Joint Segmentation Research Articles

Study on Mechanical Characteristics of Discontinuous Cut-Off Wall of Dam Foundation Based on Plastic Damage Calculation Method

Dam foundations are prone to leakage damage after being exposed to long-term water action, which seriously affects the operation safety of the dam. At present, concrete cut-off walls serve an important means of anti-seepage for dam foundations. However, due to construction challenges, the cut-off wall needs to be poured segment-by-segment during the construction process, and the joints between adjacent segments become weak parts for seepage prevention. Therefore, it is crucial to clarify the stress state of segmented discontinuous concrete cut-off walls. Based on the Lee-Fenves framework and the tension–compression constitutive relationship of fracture energy, a plastic damage calculation method was established in this paper to characterize the mechanical behavior of discontinuous cut-off walls. The method was then used to analyze the mechanical performance of discontinuous walls with segment joints containing slurry cake. The research results showed that compared to the continuous cut-off wall, the vertical settlement in the middle part of the discontinuous cut-off wall increased by 5.8%, and the displacement along the river flow direction decreased by 35.3%. As the wall segment width decreased, the joint opening and the degree of tensile damage were reduced accordingly, while the compressive damage in the middle and lower parts of the wall was intensified. As the wall depth decreased, the constraints and load on the bottom of the wall showed obvious changes, leading to a reduced stress and damage level of the wall. The findings provide reference for the design and safety control of cut-off walls.

Structural behavior of triple-layer composite lining of a water conveyance tunnel: Insight from full-scale loading tests

When constructing water conveyance shield tunnels under high internal pressure, composite linings are preferred over single-layer segmental linings due to the superior water tightness and load-bearing capacity. A triple-layer composite lining, consisting of an outer segmental lining, internal steel tube, and self-compacting concrete (SCC) filling, has recently been applied in a large-scale water conveyance tunnel project in China. However, its structural behavior under external overburden and internal water pressures remains poorly understood. This study investigates the mechanical behavior of the triple-layer composite lining through full-scale loading tests using a novel platform that simulates external and internal pressures. Results show that the composite lining remains highly elastic under combined loads with an internal pressure of 0.4 MPa. When the internal pressure increases to 0.6 MPa, cracks first appear in the SCC layer near segment joints, propagating uniformly and leading to stress redistribution. Studs on the steel tube-SCC interface strengthen bonding, reducing debonding at this interface while slightly increasing debonding at the SCC-segment interface. Despite localized SCC damage, the lining maintains excellent serviceability under cyclic pressure fluctuations. This study offers valuable insights for the design and construction of water conveyance shield tunnels with triple-layer composite linings, particularly in high-pressure environments.

Compression-shear capacity of circumferential joint with dowel in shield tunnel: From experiments to analytical solution

Dislocations of circumferential joints are commonly prevalent in shield tunnels. Generally, the displacement space of bolt in rigid segment joint is very small. When the dislocation of the circumferential joint is large, plastic deformation of the bolts or concrete crushing is inevitable, which will affect the service life of the shield tunnel. The flexibility of the joint can be achieved by embedding in a dowel, which can reduce the damage of the segment joint during the misalignment of the circumferential joint. However, the addition of the dowel changes the structure of the circumferential joint. At present, the shear bearing capacity of the circumferential joint, affecting the design, safety verification, and service performance evaluation, cannot be accurately calculated through the existing mechanical models. Therefore, the envelope curves of the shear bearing capacity of the circumferential joint with dowel were investigated in this paper. Firstly, a series of shear resistance experiments were conducted to clarify the failure characteristics of the circumferential joint with the dowel. Subsequently, based on the experimental results, several shear mechanical calculation models for the circumferential joint were proposed. And the analytical method for the envelope curves between axial force (N) and shear force (Q) was derived. Finally, the accuracy of the analytical method was verified, and corresponding optimization methods to improve the bearing performance of the circumferential joint were proposed. The research results indicate that the circumferential joint with dowel has both concrete shear stage and steel rebars shear stage. The N-Q envelope curves is determined by the combination of concrete, connectors (including the dowel and the bolt), and steel rebars, and the leading factor can be clarified by the proposed method. A constructive conclusion has been found that the optimization design of the circumferential joint must consider the axial force in order to effectively improve its shear bearing performance. The research results can serve the joint optimization, load-bearing verification during design process, and the physical model in big data analysis during the operation and maintenance of the shield tunnel.

A Unified Multi-Task Learning Model with Joint Reverse Optimization for Simultaneous Skin Lesion Segmentation and Diagnosis.

