Thin Segments Research Articles

Thin vessel segmentation in fundus images using attention UNet and modified Frangi filtering

Thin vessel segmentation is an active research problem, with an emphasis on finding a universal approach for different types of fundus datasets. Enhancement of the thin vessels is the first and foremost task for proper segmentation, which is proposed to be done with the total variation (TV) decomposition method with layer-selective enhancement and illumination correction. The vessel segmentation task is carried out on two fronts. For thin vessels, we propose the attention UNet backbone, and for thick vessels, the modified Frangi method is used. The method is trained and tested on HRF, CHASE_DB1, and DRIVE datasets with accuracy of 96.73%, 96.38%, and 94.97%, and sensitivity of 92.66%, 81.05%, and 87.56%, respectively. The method was cross-validated on the STARE dataset with an accuracy of 96.30% and a sensitivity of 93.69%. The sensitivity performance surpasses the state of the art.

Advanced petrographic thin section segmentation through deep learning-integrated adaptive GLFIF

In geological research, precise segmentation of sandstone thin sections is crucial for detailed subsurface material analysis. Traditional methods often fall short in accurately capturing the complexities of these samples. This study presents an innovative segmentation approach that integrates an adaptive Global and Local Fuzzy Image Fitting (GLFIF) algorithm with Otsu's thresholding, significantly enhancing segmentation accuracy and efficiency. Our method combines deep learning and traditional image processing techniques. The adaptive GLFIF algorithm, powered by deep learning, automates parameter tuning, thereby reducing manual intervention and improving precision. Unlike conventional methods that learn fixed parameters, our model dynamically adjusts the segmentation process to achieve accurate results. The dual-phase segmentation strategy effectively isolates small features and handles intricate boundaries, ensuring high-quality outcomes. Experimental results demonstrate that our approach improves segmentation accuracy by 11.2% (from 82.6% to 93.8%), the Jaccard index by 15.4% (from 76.8% to 92.2%), and the Dice coefficient by 9% (from 86.9% to 95.9%) compared to traditional methods. This technique bridges the gap between conventional image analysis and deep learning, combining precise segmentation with the automation and computational power of advanced algorithms. Our segmentation algorithm represents a significant advancement in automated petrographic thin section analysis. Traditional image processing methods, such as thresholding and level sets, excel in handling small objects and complex boundaries but require significant manual intervention and cannot achieve full automation. Recent deep learning methods, particularly semantic segmentation, offer end-to-end automation but struggle with small targets and intricate boundaries. Our approach effectively combines the strengths of both methodologies, providing a comprehensive and efficient solution for geological image analysis that ensures both high accuracy and full automation.

UCAN: U-shaped context aggregation network for thin crack segmentation under topological constraints

PurposeThin cracks on the surface, such as those found in nuclear power plant concrete structures, are difficult to identify because they tend to be thin. This paper aims to design a novel segmentation network, called U-shaped contextual aggregation network (UCAN), for better recognition of weak cracks.Design/methodology/approachUCAN uses dilated convolutional layers with exponentially changing dilation rates to extract additional contextual features of thin cracks while preserving resolution. Furthermore, this paper has developed a topology-based loss function, called ℓcl Dice, which enhances the crack segmentation’s connectivity.FindingsThis paper generated five data sets with varying crack widths to evaluate the performance of multiple algorithms. The results show that the UCAN network proposed in this study achieves the highest F1-Score on thinner cracks. Additionally, training the UCAN network with the ℓcl Dice improves the F1-Scores compared to using the cross-entropy function alone. These findings demonstrate the effectiveness of the UCAN network and the value of incorporating the ℓcl Dice in crack segmentation tasks.Originality/valueIn this paper, an exponentially dilated convolutional layer is constructed to replace the commonly used pooling layer to improve the model receptive field. To address the challenge of preserving fracture connectivity segmentation, this paper introduces ℓcl Dice. This design enables UCAN to extract more contextual features while maintaining resolution, thus improving the crack segmentation performance. The proposed method is evaluated using extensive experiments where the results demonstrate the effectiveness of the algorithm.

Immunohistochemical and Ultrastructural Characterization of Telocytes in Normal and Diabetic Human Kidneys.

