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Abstract Cardiac hemangioma is a rare benign tumour that presents diagnostic challenges due to its variable clinical symptoms, imaging features, and locations. This study proposes a novel segmentation method based on a Convolutional Neural Network (CNN) and Transformer integration, with Swin‐UNet as the core model. We incorporated a U‐shaped convolutional neural network block into the original jump connection of Swin‐UNet. The Binary Cross Entropy Loss (BCE Loss) algorithm was added, and the learning rate decay algorithm was modified to select the appropriate one by comparing loss values. This paper utilizes the publicly available cardiac angioma dataset in AI Studio, consisting of 215 images for training and testing. To evaluate the effectiveness of the proposed model, this paper demonstrates its optimality through ablation experiments and comparisons with other mainstream models. The comparison experiments show that this model improves Dice by approximately 12%, HD95 by approximately 4.7 mm, Accuracy by approximately 6.1%, and F1 score by 0.11 compared to models such as UNet, UNet++, and Deeplabv3+, etc. For the recently proposed SOTO models, such as TransUNet, Swin‐UNet, and MultiResUnet, the Dice score improved by about 1.2%, HD95 reduced by about 1mm, Accuracy improved by about 0.3%, and F1 score improved by 0.015.

In the field of medicine there is a need for the automatic detection of retinal disorders. Blindness in older persons is primarily caused by Central Retinal Vein Occlusion (CRVO). It results in rapid, irreversible eyesight loss, therefore, it is essential to identify and address CRVO as soon as feasible. Hemorrhages, which can differ in size, pigment, and shape from dot-shaped to flame hemorrhages, are one of the earliest symptoms of CRVO. The early signs of CRVO are, hemorrhages, however, so mild that ophthalmologists must dynamically observe such indicators in the retina image known as the fundus image, which is a challenging and time-consuming task. It is also difficult to segment hemorrhages since the blood vessels and hemorrhages (HE) have the same color properties also there is no particular shape for hemorrhages and it scatters all over the fundus image. A challenging study is needed to extract the characteristics of vein deformability and dilatation. Furthermore, the quality of the captured image affects the efficacy of feature Identification analysis. In this paper, a deep learning approach for CRVO extraction is proposed.

Background: Glaucoma is a common causes of blindness. The associated elevation in intra ocular pressure leads to progressive degeneration of the optic nerve and resultant structural changes with functional failure of the visual field. Since, glaucoma is asymptomatic in the early stages and the associated vision loss is irreparable, its early detection and timely medical treatment is essential to prevent further visual damage. Objective: This paper presents a novel method for glaucoma detection using digital fundus image and optical coherence tomography (OCT) image. Method: The first section focuses on the features such as cup to disc ratio (CDR) and the inferior superior nasal temporal (ISNT) ratio which were obtained from fundus images.The above features were used for classifying the normal and glaucoma condition using back propagation neural network (BPN) and Support Vector Machine (SVM) classifiers. In the second part of the article, features such as CDR and two novel features, cup depth and retinal thickness were obtained from the OCT image. These features were evaluated by the BPN and SVM classifier. Results and Conclusion: The combined features from fundus and OCT images were analyzed. The system proposed here is able to classify glaucoma automatically. The accuracy of BPN and SVM Classifiers was 90.76% and 96.92% respectively.

Diabetic Retinopathy is a effect of diabetes. It results abnormalities in the retinal blood vessels. The abnormalities can cause blurry vision and blindness. Automatic retinal blood vessels segmentation on retinal image can detect abnormalities in these blood vessels, actually resulting in faster and more accurate segmentation results. The paper proposed an automatic blood vessel segmentation method that combined Otsu Thresholding with image enhancement techniques. In image enhancement, it combined CLAHE with Top-hat transformation to improve image quality. The study used DRIVE dataset that provided retinal image data. The image data in dataset was generated by the fundus camera. The CLAHE and Top-hat transformation methods were applied to rise the contrast and reduce noise on the image. The images that had good quality could help the segmentation process to find blood vessels in retinal images appropriately by a computer. It improved the performance of the segmentation method for detecting blood vessels in retinal image. Otsu Thresholding was used to segment blood vessel pixels and other pixels as background by local threshold. To evaluation performance of the proposed method, the study has been measured accuracy, sensitivity, and specificity. The DRIVE dataset's study results showed that the averages of accuracy, sensitivity, and specificity values were 94.7%, 72.28%, and 96.87%, respectively. It indicated that the proposed method was successful and well to work on blood vessels segmentation retinal images especially for thick blood vessels.