Classifying and segmenting skin cancer represent pivotal objectives for automated diagnostic systems that utilize dermoscopy images. However, these tasks present significant challenges due to the diverse shape variations of skin lesions and the inherently fuzzy nature of dermoscopy images, including low contrast and the presence of artifacts. Given the robust correlation between the classification of skin lesions and their segmentation, we propose that employing a combined learning method holds the promise of considerably enhancing the performance of both tasks. In this paper, we present a unified multi-task learning strategy that concurrently classifies abnormalities of skin lesions and allows for the joint segmentation of lesion boundaries. This approach integrates an optimization technique known as joint reverse learning, which fosters mutual enhancement through extracting shared features and limiting task dominance across the two tasks. The effectiveness of the proposed method was assessed using two publicly available datasets, ISIC 2016 and PH2, which included melanoma and benign skin cancers. In contrast to the single-task learning strategy, which solely focuses on either classification or segmentation, the experimental findings demonstrated that the proposed network improves the diagnostic capability of skin tumor screening and analysis. The proposed method achieves a significant segmentation performance on skin lesion boundaries, with Dice Similarity Coefficients (DSC) of 89.48% and 88.81% on the ISIC 2016 and PH2 datasets, respectively. Additionally, our multi-task learning approach enhances classification, increasing the F1 score from 78.26% (baseline ResNet50) to 82.07% on ISIC 2016 and from 82.38% to 85.50% on PH2. This work showcases its potential applicability across varied clinical scenarios.

Mechanical properties of segment joints in subway shield tunnels by a full-scale test

Mechanical properties of segment joints in subway shield tunnels by a full-scale test

Modeling the Segmental Lining Behavior Using an Analytical Segmental Joint Model

To investigate the influence of joints on the segmental lining behavior for a shield tunnel, an analytical model incorporating the effect of the concrete and gaskets is proposed for the segmental joints. Based on the results of the analytical model, a beam-spring numerical model is established for the segmental lining, and parametric studies of the gasket and bolt are conducted to reveal the segmental lining behavior. The numerical results indicate that the joint has a slight influence on the axial force of the segmental lining but significantly affects the bending moment and deformation of the segmental lining. With increasing the gasket thickness or decreasing the gasket hardness, the maximum moment is decreased and the ellipticity is increased, which leads the bending capacity of the segmental lining to be decreased significantly. Therefore, the influence of gaskets should be an indispensable factor during the design of the segmental lining. However, with increasing the bolt pretightening force or the bolt cross-sectional area, the bending capacity of the segmental lining is improved only to a certain extent. Since the ellipticity of the segmental lining is slightly influenced by these bolt parameters, the improvement in the bending capacity can even be neglected.

A foundation model for joint segmentation, detection and recognition of biomedical objects across nine modalities.

Biomedical image analysis is fundamental for biomedical discovery. Holistic image analysis comprises interdependent subtasks such as segmentation, detection and recognition, which are tackled separately by traditional approaches. Here, we propose BiomedParse, a biomedical foundation model that can jointly conduct segmentation, detection and recognition across nine imaging modalities. This joint learning improves the accuracy for individual tasks and enables new applications such as segmenting all relevant objects in an image through a textual description. To train BiomedParse, we created a large dataset comprising over 6 million triples of image, segmentation mask and textual description by leveraging natural language labels or descriptions accompanying existing datasets. We showed that BiomedParse outperformed existing methods on image segmentation across nine imaging modalities, with larger improvement on objects with irregular shapes. We further showed that BiomedParse can simultaneously segment and label all objects in an image. In summary, BiomedParse is an all-in-one tool for biomedical image analysis on all major image modalities, paving the path for efficient and accurate image-based biomedical discovery.

Automated Dental Registration and TMJ Segmentation for Virtual Surgical Planning of Orthognathic Surgery via Three-Step Computer-Based Method

Objective: This study developed and evaluated a computer-based method for automating the registration of scanned dental models with 3D reconstructed skulls and segmentation of the temporomandibular joint (TMJ). Methods: A dataset comprising 1274 skull models and corresponding scanned dental models was collected. In total, 1066 cases were used for the development of the computer-based method, while 208 cases were used for validation. Performance was evaluated by comparing the automated results with manual registration and segmentation performed by clinicians, using accuracy and completeness metrics (e.g. intersection of union [IoU] and Dice similarity coefficient [DSC]). Results: The automated registration achieved a mean absolute error of 0.35 mm for the maxilla and 0.38 mm for the mandible, and a root mean squared error of 0.46 mm and 0.39 mm, respectively. The automatic TMJ segmentation exhibited an accuracy of 97.48%, a precision of 97.06%, a IoU of 95.72%, DSC of 97.3%, and a Hausdorff value of 1.87 mm, which were sufficient for clinical application. Conclusion: The proposed method significantly improved the efficiency of orthognathic surgical planning by automating the registration and segmentation processes. The accuracy and precision of the automated results were sufficient for clinical use, reducing the workload on clinicians and facilitating faster and more reliable surgical planning. Clinical significance: The computer-based method streamlines orthognathic surgical planning, enhancing precision and efficiency without compromising clinical accuracy, ultimately improving patient outcomes and reducing the workload of surgeons.

