Lung cancer metastasis-induced distal esophageal segmental spasm confirmed by individualized peroral endoscopic myotomy:A case report

BACKGROUND Peroral endoscopic myotomy(POEM)has been widely performed as a standard treatment for achalasia;however,its efficacy and safety for treating distal esophageal segmental spasms induced by cancer metastasis remain unknown.CASE SUMMARY A 72-year-old male was referred to our hospital and complained of progressive dysphagia for two years.Endoscopy revealed a 2 cm long segment esophageal stenosis with intact mucosa and normal cardia.Computed tomography showed a right upper lung mass,and pathology of the right pleural effusion confirmed the diagnosis of right upper lung adenocarcinoma with multiple rib and mediastinal lymph node metastases and right malignant pleural effusion.Individualized POEM was performed first to alleviate dysphagia,and the final diagnosis was changed to esophageal muscle metastasis arising from lung adenocarcinoma.After treatment,the patient could eat soft solid food and received multiple rounds of pembrolizumab-combination chemotherapy.The patient’s progression-free survival was approximately 16 months.Long stable disease was obtained during the 24-month follow-up.CONCLUSION The incidence of distal esophageal segmental spasms induced by muscular metastasis arising from lung adenocarcinoma is extremely low.Individualized POEM can effectively improve a patient’s nutritional status before subsequent chemotherapy can be combined with immune checkpoint inhibitors.

Numerical Simulation-Based Analysis of the Impact of Overloading on Segmentally Assembled Bridges

Segmentally assembled bridges are increasinglyfinding engineering applications in recent years due to their unique advantages,especially as urban viaducts.Vehicle loads are one of the most important variable loads acting on bridge structures.Accordingly,the influence of overloaded vehicles on existing assembled bridge structures is an urgent concern at present.This paper establishes thefinite element model of the segmentally assembled bridge based on ABAQUS software and analyzes the influence of vehicle overload on an assembled girder bridge struc-ture.First,afinite element model corresponding to the target bridge is established based on ABAQUS software,and the load is controlled to simulate vehicle movement in each area of the traveling zone at different times.Sec-ond,the key cross-sections of segmental girder bridges are monitored in real time based on the force character-istics of continuous girder bridges,and they are compared with the simulation results.Finally,a material damage ontology model is introduced,and the structural damage caused by different overloading rates is compared and analyzed.Results show that thefinite element modeling method is accurate by comparing with on-site measured data,and it is suitable for the numerical simulation of segmental girder bridges;Dynamic sensors installed at 1/4L,1/2L,and 3/4L of the segmental girder main beams could be used to identify the dynamic response of segmental girder bridges;The bottom plate of the segmental girder bridge is mostly damaged at the position where the length of the precast beam section changes and the midspan position.With the increase in load,damage in the direction of the bridge develops faster than that in the direction of the transverse bridge.Thefindings of this study can guide maintenance departments in the management and maintenance of bridges and vehicles.

Review of plasma exchange and rituximab for prevention of recurrent focal segmental glomerulosclerosis after a prior graft loss

BACKGROUND Focal segmental glomerulosclerosis(FSGS)often recurs after transplantation,leading to graft dysfunction and graft loss.Patients who have lost prior grafts due to recurrence are at particularly high risk of re-recurrence in subsequent grafts.Rituximab and plasma exchange have been used pre-emptively to prevent post-transplant recurrence.However,the efficacy of such preventative measures remains unclear.AIM To investigate the outcomes of preventative rituximab and plasma exchange for recurrent FSGS in transplant recipients after prior graft loss.METHODS We conducted a systematic review of 11 studies with 32 patients who had experienced prior graft loss due to post-transplant FSGS recurrence and were treated with either pre-emptive plasma exchange alone,rituximab alone,or a combination of both.RESULTS Overall,47%of the 32 patients experienced recurrence despite prophylactic treatment.Re-recurrence was seen in 25%(1/4)with pre-emptive rituximab alone,and 45%recurrence(9/20)with plasma exchange alone.Re-recurrence was noted in 63%with the use of combined plasma exchange and rituximab.CONCLUSION There is a paucity of available evidence in the literature to draw clear conclusions on the benefits of pre-emptive measures to prevent FSGS re-recurrence.The small sample sizes and variations in protocols call for larger and controlled studies to serve this patient population at high risk of recurrence and graft loss.

