial enhancement filtering technology to compensate for the semantic gap and propose an enhanced dense U-Net (E-DU), aiming to apply it to multimodal medical image segmentation to improve the segmentation performance and efficiency. MethodsBefore combining encoder and decoder features, we replace the traditional skip connection with a multiscale denoise enhancement (MDE) module. The encoder features need to be deeply convolved by the spatial enhancement filter and then combined with the decoder features. We propose a simple and efficient deep full convolution network structure E-DU, which can not only fuse semantically various features but also denoise and enhance the feature map. ResultsWe performed experiments on medical image segmentation datasets with seven image modalities and combined MDE with various baseline networks to perform ablation studies. E-DU achieved the best segmentation results on evaluation indicators such as DSC on the U-Net family, with DSC values of 97.78, 97.64, 95.31, 94.42, 94.93, 98.85, and 98.38 (%), respectively. The addition of the MDE module to the attention mechanism network improves segmentation performance and efficiency, reflecting its generalization performance. In comparison to advanced methods, our method is also competitive. ConclusionOur proposed MDE module has a good segmentation effect and operating efficiency, and it can be easily extended to multiple modal medical segmentation datasets. Our idea and method can achieve clinical multimodal medical image segmentation and make full use of image information to provide clinical decision support. It has great application value and promotion prospects.

Automated screening of glaucoma stages from retinal fundus images using BPS and LBP based GLCM features

AbstractGlaucoma is an eye disease in which the retinal nerve fibers are irreversibly damaged. Early identification of glaucoma is essential because it may slow the progression of the illness. The clinical treatments and medical imaging methods that are currently available are all manual and require expert supervision. An automated glaucoma diagnosis system that is fast, accurate, and helps to reduce the load on professionals is necessary for mass screening. In our proposed work, a novel approach based on bit‐plane slicing (BPS), local binary pattern (LBP), and gray‐level co‐occurrence matrix (GLCM) is used. First, fundus images are separated into channels like red, green, and blue, and these separated channels are split into plans using BPS. Then, LBP images are obtained from selected green channel images. Second, we extract features based on GLCM from LBP images. Finally, using a least‐squares support vector machine classifier, the higher ranked features are employed to classify glaucoma stages. According to the findings of the experiments, our model outperformed state‐of‐the‐art approaches for glaucoma classification. Using 10‐fold cross‐validation, this model achieved an improved classification accuracy of 95.04%, specificity of 96.37%, and sensitivity of 93.77%. We conducted many relative experiments with deep learning and traditional machine learning‐based models to test our proposed methodology. Compared to existing glaucoma classification approaches, the new method has been shown to be more efficient.