Open reduction and internal fixation of mandibular condyle fractures using AUTOFIX 2.0® cannulated compression screw system: A technical innovation with intraoperative CT-scan

Condylar process fractures remain a matter of ongoing controversy in maxillofacial surgery because of variety of opinions and proposed treatment modalities offered in the literature. The trend is toward open reduction and internal fixation (ORIF) whenever there is displacement or dislocation combined with unstable occlusal conditions. The fundamental treatment goals are anatomical fixation and early return to function. However, ORIF of a fractured condyle carries significant risks, including facial nerve damage, and presents challenges in reducing and stabilizing the fractured segment in an area of limited access. We present here an innovative technique for ORIF of condylar head fractures. It consists in fracture reduction and stabilization with two Kirschner wires followed by osteosynthesis with two cannulated headless compression screws using the AUTOFIX 2.0® system (Stryker, Kalamazoo, USA). This new technique, combined with intraoperative CT scan, ensures satisfactory reduction and stabilization of the dislocated joint segment, thereby restoring the condyle to its physiologically normal position.

RELATIONSHIP BETWEEN SHOULDER FLEXIBILITY AND HANDSTAND ALIGNMENT IN DIFFERENT FEMALE ARTISTIC GYMNASTICS APPARATUS

A study was performed with the objective of analyzing the relationship between shoulder flexibility and handstand performance across three female artistic gymnastics apparatuses: floor exercise (FX), balance beam (BB), and uneven bars (UB). Twenty-one gymnasts participated and were divided into two groups: “G1”, consisting of twelve child gymnasts, and “G2”, consisting of nine adolescent gymnasts, aged 9 and 14 respectively. The study aimed to consider the sensitive phases for the development of flexibility. Using the Kinovea program, the shoulder angle (°H) in the handstand and the alignment of the joint segments (AJS) involved were analyzed. Participants underwent two different active shoulder flexibility tests (GFMT and FIG), and results were compared between the groups. Significant differences were found between groups in the GFMT test (p = 0.018), the FIG test (p = 0.026), and AJS in the BB handstand (p = 0.043). Significant relationships were also observed between the GFMT test and °H in UB (p = 0.021); and between the FIG test and °H in BB (p = 0.006), both withing the cild gymnast group. Overall, the adolescent group presented better scores, supporting the notion of greater body awareness and technical mastery of the handstand across the different female artistic gymnastics apparatuses.

Temporomandibular joint CBCT image segmentation via multi-view ensemble learning network.

Accurate segmentation of the temporomandibular joint (TMJ) from cone beam CT (CBCT) images holds significant clinical value for diagnosing temporomandibular joint osteoarthrosis (TMJOA) and related conditions. Convolutional neural network-based medical image segmentation methods have achieved state-of-the-art performance in various segmentation tasks. However, 3D medical images segmentation requires substantial global context and rich spatial semantic information, demanding much more GPU memory and computational resources. To address these challenges in 3D medical image segmentation, we propose a novel network- the MVEL-Net (Multi-view Ensemble Learning Network) for TMJ CBCT image segmentation. By resampling images along three dimensions, we generate multiple weak learners with different spatial semantic information. A subsequent strong learning network effectively integrates the outputs from these weak learners to achieve more accurate segmentation results. We evaluated our network model using a clinical dataset comprising 88 subjects with TMJ CBCT images. The average Dice similarity coefficient (DSC) was 0.9817 ± 0.0049, the average surface distance was 0.0540 ± 0.0179mm, and the 95% Hausdorff distance was 0.1743 ± 0.0550mm. Our proposed MVEL-Net demonstrates excellent segmentation performance on TMJ from CBCT images, while using fewer GPU memory resources compared to other 3D networks. The effectiveness of this method in capturing spatial context could be leveraged for tasks like organ segmentation from volumetric scans. This may facilitate wider adoption of AI-based solutions for automated analysis of 3D medical images.

Mechanical behaviour of double lining with outer segmental lining and inner fibre reinforced concrete lining under internal water pressure