Clinicopathological features and medium-term outcomes of histologic variants of primary focal segmental glomerulosclerosis in adults:A retrospective study

BACKGROUND The Columbia classification identified five histological variants of focal segmental glomerulosclerosis(FSGS).The prognostic significance of these variants remains controversial.AIM To evaluate the relative frequency,clinicopathologic characteristics,and medium-term outcomes of FSGS variants at a single center in Pakistan.METHODS This retrospective study was conducted at the Department of Nephrology,Sindh Institute of Urology and Transplantation,Karachi,Pakistan on all consecutive adults(≥16 years)with biopsy-proven primary FSGS from January 1995 to December 2017.Studied subjects were treated with steroids as a first-line therapy.The response rates,doubling of serum creatinine,and kidney failure(KF)with replacement therapy were compared between histological variants using ANOVA or Kruskal Wallis,and Chi-square tests as appropriate.Data were analyzed by SPSS version 22.0.P-value≤0.05 was considered significant.RESULTS A total of 401 patients were diagnosed with primary FSGS during the study period.Among these,352(87.7%)had a designated histological variant.The not otherwise specified(NOS)variant was the commonest,being found in 185(53.9%)patients,followed by the tip variant in 100(29.1%)patients.Collapsing(COL),cellular(CEL),and perihilar(PHI)variants were seen in 58(16.9%),6(1.5%),and 3(0.7%)patients,respectively.CEL and PHI variants were excluded from further analysis due to small patient numbers.The mean follow-up period was 36.5±29.2 months.Regarding response rates of variants,patients with TIP lesions achieved remission more frequently(59.5%)than patients with NOS(41.8%)and COL(24.52%)variants(P<0.001).The hazard ratio of complete response among patients with the COL variant was 0.163[95%confidence interval(CI):0.039-0.67]as compared to patients with NOS.The TIP variant showed a hazard ratio of 2.5(95%CI:1.61-3.89)for complete remission compared to the NOS variant.Overall,progressive KF was observed more frequently in patients with the COL variant,43.4%(P<0.001).Among these,24.53%of patients required kidney replacement therapy(P<0.001).The hazard ratio of doubling of serum creatinine among patients with the COL variant was 14.57(95%CI:1.87-113.49)as compared to patients with the TIP variant.CONCLUSION In conclusion,histological variants of FSGS are predictive of response to treatment with immunosuppressants and progressive KF in adults in our setup.

Analysis of the Effect of High-Dose Segmental Citrate Anticoagulation in High Flux Hemodialysis

Objective:To analyze the clinical effect of high-dose citrate in segmental extracorporeal anticoagulation for high-throughput hemodialysis.Methods:The subjects included in this study were admitted to the hospital for maintenance hemodialysis treatment from January 2021 to January 2023.All patients had a high risk of bleeding and received 4%trisodium citrate anticoagulant treatment,administered at a rate of 200 mL/h before and after the dialyzer.The anticoagulant effects achieved by the patients were observed and analyzed.Results:The total number of patients who received high-dose segmented citrate extracorporeal anticoagulation dialysis treatment was 50,with each patient undergoing 100 treatments.During the treatment,2 patients had to end the treatment early due to transmembrane pressure exceeding 30 mmHg and an increase in venous pressure exceeding 250 mmHg;the treatment times for these patients were 20 minutes and 200 minutes,respectively.The remaining patients successfully completed the 4-hour treatment.Blood pH and calcium ion concentration in the venous pot were monitored.It was observed that before dialysis,after 2 hours of dialysis,and at the end of dialysis,the blood pH of the patients remained within a relatively normal range.Although some patient levels changed after dialysis,they remained within the normal range.No adverse reactions(such as numbness of the limbs or convulsions)were observed during the anticoagulant treatment.Conclusion:Administering 4%trisodium citrate at a rate of 200 mL/h before and after the dialyzer achieves a good anticoagulant effect,maintains the patient’s blood gas levels within the normal range at the end of dialysis,and causes no adverse reactions.

