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.

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.

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.

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.

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.

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.

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.

Dynamic SLAM Visual Odometry Based on Instance Segmentation:A Comprehensive Review

Dynamic Simultaneous Localization and Mapping(SLAM)in visual scenes is currently a major research area in fields such as robot navigation and autonomous driving.However,in the face of complex real-world envi-ronments,current dynamic SLAM systems struggle to achieve precise localization and map construction.With the advancement of deep learning,there has been increasing interest in the development of deep learning-based dynamic SLAM visual odometry in recent years,and more researchers are turning to deep learning techniques to address the challenges of dynamic SLAM.Compared to dynamic SLAM systems based on deep learning methods such as object detection and semantic segmentation,dynamic SLAM systems based on instance segmentation can not only detect dynamic objects in the scene but also distinguish different instances of the same type of object,thereby reducing the impact of dynamic objects on the SLAM system’s positioning.This article not only introduces traditional dynamic SLAM systems based on mathematical models but also provides a comprehensive analysis of existing instance segmentation algorithms and dynamic SLAM systems based on instance segmentation,comparing and summarizing their advantages and disadvantages.Through comparisons on datasets,it is found that instance segmentation-based methods have significant advantages in accuracy and robustness in dynamic environments.However,the real-time performance of instance segmentation algorithms hinders the widespread application of dynamic SLAM systems.In recent years,the rapid development of single-stage instance segmentationmethods has brought hope for the widespread application of dynamic SLAM systems based on instance segmentation.Finally,possible future research directions and improvementmeasures are discussed for reference by relevant professionals.

Real-Time Detection and Instance Segmentation of Strawberry in Unstructured Environment

The real-time detection and instance segmentation of strawberries constitute fundamental components in the development of strawberry harvesting robots.Real-time identification of strawberries in an unstructured envi-ronment is a challenging task.Current instance segmentation algorithms for strawberries suffer from issues such as poor real-time performance and low accuracy.To this end,the present study proposes an Efficient YOLACT(E-YOLACT)algorithm for strawberry detection and segmentation based on the YOLACT framework.The key enhancements of the E-YOLACT encompass the development of a lightweight attention mechanism,pyramid squeeze shuffle attention(PSSA),for efficient feature extraction.Additionally,an attention-guided context-feature pyramid network(AC-FPN)is employed instead of FPN to optimize the architecture’s performance.Furthermore,a feature-enhanced model(FEM)is introduced to enhance the prediction head’s capabilities,while efficient fast non-maximum suppression(EF-NMS)is devised to improve non-maximum suppression.The experimental results demonstrate that the E-YOLACT achieves a Box-mAP and Mask-mAP of 77.9 and 76.6,respectively,on the custom dataset.Moreover,it exhibits an impressive category accuracy of 93.5%.Notably,the E-YOLACT also demonstrates a remarkable real-time detection capability with a speed of 34.8 FPS.The method proposed in this article presents an efficient approach for the vision system of a strawberry-picking robot.

A semantic segmentation-based underwater acoustic image transmission framework for cooperative SLAM

With the development of underwater sonar detection technology,simultaneous localization and mapping(SLAM)approach has attracted much attention in underwater navigation field in recent years.But the weak detection ability of a single vehicle limits the SLAM performance in wide areas.Thereby,cooperative SLAM using multiple vehicles has become an important research direction.The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles.However,the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data.It is essential to compress the images before transmission.Recently,deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks,but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information.In this paper,we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression(SSIC)framework and the joint source-channel codec,to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver.The SSIC framework consists of Auto-Encoder structure-based sonar image compression network,which is measured by a semantic segmentation network's residual.Considering that sonar images have the characteristics of blurred target edges,the semantic segmentation network used a special dilated convolution neural network(DiCNN)to enhance segmentation accuracy by expanding the range of receptive fields.The joint source-channel codec with unequal error protection is proposed that adjusts the power level of the transmitted data,which deal with sonar image transmission error caused by the serious underwater acoustic channel.Experiment results demonstrate that our method preserves more semantic information,with advantages over existing methods at the same compression ratio.It also improves the error tolerance and packet loss resistance of transmission.

An Improved UNet Lightweight Network for Semantic Segmentation of Weed Images in Corn Fields

In cornfields,factors such as the similarity between corn seedlings and weeds and the blurring of plant edge details pose challenges to corn and weed segmentation.In addition,remote areas such as farmland are usually constrained by limited computational resources and limited collected data.Therefore,it becomes necessary to lighten the model to better adapt to complex cornfield scene,and make full use of the limited data information.In this paper,we propose an improved image segmentation algorithm based on unet.Firstly,the inverted residual structure is introduced into the contraction path to reduce the number of parameters in the training process and improve the feature extraction ability;secondly,the pyramid pooling module is introduced to enhance the network’s ability of acquiring contextual information as well as the ability of dealing with the small target loss problem;and lastly,Finally,to further enhance the segmentation capability of the model,the squeeze and excitation mechanism is introduced in the expansion path.We used images of corn seedlings collected in the field and publicly available corn weed datasets to evaluate the improved model.The improved model has a total parameter of 3.79 M and miou can achieve 87.9%.The fps on a single 3050 ti video card is about 58.9.The experimental results show that the network proposed in this paper can quickly segment corn weeds in a cornfield scenario with good segmentation accuracy.