Background: Telocytes are interstitial stromal cells identified in various human organs, including the kidney. Their presence and role in human diabetic kidney disease remain unknown. Methods: To identify and localize telocytes in glomerular and tubule-interstitial compartments, both normal and diabetic human renal tissues were examined using immunohistochemistry, immunofluorescence, and transmission electron microscopy. Results: Renal telocytes are elongated interstitial cells with long, thin telopodes, showing alternating thin and thick segments. They expressed CD34, Nestin, α-SMA, and Vimentin markers. Occasionally, c-Kit expression was observed in some rounded and spindle cells, while no positivity was detected for PDGFR-β and NG2. Telocytes were identified around Bowman's capsule, tubules, and peritubular capillaries in both normal and diabetic conditions. In diabetic renal samples, there was a significant increase in α-SMA expressing telocytes, leading to periglomerular fibrosis. These telocytes also exhibited a synthetic phenotype with proteoglycan deposition in the extracellular matrix and, in some cases, showed pre-adipocytic differentiation. Conclusions: Telocytes were identified in normal and diabetic human kidneys. These cells form an elastic mechanical scaffold in the interstitium and are present in all renal cortical compartments. In diabetic samples, their increased α-SMA expression and synthetic phenotype suggest their potential role in the pathogenesis of diabetic nephropathy.

CD34+stromal cells/telocytes and their role in mouse lung development: Light microscopy, immunofluorescence, ultrastructural and scanning electron microscopy evidence.

Telocytes (TCs), a novel type of mesenchymal or interstitial cell with specific, very long and thin cellular prolongations, have been found in various mammalian organs and have potential biological functions. However, their existence during lung development is poorly understood. This study aimed to investigate the existence, morphological features, and role of CD34+ SCs/TCs in mouse lungs from foetal to postnatal life using primary cell culture, double immunofluorescence, transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The immunofluorescence double staining profiles revealed positive expression of CD34 and PDGFR-α, Sca-1 or VEGFR-3, and the expression of these markers differed among the age groups during lung development. Intriguingly, in the E18.5 stage of development, along with the CD34+ SCs/TCs, haematopoietic stem cells and angiogenic factors were also significantly increased in number compared with those in the E14.5, E16.5, P0 and P7. Subsequently, TEM confirmed that CD34+ SCs/TCs consisted of a small cell body with long telopodes (Tps) that projected from the cytoplasm. Tps consisted of alternating thin and thick segments known as podomers and podoms. TCs contain abundant endoplasmic reticulum, mitochondria and secretory vesicles and establish close connections with neighbouring cells. Furthermore, SEM revealed characteristic features, including triangular, oval, spherical, or fusiform cell bodies with extensive cellular prolongations, depending on the number of Tps. Our findings provide evidence for the existence of CD34+ SCs/TCs, which contribute to vasculogenesis, the formation of the air‒blood barrier, tissue organization during lung development and homoeostasis.

Ultrastructural analysis and 3D reconstruction of the frontal sensory-glandular complex and its neural projections in the platyhelminth Macrostomum lignano.

The marine microturbellarian Macrostomum lignano (Platyhelminthes, Rhabditophora) is an emerging laboratory model used by a growing community of researchers because it is easy to cultivate, has a fully sequenced genome, and offers multiple molecular tools for its study. M. lignano has a compartmentalized brain that receives sensory information from receptors integrated in the epidermis. Receptors of the head, as well as accompanying glands and specialized epidermal cells, form a compound sensory structure called the frontal glandular complex. In this study, we used semi-serial transmission electron microscopy (TEM) to document the types, ultrastructure, and three-dimensional architecture of the cells of the frontal glandular complex. We distinguish a ventral compartment formed by clusters of type 1 (multiciliated) sensory receptors from a central domain where type 2 (collar) sensory receptors predominate. Six different types of glands (rhammite glands, mucoid glands, glands with aster-like and perimaculate granula, vacuolated glands, and buckle glands) are closely associated with type 1 sensory receptors. Endings of a seventh type of gland (rhabdite gland) define a dorsal domain of the frontal glandular complex. A pair of ciliary photoreceptors is closely associated with the base of the frontal glandular complex. Bundles of dendrites, connecting the receptor endings with their cell bodies which are located in the brain, form the (frontal) peripheral nerves. Nerve fibers show a varicose structure, with thick segments alternating with thin segments, and are devoid of a glial layer. This distinguishes platyhelminths from larger and/or more complex invertebrates whose nerves are embedded in prominent glial sheaths.