Synergism of calycosin and bone marrow-derived mesenchymal stem cells to combat podocyte apoptosis to alleviate adriamycininduced focal segmental glomerulosclerosis

BACKGROUND Bone marrow-derived mesenchymal stem cells(MSCs)show podocyte-protective effects in chronic kidney disease.Calycosin(CA),a phytoestrogen,is isolated from Astragalus membranaceus with a kidney-tonifying effect.CA preconditioning enhances the protective effect of MSCs against renal fibrosis in mice with unilateral ureteral occlusion.However,the protective effect and underlying mechanism of CA-pretreated MSCs(MSCsCA)on podocytes in adriamycin(ADR)-induced focal segmental glomerulosclerosis(FSGS)mice remain unclear.AIM To investigate whether CA enhances the role of MSCs in protecting against podocyte injury induced by ADR and the possible mechanism involved.METHODS ADR was used to induce FSGS in mice,and MSCs,CA,or MSCsCA were administered to mice.Their protective effect and possible mechanism of action on podocytes were observed by Western blot,immunohistochemistry,immunofluorescence,and real-time polymerase chain reaction.In vitro,ADR was used to stimulate mouse podocytes(MPC5)to induce injury,and the supernatants from MSC-,CA-,or MSCsCA-treated cells were collected to observe their protective effects on podocytes.Subsequently,the apoptosis of podocytes was detected in vivo and in vitro by Western blot,TUNEL assay,and immunofluorescence.Overexpression of Smad3,which is involved in apoptosis,was then induced to evaluate whether the MSCsCA-mediated podocyte protective effect is associated with Smad3 inhibition in MPC5 cells.RESULTS CA-pretreated MSCs enhanced the protective effect of MSCs against podocyte injury and the ability to inhibit podocyte apoptosis in ADR-induced FSGS mice and MPC5 cells.Expression of p-Smad3 was upregulated in mice with ADR-induced FSGS and MPC5 cells,which was reversed by MSCCA treatment more significantly than by MSCs or CA alone.When Smad3 was overexpressed in MPC5 cells,MSCsCA could not fulfill their potential to inhibit podocyte apoptosis.CONCLUSION MSCsCA enhance the protection of MSCs against ADR-induced podocyte apoptosis.The underlying mechanism may be related to MSCsCA-targeted inhibition of p-Smad3 in podocytes.

Visual Semantic Segmentation Based on Few/Zero-Shot Learning:An Overview

Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception.Conventional learning-based visual semantic segmentation approaches count heavily on largescale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories.This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning.The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples,which advances the extension to practical applications.Therefore,this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances.Specifically,the preliminaries on few/zeroshot visual semantic segmentation,including the problem definitions,typical datasets,and technical remedies,are briefly reviewed and discussed.Moreover,three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation,including image semantic segmentation,video object segmentation,and 3D segmentation.Finally,the future challenges of few/zero-shot visual semantic segmentation are discussed.

Multilevel Attention Unet Segmentation Algorithmfor Lung Cancer Based on CT Images

Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Micro segment analysis of supercritical methane thermal-hydraulic performance and pseudo-boiling in a PCHE straight channel

The printed circuit heat exchanger(PCHE) is receiving wide attention as a new kind of compact heat exchanger and is considered as a promising vaporizer in the LNG process. In this paper, a PCHE straight channel in the length of 500 mm is established, with a semicircular cross section in a diameter of 1.2 mm.Numerical simulation is employed to investigate the flow and heat transfer performance of supercritical methane in the channel. The pseudo-boiling theory is adopted and the liquid-like, two-phase-like, and vapor-like regimes are divided for supercritical methane to analyze the heat transfer and flow features.The results are presented in micro segment to show the local convective heat transfer coefficient and pressure drop. It shows that the convective heat transfer coefficient in segments along the channel has a significant peak feature near the pseudo-critical point and a heat transfer deterioration when the average fluid temperature in the segment is higher than the pseudo-critical point. The reason is explained with the generation of vapor-like film near the channel wall that the peak feature related to a nucleateboiling-like state and heat transfer deterioration related to a film-boiling-like state. The effects of parameters, including mass flow rate, pressure, and wall heat flux on flow and heat transfer were analyzed.In calculating of the averaged heat transfer coefficient of the whole channel, the traditional method shows significant deviation and the micro segment weighted average method is adopted. The pressure drop can mainly be affected by the mass flux and pressure and little affected by the wall heat flux. The peak of the convective heat transfer coefficient can only form at high mass flux, low wall heat flux, and near critical pressure, in which condition the nucleate-boiling-like state is easier to appear. Moreover,heat transfer deterioration will always appear, since the supercritical flow will finally develop into a filmboiling-like state. So heat transfer deterioration should be taken seriously in the design and safe operation of vaporizer PCHE. The study of this work clarified the local heat transfer and flow feature of supercritical methane in microchannel and contributed to the deep understanding of supercritical methane flow of the vaporization process in PCHE.