Improved Convolutional Neural Network for Traffic Scene Segmentation

In actual traffic scenarios,precise recognition of traffic participants,such as vehicles and pedestrians,is crucial for intelligent transportation.This study proposes an improved algorithm built on Mask-RCNN to enhance the ability of autonomous driving systems to recognize traffic participants.The algorithmincorporates long and shortterm memory networks and the fused attention module(GSAM,GCT,and Spatial Attention Module)to enhance the algorithm’s capability to process both global and local information.Additionally,to increase the network’s initial operation stability,the original network activation function was replaced with Gaussian error linear unit.Experiments were conducted using the publicly available Cityscapes dataset.Comparing the test results,it was observed that the revised algorithmoutperformed the original algorithmin terms of AP_(50),AP_(75),and othermetrics by 8.7%and 9.6%for target detection and 12.5%and 13.3%for segmentation.

ProNet Adaptive Retinal Vessel Segmentation Algorithm Based on Improved UperNet Network

This paper proposes a new network structure,namely the ProNet network.Retinal medical image segmentation can help clinical diagnosis of related eye diseases and is essential for subsequent rational treatment.The baseline model of the ProNet network is UperNet(Unified perceptual parsing Network),and the backbone network is ConvNext(Convolutional Network).A network structure based on depth-separable convolution and 1×1 convolution is used,which has good performance and robustness.We further optimise ProNet mainly in two aspects.One is data enhancement using increased noise and slight angle rotation,which can significantly increase the diversity of data and help the model better learn the patterns and features of the data and improve the model’s performance.Meanwhile,it can effectively expand the training data set,reduce the influence of noise and abnormal data in the data set on the model,and improve the accuracy and reliability of the model.Another is the loss function aspect,and we finally use the focal loss function.The focal loss function is well suited for complex tasks such as object detection.The function will penalise the loss carried by samples that the model misclassifies,thus enabling better training of the model to avoid these errors while solving the category imbalance problem as a way to improve image segmentation density and segmentation accuracy.From the experimental results,the evaluation metrics mIoU(mean Intersection over Union)enhanced by 4.47%,and mDice enhanced by 2.92% compared to the baseline network.Better generalization effects and more accurate image segmentation are achieved.

Semantic segmentation via pixel-to-center similarity calculation

Since the fully convolutional network has achieved great success in semantic segmentation,lots of works have been proposed to extract discriminative pixel representations.However,the authors observe that existing methods still suffer from two typical challenges:(i)The intra-class feature variation between different scenes may be large,leading to the difficulty in maintaining the consistency between same-class pixels from different scenes;(ii)The inter-class feature distinction in the same scene could be small,resulting in the limited performance to distinguish different classes in each scene.The authors first rethink se-mantic segmentation from a perspective of similarity between pixels and class centers.Each weight vector of the segmentation head represents its corresponding semantic class in the whole dataset,which can be regarded as the embedding of the class center.Thus,the pixel-wise classification amounts to computing similarity in the final feature space between pixels and the class centers.Under this novel view,the authors propose a Class Center Similarity(CCS)layer to address the above-mentioned challenges by generating adaptive class centers conditioned on each scenes and supervising the similarities between class centers.The CCS layer utilises the Adaptive Class Center Module to generate class centers conditioned on each scene,which adapt the large intra-class variation between different scenes.Specially designed Class Distance Loss(CD Loss)is introduced to control both inter-class and intra-class distances based on the predicted center-to-center and pixel-to-center similarity.Finally,the CCS layer outputs the processed pixel-to-center similarity as the segmentation prediction.Extensive experiments demonstrate that our model performs favourably against the state-of-the-art methods.

SAM Era:Can It Segment Any Industrial Surface Defects?

Segment Anything Model(SAM)is a cutting-edge model that has shown impressive performance in general object segmentation.The birth of the segment anything is a groundbreaking step towards creating a universal intelligent model.Due to its superior performance in general object segmentation,it quickly gained attention and interest.This makes SAM particularly attractive in industrial surface defect segmentation,especially for complex industrial scenes with limited training data.However,its segmentation ability for specific industrial scenes remains unknown.Therefore,in this work,we select three representative and complex industrial surface defect detection scenarios,namely strip steel surface defects,tile surface defects,and rail surface defects,to evaluate the segmentation performance of SAM.Our results show that although SAM has great potential in general object segmentation,it cannot achieve satisfactory performance in complex industrial scenes.Our test results are available at:https://github.com/VDT-2048/SAM-IS.