Kinetostatic Modeling of an Asymmetrical Double-Stepped Beam for Displacement Amplification

Abstract A kinetostatic model of an asymmetrical double-stepped beam under axial loading is developed. The beam is composed of two thick segments and three thin segments where a thick segment is between two thin segments, and the longitudinal axis of the thick segment is not colinear with that of the thin segment. The kinetostatic model based on the beam constraint model (BCM) is capable of predicting accurate bending and near-buckling behaviors of the beam. A virtual rigid link neighboring the noncolinear segments is introduced in the BCM to broaden the applicability of the BCM. An analytical formula to represent the critical load of a symmetrical double-stepped beam under axial loading is derived and the value calculated by the formula agrees with the limit load of the asymmetrical double-stepped beam within the elastic range. The investigated beam has potential applications in displacement amplifiers and robotic grippers.

Improving the detection limit in lung counting with a segmented HPGe detector

A segmented High-Purity Germanium (HPGe) detector with a thin front segment together with various active and passive shield configurations was simulated with the aim of reducing the level of background events in lung counting applications. Eight different detector models were tested in a Geant4 simulation environment in a scenario where inhaled 241Am activity was deposited in the lungs of an ICRP adult reference computational phantom. In lung counting measurements, the Compton continuum in the spectrum is generated by the natural and man-made radionuclides inside the human body and the natural background radiation from the environment. The reduction in Minimum Detectable Activity (MDA) using the segmented HPGe detector combined with an active shield compared to a model with a single germanium crystal was investigated. A reduction in MDA up to 30% and 66% was obtained for internal and external sources, respectively. The results show that the detection limit and/or the measurement time in lung counting can be reduced using such a detector configuration. Furthermore, combining the segmented HPGe detector with an active shield would be particularly useful in field measurements.

PSO-HRVSO: Segmentation of Retinal Vessels Through Homomorphic Filtering Enhanced by PSO Optimization

The structure of retinal blood vessels is crucial for the early detection of diabetic retinopathy, a leading cause of blindness worldwide. Yet, accurately segmenting retinal vessels poses significant challenges due to the low contrast and noise present in capillaries.The automated segmentation of retinal blood vessels significantly enhances Computer-Aided Diagnosis for diverse ophthalmic and cardiovascular conditions. It is imperative to develop a method capable of segmenting both thin and thick retinal vessels to facilitate medical analysis and disease diagnosis effectively. This article introduces a novel methodology for robust vessel segmentation, addressing prevalent challenges identified in existing literature. The methodology PSO-HRVSO comprises three key stages: pre-processing, main processing, and postprocessing. In the initial stage, filters are employed for image smoothing and enhancement, leveraging PSO optimization. The main processing phase is bifurcated into two configurations. Initially, thick vessels are segmented utilizing an optimized top-hat approach, homo-morphic filtering, and median filter. Subsequently, the second configuration targets thin vessel segmentation, employing the optimized top-hat method, homomorphic filtering, and matched filter. Lastly, morphological image operations are conducted during the post-processing stage. The PSO-HRVSO method underwent evaluation using two publicly accessible databases (DRIVE and STARE), measuring performance across three key metrics: specificity, sensitivity, and accuracy. Analysis of the outcomes revealed averages of 0.9891, 0.8577, and 0.0.9852 for the DRIVE dataset, and 0.9868, 0.8576, and 0.9831 for the STARE dataset, respectively. The PSO-HRVSO technique yields numerical results that demonstrate competitive average values when compared to current methods. Moreover, it sur-passes all leading unsupervised methods in terms of specificity and accuracy. Additionally, it outperforms the majority of state-of-the-art supervised methods without incurring the computational costs associated with such algorithms. Detailed visual analysis reveals that the PSO-HRVSO approach enables a more precise segmentation of thin vessels compared to alternative procedures.

Renal artery occlusion in a young woman - a tale of mysterious thrombosis.