Hysteretic behavior of post-tensioned precast segmental CFT double-column piers

Considering the desirable behavior of concrete filled steel tube(CFT)columns and the complicated behavior of segmental double-column piers under cyclic loads,three post-tensioned precast segmental CFT double-column pier specimens were tested to extend their application in moderate and high seismicity areas.The effects of the number of CFT segments and the steel endplates as energy dissipaters on the seismic behavior of the piers were evaluated.The experimental results show that the segmental piers exhibited stable hysteretic behavior with small residual displacements under cyclic loads.All the tested specimens achieved a drift ratio no less than 13%without significant damage and strength deterioration due to the desirable behavior of CFT columns.Since the deformation of segmental columns was mainly concentrated at the column-footing interfaces,the increase of the segment numbers for each column had no obvious effects on the loading capacity but reduced the initial stiffness of the specimens.The use of steel endplates improved the bearing capacity,stiffness and energy dissipation of segmental piers,but weakened their self-centering capacity.Fiber models were also proposed to simulate the hysteretic behavior of the tested specimens,and the influences of segment numbers and prestress levels on seismic behavior were further studied.

Cascading multi-segment rupture process of the 2023 Turkish earthquake doublet on a complex fault system revealed by teleseismic P wave back projection method

In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.

Short- and long-term results of open vs laparoscopic multisegmental resection and anastomosis for synchronous colorectal cancer located in separate segments

BACKGROUND It remains unclear whether laparoscopic multisegmental resection and ana-stomosis(LMRA)is safe and advantageous over traditional open multisegmental resection and anastomosis(OMRA)for treating synchronous colorectal cancer(SCRC)located in separate segments.AIM To compare the short-term efficacy and long-term prognosis of OMRA as well as LMRA for SCRC located in separate segments.METHODS Patients with SCRC who underwent surgery between January 2010 and December 2021 at the Cancer Hospital,Chinese Academy of Medical Sciences and the Peking University First Hospital were retrospectively recruited.In accordance with the RESULTS LMRA patients showed markedly less intraoperative blood loss than OMRA patients(100 vs 200 mL,P=0.006).Compared to OMRA patients,LMRA patients exhibited markedly shorter postoperative first exhaust time(2 vs 3 d,P=0.001),postoperative first fluid intake time(3 vs 4 d,P=0.012),and postoperative hospital stay(9 vs 12 d,P=0.002).The incidence of total postoperative complications(Clavien-Dindo grade:≥II)was 2.9%and 17.1%(P=0.025)in the LMRA and OMRA groups,respectively,while the incidence of anastomotic leakage was 2.9%and 7.3%(P=0.558)in the LMRA and OMRA groups,respectively.Furthermore,the LMRA group had a higher mean number of lymph nodes dissected than the OMRA group(45.2 vs 37.3,P=0.020).The 5-year overall survival(OS)and disease-free survival(DFS)rates in OMRA patients were 82.9%and 78.3%,respectively,while these rates in LMRA patients were 78.2%and 72.8%,respectively.Multivariate prognostic analysis revealed that N stage[OS:HR hazard ratio(HR)=10.161,P=0.026;DFS:HR=13.017,P=0.013],but not the surgical method(LMRA/OMRA)(OS:HR=0.834,P=0.749;DFS:HR=0.812,P=0.712),was the independent influencing factor in the OS and DFS of patients with SCRC.CONCLUSION LMRA is safe and feasible for patients with SCRC located in separate segments.Compared to OMRA,the LMRA approach has more advantages related to short-term efficacy.

Anesthesia for extracorporeal membrane oxygenation-assisted thoracoscopic lower lobe subsegmental resection in a patient with a single left lung:A case report

BACKGROUND It is difficult and risky for patients with a single lung to undergo thoracoscopic segmental pneumonectomy,and previous reports of related cases are rare.We introduce anesthesia for Extracorporeal membrane oxygenation(ECMO)-assisted thoracoscopic lower lobe subsegmental resection in a patient with a single left lung.CASE SUMMARY The patient underwent comprehensive treatment for synovial sarcoma of the right lung and nodules in the lower lobe of the left lung.Examination showed pulmonary function that had severe restrictive ventilation disorder,forced expiratory volume in 1 second of 0.72 L(27.8%),forced vital capacity of 1.0 L(33%),and maximal voluntary ventilation of 33.9 L(35.5%).Lung computed tomography showed a nodular shadow in the lower lobe of the left lung,and lung metastasis was considered.After multidisciplinary consultation and adequate preoperative preparation,thoracoscopic left lower lung lobe S9bii+S10bii combined subsegmental resection was performed with the assistance of total intravenous anesthesia and ECMO intraoperative pulmonary protective ventilation.The patient received postoperative ICU supportive care.After surgical treatment,the patient was successfully withdrawn from ECMO on postoperative Day 1.The tracheal tube was removed on postoperative Day 4,and she was discharged from the hospital on postoperative Day 15.CONCLUSION The multi-disciplinary treatment provided maximum medical optimization for surgical anesthesia and veno-venous ECMO which provided adequate protection for the patient's perioperative treatment.

Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation

Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment options.Radiotherapy planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is presented.The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this work.The new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency regularisation.The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the baseline.The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.

Part-Whole Relational Few-Shot 3D Point Cloud Semantic Segmentation

This paper focuses on the task of few-shot 3D point cloud semantic segmentation.Despite some progress,this task still encounters many issues due to the insufficient samples given,e.g.,incomplete object segmentation and inaccurate semantic discrimination.To tackle these issues,we first leverage part-whole relationships into the task of 3D point cloud semantic segmentation to capture semantic integrity,which is empowered by the dynamic capsule routing with the module of 3D Capsule Networks(CapsNets)in the embedding network.Concretely,the dynamic routing amalgamates geometric information of the 3D point cloud data to construct higher-level feature representations,which capture the relationships between object parts and their wholes.Secondly,we designed a multi-prototype enhancement module to enhance the prototype discriminability.Specifically,the single-prototype enhancement mechanism is expanded to the multi-prototype enhancement version for capturing rich semantics.Besides,the shot-correlation within the category is calculated via the interaction of different samples to enhance the intra-category similarity.Ablation studies prove that the involved part-whole relations and proposed multi-prototype enhancement module help to achieve complete object segmentation and improve semantic discrimination.Moreover,under the integration of these two modules,quantitative and qualitative experiments on two public benchmarks,including S3DIS and ScanNet,indicate the superior performance of the proposed framework on the task of 3D point cloud semantic segmentation,compared to some state-of-the-art methods.

Computational Approach for Automated Segmentation and Classification of Region of Interest in Lateral Breast Thermograms

Breast cancer is one of the major health issues with high mortality rates and a substantial impact on patients and healthcare systems worldwide.Various Computer-Aided Diagnosis(CAD)tools,based on breast thermograms,have been developed for early detection of this disease.However,accurately segmenting the Region of Interest(ROI)fromthermograms remains challenging.This paper presents an approach that leverages image acquisition protocol parameters to identify the lateral breast region and estimate its bottomboundary using a second-degree polynomial.The proposed method demonstrated high efficacy,achieving an impressive Jaccard coefficient of 86%and a Dice index of 92%when evaluated against manually created ground truths.Textural features were extracted from each view’s ROI,with significant features selected via Mutual Information for training Multi-Layer Perceptron(MLP)and K-Nearest Neighbors(KNN)classifiers.Our findings revealed that the MLP classifier outperformed the KNN,achieving an accuracy of 86%,a specificity of 100%,and an Area Under the Curve(AUC)of 0.85.The consistency of the method across both sides of the breast suggests its viability as an auto-segmentation tool.Furthermore,the classification results suggests that lateral views of breast thermograms harbor valuable features that can significantly aid in the early detection of breast cancer.

Adaptive Segmentation for Unconstrained Iris Recognition

In standard iris recognition systems,a cooperative imaging framework is employed that includes a light source with a near-infrared wavelength to reveal iris texture,look-and-stare constraints,and a close distance requirement to the capture device.When these conditions are relaxed,the system’s performance significantly deteriorates due to segmentation and feature extraction problems.Herein,a novel segmentation algorithm is proposed to correctly detect the pupil and limbus boundaries of iris images captured in unconstrained environments.First,the algorithm scans the whole iris image in the Hue Saturation Value(HSV)color space for local maxima to detect the sclera region.The image quality is then assessed by computing global features in red,green and blue(RGB)space,as noisy images have heterogeneous characteristics.The iris images are accordingly classified into seven categories based on their global RGB intensities.After the classification process,the images are filtered,and adaptive thresholding is applied to enhance the global contrast and detect the outer iris ring.Finally,to characterize the pupil area,the algorithm scans the cropped outer ring region for local minima values to identify the darkest area in the iris ring.The experimental results show that our method outperforms existing segmentation techniques using the UBIRIS.v1 and v2 databases and achieved a segmentation accuracy of 99.32 on UBIRIS.v1 and an error rate of 1.59 on UBIRIS.v2.