The purpose of the comments is the analysis of Professor Dmitry D. Pletnev's article Hypertension (experience in analyzing its genesis) from the point of view of modern ideas about arterial hypertension. Currently, a small number of publications of Professor D.D. Pletnev, a Russian scientist and clinician, unjustly convicted in 1937, have been preserved. The small work includes not only a deep and critical analysis of the pathogenetic mechanisms of this disease, but also an assessment of diagnostic methods, as well as a detailed description of the clinical picture. The paper, published in 1935, remains relevant and of interest to clinicians and medical historians.

Considerable evidence shows that oxidative stress exists in the pathophysiological process of female reproductive system diseases. At present, there have been many studies on oxidative stress of placenta during pregnancy, especially for preeclampsia. However, studies that directly focus on the effects of oxidative stress on blood vessels at the maternal-fetal interface and their associated possible outcomes are still incomplete and ambiguous. To provide an option for early clinical prediction and therapeutic application of oxidative stress in female reproductive system diseases, this paper briefly describes the composition of the maternal-fetal interface and the molecular mediators produced by oxidative stress, focuses on the sources of oxidative stress and the signaling pathways of oxidative stress at the maternal-fetal interface, expounds the adverse consequences of oxidative stress on blood vessels, and deeply discusses the relationship between oxidative stress and some pregnancy complications and other female reproductive system diseases.

Abstract Objective This paper was a anatomical radiographic study of distance between lumbar bi-cortical pedicle screws (BPSs) and anterior large vessels (ALVs) in patients with lumbar spondylolisthesis, and to provide clinical basis for evaluating the safety of bi-cortical pedicle screw implantation during lumbar spondylolisthesis. Methods Complete Computed tomography (CT) data of 104 patients with grade I lumbar spondylolisthesis (L4 52 and L5 52) and 107 non-spondylolisthesis patients (control group) were collected in this study. The distances between lumbar 4,5(L4,5) and sacrum 1(S1) BPSs and ALVs (abdominal aorta, inferior vena cava, left and right common iliac artery, internal and external iliac artery) were respectively measured at different transverse screw angles (TSAs) (L4:5°,10°; L5:10°,15°; S1:0°,5°,10°) and analyzed by SPSS (v25.0). There were three types of distances from the anterior vertebral cortex (AVC) to the ALVs (DAVC-ALV): DAVC-ALV N, DAVC-ALV ≥ 0.50 cm, and DAVC-ALV

A hybrid segmentation algorithm is proposed is this paper to extract the blood vessels from the fundus image of retina. Fundus camera captures the posterior surface of the eye and the captured images are used to diagnose diseases, like Diabetic Retinopathy, Retinoblastoma, Retinal haemorrhage, etc. Segmentation or extraction of blood vessels is highly required, since the analysis of vessels is crucial for diagnosis, treatment planning, and execution of clinical outcomes in the field of ophthalmology. It is derived from the literature review that no unique segmentation algorithm is suitable for images of different eye-related diseases and the degradation of the vessels differ from patient to patient. If the blood vessels are extracted from the fundus images, it will make the diagnosis process easier. Hence, this paper aims to frame a hybrid segmentation algorithm exclusively for the extraction of blood vessels from the fundus image. The proposed algorithm is hybridized with morphological operations, bottom hat transform, multi-scale vessel enhancement (MSVE) algorithm, and image fusion. After execution of the proposed segmentation algorithm, the area-based morphological operator is applied to highlight the blood vessels. To validate the proposed algorithm, the results are compared with the ground truth of the High-Resolution Fundus (HRF) images dataset. Upon comparison, it is inferred that the proposed algorithm segments the blood vessels with more accuracy than the existing algorithms.