Renal artery thrombosis is rare and limited reports exist in the young population. The most common aetiology is thromboembolic disease or abdominal trauma in this population and isolated occurrences are extremely rare. We present the case of an 18-year-old woman with spontaneous unilateral renal artery thrombosis and infarction for whom reperfusion was achieved through endovascular intervention. The aetiology of her thrombosis remains unclear and is under investigation with differential diagnoses being fibromuscular dysplasia, large and medium vessel vasculitis, and thromboembolic causes. To demonstrate the value in attempting salvage of an ischaemic kidney in a young patient with an unexplained spontaneous renal thrombosis. JM is an 18-year-old woman who presented to a large regional tertiary hospital with 3days of right flank pain. She had no infective symptoms and no urinary or bowel changes before admission. She was not pregnant, and her only medication was the oral contraceptive pill commenced 3months prior. A CT angiogram demonstrated right renal artery thrombosis with renal infarction. The kidney was deemed potentially salvageable, and ultrasound defined adequate vessel calibre to access for thrombectomy in the context of a negative coagulopathy screen. The patient underwent thrombolysis, thrombectomy and balloon angioplasty. Intraoperatively, a thin segment of distal stenosis was identified, and angiogram reperfusion was achieved with subsequent improvement in renal function. Renal artery thrombosis in young people is extremely rare and presents a diagnostic and management challenge requiring input from multiple teams including nephrology, rheumatology, paediatrics and vascular surgery. Systemic coagulopathy and vasculitis are differentials against anatomical aetiologies such as fibromuscular dysplasia. Our case adds to the limited literature regarding this in the young population. Renal artery thrombosis with occlusion in young people is very rare and is most often associated with a systemic coagulopathic disorder, such as antiphospholipid syndrome or structural pathology of the renal vasculature such as fibromuscular dysplasia. The work-up of a young female presenting with renal artery thrombosis without any previous medical history screens for a wide range of pathological processes.

Macro and Micro Anatomical Comparative Study for Liver, Kidney and Testis Between the Albino Rat and Pigeon

The present study was designed to demonstrate the anatomical and histological variations between the certain organs in both the rat as a model of experimental mammals and pigeon as an avian model. Five albino rats and five pigeons were utilized, the liver, kidney and the testicles were taken from those animals after scarifying and specimens were taken from those organs, fixed in 10 % formalin for 24 hours before transferred for histological technique, the slices were stained with hematoxylin and eosin. The anatomical observation indicated that the liver of rat was formed by right, left lobes and intermediate, located in the anterior part of abdomen, in the pigeon, had two lobes without gall bladder. The kidney of the rat was bean shaped, brown reddish color with idented medial border present in the abdominal cavity near and around the lumbar vertebrae, the kidney of pigeon was lobulated into three and the kidney was lodged on the ventral aspect of the synsacrum.
 The testis of rat was oval in shape present in the scrotum, and in the pigeon located in the abdominal and pelvic cavity. The histological result indicated the liver of rat was with columns of polygonal liver cells surrounded by blood sinusoids draining in the central vein, while in the pigeon, the cells were present in groups also had blood sinusoids with Kupffer cells and central vein for draining the blood outside the liver, the kidney of both types had an cortex and medulla with presence of glomeruli in the cortex, both had proximal and distal convoluted tubules , in the pigeon there is no thin segment of Henle loops .
 The testis was occupied by seminiferous tubules in both types and each tubule was containing the different types of spermatic cells and spermatozoa, also Leydig cells were present in groups, spermatogenesis was intensive in pigeon in cases of seasonal phase in conclusion, variation for the visceral organs in both rats and pigeon were determined also histological differences were present, otherwise similarities were also observed.

WSA-MP-Net: Weak-signal-attention and multi-scale perception network for microvascular extraction in optical-resolution photoacoustic microcopy

The unique advantage of optical-resolution photoacoustic microscopy (OR-PAM) is its ability to achieve high-resolution microvascular imaging without exogenous agents. This ability has excellent potential in the study of tissue microcirculation. However, tracing and monitoring microvascular morphology and hemodynamics in tissues is challenging because the segmentation of microvascular in OR-PAM images is complex due to the high density, structure complexity, and low contrast of vascular structures. Various microvasculature extraction techniques have been developed over the years but have many limitations: they cannot consider both thick and thin blood vessel segmentation simultaneously, they cannot address incompleteness and discontinuity in microvasculature, there is a lack of open-access datasets for DL-based algorithms. We have developed a novel segmentation approach to extract vascularity in OR-PAM images using a deep learning network incorporating a weak signal attention mechanism and multi-scale perception (WSA-MP-Net) model. The proposed WSA network focuses on weak and tiny vessels, while the MP module extracts features from different vessel sizes. In addition, Hessian-matrix enhancement is incorporated into the pre-and post-processing of the input and output data of the network to enhance vessel continuity. We constructed normal vessel (NV-ORPAM, 660 data pairs) and tumor vessel (TV-ORPAM, 1168 data pairs) datasets to verify the performance of the proposed method. We developed a semi-automatic annotation algorithm to obtain the ground truth for our network optimization. We applied our optimized model successfully to monitor glioma angiogenesis in mouse brains, thus demonstrating the feasibility and excellent generalization ability of our model. Compared to previous works, our proposed WSA-MP-Net extracts a significant number of microvascular while maintaining vessel continuity and signal fidelity. In quantitative analysis, the indicator values of our method improved by about 1.3% to 25.9%. We believe our proposed approach provides a promising way to extract a complete and continuous microvascular network of OR-PAM and enables its use in many microvascular-related biological studies and medical diagnoses.

Bilayer and trilayer X-ray mirror coatings containing W, Pt, or Ir<?oxy_insert_start author="CKapler" timestamp="20231211T162235-0500"?>,<?oxy_insert_end?> in combination with C, C/Co, B4C, or B4C/Ni: X-ray reflectance, film stress, and temporal stability

X-ray reflectance and film stress were measured for 12 bilayer and trilayer reflective interference coatings and compared with a single-layer Ir coating. The interference coatings comprise a base layer of W, Pt, or Ir, top layers of either C or B4C, and, in the case of the trilayer coatings, middle layers of either Co or Ni. The coatings were deposited by magnetron sputtering. Film stress was measured using the wafer curvature technique, while X-ray reflectance was measured at grazing incidence over the ∼0.1−10keV energy band using synchrotron radiation. Re-measurements over a period of more than two years of both stress and X-ray reflectance were used to assess temporal stability. The X-ray reflectance of all 12 bilayer and trilayer coatings was found to be both stable over time and substantially higher than single-layer Ir over much of the energy range investigated, particularly below ∼4keV, except near the B and C K-edges, and the Co and Ni L-edges, where we observe sharp, narrow drops in reflectance due to photo-absorption in layers containing these materials. Film stress was found to be substantially smaller than single-layer Ir in all cases as well; however, film stress was also found to change over time for all coatings (including the single-layer Ir coating). The effective area of future X-ray telescopes will be substantially higher if these high reflectance bilayer and/or trilayer coatings are used in place of single-layer coatings. Additionally, the smaller film stresses found in the bilayer and trilayer coatings relative to single-layer Ir will reduce coating-stress-driven mirror deformations. Nevertheless, as all the interference films studied here have stresses that are far from zero (albeit smaller than that of single-layer Ir), methods to mitigate such deformations must be developed in order to construct high-angular-resolution telescopes using thin mirror segments. Furthermore, unless film stress can be sufficiently stabilized over time, perhaps through thermal annealing, any such mitigation methods must also account for the temporal instability of film stress that was found in all coatings investigated here.

A novel chaotic weighted EHO-based methodology for retinal vessel segmentation

ABSTRACT Retinal image segmentation process deals withproblems like spurious vascularisation and thin vessel detection. In thispaper, a three-step methodology has been proposed for retinal vesselsegmentation. In first step, RGB to YIQ conversion is performed. In second step, Y component enhancement is performed. A novel Chaotic weighted Elephant HerdingOptimization (CWEHO) has been proposed to optimize the clip limit and blocksize values of Contrast Limited Adaptive Histogram Equalization (CLAHE). CWEHO-based CLAHE along with morphological operations, non-local means filter, and median filter is applied to enhanceretinal images. In third step, thin and thick vessel segmentation is performed.Top hat transformation, otsu thresholding algorithm, and vessel point selectionare applied for thick vessel extraction. The first-order Gaussian derivative in conjunction with the matchfilter is used to extract thin vessels. DRIVE and HRF datasets are used toassess the effectiveness of proposed methodology. The average values ofsegmentation accuracy, specificity, sensitivity, and Mathew CorrelationCoefficient (MCC) are observed to be 0.9650, 0.9895, 0.7007, 0.7650,respectively, for observer1 and 0.9696, 0.9912, 0.7390, 0.7901 for observer2using DRIVE dataset. Similarly, 0.9592, 0.9839, 0.6850, and 0.7116,respectively, metrics for HRF dataset. Compared to state-of-the-art methods, the proposed segmentation methodology providesbetter results .