White adipose tissue hyperplasia has been shown to be crucial for handling excess energy in healthy ways. Though adipogenesis mechanisms have been underscored in vitro, we lack information on how tissue and systemic factors influence the differentiation of new adipocytes. While this could be studied in zebrafish, adipocyte identification currently relies on neutral lipid labeling, thus precluding access to cells in early stages of differentiation. Here we report the generation and analysis of a zebrafish line with the transgene fabp4a(-2.7):EGFPcaax. In vivo confocal microscopy of the pancreatic and abdominal visceral depots of transgenic larvae, revealed the presence of labeled mature adipocytes as well as immature cells in earlier stages of differentiation. Through co-labeling for blood vessels, we observed a close interaction of differentiating adipocytes with endothelial cells through cell protrusions. Finally, we implemented hyperspectral imaging and spectral phasor analysis in Nile Red-labeled transgenic larvae and revealed the lipid metabolic transition towards neutral lipid accumulation of differentiating adipocytes. Altogether our work presents the characterization of a novel adipocyte-specific label in zebrafish and uncovers previously unknown aspects of in vivo adipogenesis. This article has an associated First Person interview with the first author of the paper.

Skin aging is a complex process that causes morphologic variations. Some of these variations have been hypothesized to be involved in skin cancer development. This paper reviews current knowledge of the features of aged skin as seen with reflectance confocal microscopy (RCM). Basic principles of the technique are described, and the RCM features of healthy skin and skin cancer are briefly discussed. Moreover, the RCM features at different layers of young and elderly skin are described, as are the variations that occur with passing years and in relation to sun exposure that contribute to photoaging and the development of skin cancer. RCM enables the noninvasive evaluation, at quasi-histologic resolution, of aging-related skin changes, some of which are shared with skin cancer; this ability helps avoid skin biopsy. Further research is needed to understand the relation between skin aging and skin cancer development.

In this paper, we present a new approach to contrast-agent-free angiographic visualization in optical coherence tomography (OCT). The proposed approach has much in common with imaging of local interframe strains in OCT-based elastography and utilizes the fact that the interframe motion of blood particles leads to discontinuity of strains within the vessel cross section. By this reasoning, we call this approach “elastography-inspired”. Here, we first elucidate the essence and main features of the elastography-inspired approach using numerical simulation of OCT data. The simulations allow one to introduce both moving scatterers imitating blood flow in vessels as well as various masking motions imitating natural motions of living “solid” tissue surrounding the vessels. Second, using real OCT signals, we present comparative results of angiographic processing using the proposed elastography-inspired approach and a realization of OCA based on high-pass filtering of temporal variability of a series of OCT B-scans. The two methods can use the same initial dataset and the high-pass filtering OCA has already been routinely applied in both animal experiments and on patients. The new elastography-inspired method has a similar computational efficiency, and it is intrinsically able to compensate spatially-inhomogeneous masking tissue motions and demonstrates high robustness with respect to motion artefacts. Thus, the new approach looks very promising for enabling wider application of OCA in both laboratory studies on animals and, most importantly, for wider clinical applications on patients.

Glaucoma is the second leading disease in the world that damages Optic Nerve Head (ONH) and causes permanent blindness. Glaucoma can be diagnosed using retinal fundus images. The detection and diagnosis of glaucoma is important in the field of Ophthalmology. This paper aimed to provide a brief review of glaucoma- its types, symptoms, risk, diagnostic tools, treatment and prevention. It also discusses retinal image datasets, different image processing techniques, diagnosing parameters and performance evaluation metrics of the methods used by various researchers. The literature search revealed that, exclusively for the retinal disease glaucoma, the detection of abnormality in optic disc and optic cup is to carry out at the initial stage. Therefore, for patients with the retinal disease glaucoma, the pathological signs and symptoms have to be analysed. From the review, it was inferred that, the many different techniques and diagnosing parameters can be used to detect glaucoma using the relevant fundus features.

Optometrists, ophthalmologists, orthoptists, and other trained medical professionals use fundus photography to monitor the progression of certain eye conditions or diseases. Segmentation of the vessel tree is an essential process of retinal analysis. In this paper, an interactive blood vessel segmentation from retinal fundus image based on Canny edge detection is proposed. Semi-automated segmentation of specific vessels can be done by simply moving the cursor across a particular vessel. The pre-processing stage includes the green color channel extraction, applying Contrast Limited Adaptive Histogram Equalization (CLAHE), and retinal outline removal. After that, the edge detection techniques, which are based on the Canny algorithm, will be applied. The vessels will be selected interactively on the developed graphical user interface (GUI). The program will draw out the vessel edges. After that, those vessel edges will be segmented to bring focus on its details or detect the abnormal vessel. This proposed approach is useful because different edge detection parameter settings can be applied to the same image to highlight particular vessels for analysis or presentation.