CHANGES IN FRACTAL DIMENSION OF THIN AND THICK BLOOD VESSELS FROM RETINAL FUNDUS IMAGES FOR DIFFERENT STAGES IN DIABETIC RETINOPATHY

Retinal vasculature feature extraction plays a critical role in the diagnosis and treatment of systemic conditions, particularly in the cases of diabetic retinopathy (DR). This research introduces an algorithm that utilizes segmented blood vessels in retinal images to identify and differentiate five stages of DR, including mild, moderate, severe and proliferative. The algorithm effectively extracts retinal blood vessels by integrating morphological operators and matched filters, yielding a more precise output. The algorithm’s performance is evaluated using the database IDRiD, demonstrating precision and sensitivity scores comparable to those of a trained observer. A box-counting method was incorporated to measure the fractal dimension (FD) of DR-segmented vessel images at various stages to enhance the accuracy of DR staging. The FD analysis was applied to both thick and thin segments of the blood vessels, enabling the assessment of accuracy, sensitivity and specificity. The results indicate that the algorithm successfully identifies the different stages of DR with an accuracy of 93.65% for the mild stage, of 93.33% for the moderate and severe stages and of 92.71% for proliferative DR compared to the images without DR. The study reveals that the variation in FD between the thick and thin vessel components can be an effective biomarker for identifying the different stages of DR, contributing to a better understanding of disease progression. By combining morphological operators, matched filters and fractal dimension analysis, this research presents a promising approach for specialists involved in diagnosing and treating DR, eventually leading to improved patient care and consequences.

A multi-scale global attention network for blood vessel segmentation from fundus images

Accurate segmentation of retinal fundus vessel images is vital to clinical diagnosis. Due to the intricate vascular morphology, high noise and low contrast of fundus vessel images, retinal fundus vessel segmentation is still a challenging task, especially for thin vessel segmentation. In recent years, on account of strong context feature extraction ability of deep learning, it has shown a remarkable performance in the automatic segmentation of retinal fundus vessels. However, it still exhibits certain limitations, such as information loss on micro objects or details, inadequate treatment of local features, etc. Faced with these challenging factors, we present a new multi-scale global attention network (MGA-Net). To realize effective feature representation, a dense attention U-Net network is proposed. Meanwhile, we design a global context attention (GCA) block to realize multi-scale feature fusion, allowing the global features from the deep network layers to flow to the shallow network layers. Further, aimed at retinal fundus vessel segmentation task again the class imbalance issue, the AG block is also introduced. Related experiments are conducted on CHASE_DB1, DRIVE and STARE datasets to show the effectiveness of proposed segmentation model. The experimental results demonstrate the robustness of the proposed method with F1 exceeding 82% on all three datasets and effectively improve the segmentation performance of thin vessels. The source code of proposed MGA-Net is available at https://github.com/gegao310/workspace.git.

Panfacial fractures management, a report of surgical treatment sequence in three cases: A case series study

Multiple facial fractures, which involve the upper, middle, and lower thirds of the face, are called Panfacial fractures, and their management is one of the biggest challenges in the field of maxillofacial surgery. The proximity of maxillofacial skeleton to important sensory or vital structures such as the visual, olfactory, masticatory and respiratory systems and intracranial components in addition to negative effects on esthetic aspects of the face have doubled the intricacy. Small or thin fractured segments that are difficult to find and stabilize make management of pan facial fractures different from anywhere else in the body.
 One major challenge is to find the best pattern and sequence of treatment. There are different concepts, depending on the surgeon's experience and the pattern of fracture. This study reports three patients with the diagnosis of pan facial fractures. A 54-year-old woman, an 18-year-old and a 14-year-old man that were all victims of road traffic injuries (MVA). Conventional open reduction and internal fixation methods have been used and favorable results have been obtained in follow-up periods.