Diabetic retinopathy (DR) is a complication of diabetes that exists throughout the world. DR occurs due to a high ratio of glucose in the blood, which causes alterations in the retinal microvasculature. Without preemptive symptoms of DR, it leads to complete vision loss. However, early screening through computer-assisted diagnosis (CAD) tools and proper treatment have the ability to control the prevalence of DR. Manual inspection of morphological changes in retinal anatomic parts are tedious and challenging tasks. Therefore, many CAD systems were developed in the past to assist ophthalmologists for observing inter- and intra-variations. In this paper, a recent review of state-of-the-art CAD systems for diagnosis of DR is presented. We describe all those CAD systems that have been developed by various computational intelligence and image processing techniques. The limitations and future trends of current CAD systems are also described in detail to help researchers. Moreover, potential CAD systems are also compared in terms of statistical parameters to quantitatively evaluate them. The comparison results indicate that there is still a need for accurate development of CAD systems to assist in the clinical diagnosis of diabetic retinopathy.

Currently, medical image processing draws intense interests of scien- tists and physicians to aid in clinical diagnosis. The retinal Fundus image is widely used in the diagnosis and treatment of various eye diseases such as Diabetic Retinopathy, glaucoma etc. If these diseases are detected and treated early, many of the visual losses can be pre- vented. This paper presents the methods to detect main features of Fundus images such as optic disk, fovea, exudates and blood vessels. To determine the optic Disk and its centre we find the brightest part of the Fundus. The candidate region of fovea is defined an area circle. The detection of fovea is done by using its spatial relationship with optic disk. Exudates are found using their high grey level variation and their contours are determined by means of morphological recon- struction techniques. The blood vessels are highlighted using bottom hat transform and morphological dilation after edge detection. All the enhanced features are then combined in the Fundus image for the detection of abnormalities in eye.

A robust method of human authentication based on the retinal blood vessel pattern is presented in this paper. This method entails a segmentation process to identify retinal blood vessel pattern, template generation consisting of the bifurcation points in the retina and matching of the intersection points in the template patterns. The number of matched blood vessel intersection points between the two patterns compared is used as a measure of similarity. As Liveness detection is a highly desirable anti-spoofing measure in biometric authentication, it is ensured while acquiring retinal images in realtime. The validity of our approach is verified with experimental results obtained from 603 comparisons made using 303 retinal images from three different publicly available databases, namely DRIVE, VARIA and STARE. We found that the proposed retinal recognition method gives 100%, 96.3% and 91.1% recognition rates respectively for the above databases. To the best of our knowledge, this is the first work that uses a large number of retinal images from different retinal databases for the authentication purpose.

This paper proposes an operation safety early warning system based on LabView (2014, National Instruments Corporation, Austin, TX, USA) for vascular interventional surgery (VIS) robotic system. The system not only provides intuitive visual feedback information for the surgeon, but also has a safety early warning function. It is well known that blood vessels differ in their ability to withstand stress in different age groups, therefore, the operation safety early warning system based on LabView has a vascular safety threshold function that changes in real-time, which can be oriented to different age groups of patients and a broader applicable scope. In addition, the tracing performance of the slave manipulator to the master manipulator is also an important index for operation safety. Therefore, we also transformed the slave manipulator and integrated the displacement error compensation algorithm in order to improve the tracking ability of the slave manipulator to the master manipulator and reduce master–slave tracking errors. We performed experiments “in vitro” to validate the proposed system. According to previous studies, 0.12 N is the maximum force when the blood vessel wall has been penetrated. Experimental results showed that the proposed operation safety early warning system based on LabView combined with operating force feedback can effectively avoid excessive collisions between the surgical catheter and vessel wall to avoid vascular puncture. The force feedback error of the proposed system is maintained between ±20 mN, which is within the allowable safety range and meets our design requirements. Therefore, the proposed system can ensure the safety of surgery.