Investigating electromagnetically induced transparency spectral lineshape distortion due to non-uniform fields in Rydberg-atom electrometry

We investigate the effects of spatially non-uniform radio-frequency electric (E) field amplitudes on the spectral line shapes of electromagnetically induced transparency (EIT) signals in Rydberg atomic systems used in electrometry (i.e., the metrology of E-field strengths). Spatially non-uniform fields distort the EIT spectra from that of an ideal case, and understanding this distortion is important in the development of Rydberg atom-based sensors, as these distortions can limit accuracy and sensitivity. To characterize this distortion, we present a model that approximates the atom vapor as multi-layered media and then uses Beer’s law to combine the absorption through its many discrete thin segments. We present a set of expected line distortions caused by various RF electric-field distributions found in practice. This provides an intuitive diagnostic tool for experiments. We compare this model to measured experimental atomic spectra in both two-photon and three-photon excitation schemes in the presence of non-uniform radio-frequency fields. We show that we can accurately model and reproduce the EIT lineshape distortion observed in these experimental data.

CSSRS: Pore segmentation method of sandstone cast thin section images based on weak supervised learning

Semantic segmentation based on deep learning has been gradually applied to digital rocks, but the amount of training sets and the fineness of data labelling affect the accuracy of the final segmentation, as well as the pixel-level annotation would cost a lot of human effort and be accompanied by certain errors. To address the problem of high data labelling cost of sandstone cast thin section images and segmentation accuracy affected by manual bias, this paper proposes a new method combining Smoothed Score Class Activation Mapping (SSCAM) and Restricted Similarity Dual Contrast Learning (RSimDCL), and we named it CSSRS. Using an image-level annotated dataset, the study applied CSSRS to the images with pore segments (pore images) presented and those without (background images). CSSRS utilized SSCAM to obtain the general location of the pores from the cast thin section map, adopted RSimDCL to obtain the fine boundaries of the pore area, and combined these two methods to generate more accurate pore segmentation of sandstone cast thin section images. Finally, pore segmentation was performed on the same test dataset using CSSRS and RSimDCL, and the segmentation results were compared with various Class Activation Mapping (CAM) algorithms based on weakly supervised learning and various Generative Adversarial Networks (GAN) algorithms. Results showed that CSSRS demonstrated the highest Precision of 79.35% (18.65% higher than the second best method RSimDCL). In addition, the study applied Mean Intersection over Union (MIoU), Dice, Pixel Accuracy (PA), False Positive Rate (FPR) comparison metrics and found that CSSRS consistently demonstrated a better performance of 5%–6% improvement than the second best method in each metric. In conclusion, the CSSRS method based on weakly supervised learning can be applied to pore segmentation of sandstone cast thin section images, which significantly reduces the data annotation workload, avoids the manual bias, and also increases the segmentation effect to a greater extent.

Term Delivery Complicated by Uterine Rupture with No Prior History of Cesarean Section or Uterine Curettage Following Oxytocin Use and Arrest in Second Stage of Labor: A Case Report.

BACKGROUND Uterine rupture during delivery in an unscarred uterus may be associated with oxytocin dose during second stage arrest and with underlying maternal factors. This report is of a 34-year-old woman, gravida 5, para 3, with no previous history of cesarean section (CS), who had a uterine rupture at term delivery following the use of oxytocin for second-stage arrest. CASE REPORT A 34-year-old Afghani woman, gravida 5, para 3 was admitted at term for delivery. The current pregnancy had been normal and the estimated birth weight was approximately 4000 g. There was no history of steroid treatment or any underlying connective tissue disease, and no history of dilation and curettage. Oxytocin was given as per protocol, starting at 20 ml/h of a dilution of 10 IU/1 L natrium chloride (NaCl). Subsequent labor progress was complicated by arrest of descent in the second stage of labor, necessitating cesarean section delivery. After opening the abdominal wall, a uterine rupture with several large blood clots was discovered, freely floating in the peritoneal space, about 500 ml in volume. The rupture stretched from the left side of the uterine body and down into the thin lower segment. The tissue in this area had diffuse hematoma. CONCLUSIONS Although uterine rupture mostly occurs in women with previous CS, this report has shown that uterine rupture can occur in pregnancy complicated by arrest in the second stage of labor.