Double lining with outer segmental lining and inner reinforced concrete lining is extensively used for water conveyance tunnel with internal water pressure. In order to improve the performance of the double lining, the steel rebar and fibre reinforced concrete (R/FRC) is designed for inner lining to enhance the tensile strength of reinforced concrete (RC). Full-scale tests were conducted to compare the mechanical behaviour of double lining with inner RC and R/FRC linings. The test results showed that both types of lining failed in four stages: elastic stage, inner lining cracking stage, crack stabilization and segmental joint damage stage, and failure stage. The cracks firstly occurred in the inner lining near the segmental joints and then propagated to the whole inner lining ring. Finally, the crack width and segmental joint opening successively exceeded the thresholds, and the double lining failed until the breaking of segmental joints. Compared with inner RC lining, R/FRC lining had better performance in terms of the initial cracking load, cracking-control ability, post-cracking stiffness, service limit capacity of inner lining and waterproof capacity of segmental lining. The improvement ratios were 36.3%, 50.3%, 46.3%, 37.2% and 31.1%, respectively. The influences of fibre volume fraction, fibre aspect ratio, and reinforcement ratio of steel rebar on the mechanical performance of R/FRC lining were also investigated based on a previously proposed analytical model. The parametric analysis demonstrated that higher fibre volume fraction and fibre aspect ratio can result in more significant increase of mechanical performance of R/FRC lining. The steel fibres were found to have more significant effects on the post-cracking behaviour of the lining than same amounts of reinforcing bars. Finally, an optimized function was proposed to fulfil a balance of lining performance and economy. The experimental and analytical results indicated that R/FRC lining can substitute RC lining and perform higher mechanical performance in water conveyance tunnels.

Research and implementation of intelligent diagnostic system for temporomandibular joint disorder

Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.

A reproducible representation of healthy tibiofemoral kinematics during stair descent using REFRAME – Part II: Exploring optimisation criteria and inter-subject differences

Kinematic analysis is a central component of movement biomechanics, describing the relative motion of joint segments during different activities, in different subject cohorts, and at different timepoints. Establishing whether two sets of kinematic signals represent fundamentally similar or different underlying motion patterns is especially challenging, given 1) the lack of consensus around reference frame and joint axis definition, and 2) the substantial effect that minimal variations in frame position and orientation are known to have on signal magnitude and characteristics. As such, enormous variability in the reporting of tibiofemoral kinematics has resulted in joint movement patterns that remain controversially discussed. Previously, we demonstrated the ability of the REference FRame Alignment MEthod (REFRAME) to reorientate and reposition differently aligned local segment frames to achieve convergence in signals representing the same underlying motion, thereby offering a novel approach to consistently report joint motion. In this study, for the first time, we apply REFRAME to assess the rotational and translational in vivo tibiofemoral motion of ten healthy subjects during stair descent based on kinematic signals collected using a moving videofluoroscope. Kinematics were analysed before and after different REFRAME implementations, revealing generally neutral ab/adduction behaviour, accompanied by varying degrees of a sinusoidal int/external tibial rotation pattern over the activity cycle. Our data demonstrate that different selected implementations of REFRAME are able to highlight different characteristics of the motion patterns: Minimisation of the translational root-mean-square revealed proximodistal translation patterns with overall neutral progression, while anteroposterior translation showed seemingly different levels of correlation with flexion/extension in different subjects. On the other hand, REFRAME minimisation of translational variances exposed differences in the relative mean displacement between the femoral and tibial origins between subjects, highlighting differences in mean centre of rotation positions. This early application of REFRAME for providing an understanding of tibiofemoral kinematics demonstrates the potential of this novel approach to bring clarity to an otherwise complex representation of highly variable time-series signals, while highlighting the philosophical challenges of clinically interpretating kinematic signals in the first place.

Deep coupled registration and segmentation of multimodal whole-brain images.

Recent brain mapping efforts are producing large-scale whole-brain images using different imaging modalities. Accurate alignment and delineation of anatomical structures in these images are essential for numerous studies. These requirements are typically modeled as two distinct tasks: registration and segmentation. However, prevailing methods, fail to fully explore and utilize the inherent correlation and complementarity between the two tasks. Furthermore, variations in brain anatomy, brightness, and texture pose another formidable challenge in designing multi-modal similarity metrics. A high-throughput approach capable of overcoming the bottleneck of multi-modal similarity metric design, while effective leveraging the highly correlated and complementary nature of two tasks is highly desirable. We introduce a deep learning framework for joint registration and segmentation of multi-modal brain images. Under this framework, registration and segmentation tasks are deeply coupled and collaborated at two hierarchical layers. In the inner layer, we establish a strong feature-level coupling between the two tasks by learning a unified common latent feature representation. In the outer layer, we introduce a mutually supervised dual-branch network to decouple latent features and facilitate task-level collaboration between registration and segmentation. Since the latent features we designed are also modality-independent, the bottleneck of designing multi-modal similarity metric is essentially addressed. Another merit offered by this framework is the interpretability of latent features, which allows intuitive manipulation of feature learning, thereby further enhancing network training efficiency and the performance of both tasks. Extensive experiments conducted on both multi-modal and mono-modal datasets of mouse and human brains demonstrate the superiority of our method. The code is available at https://github.com/tingtingup/DCRS.

Joint segmentation of tumors in 3D PET-CT images with a network fusing multi-view and multi-modal information

Objective. Joint segmentation of tumors in positron emission tomography-computed tomography (PET-CT) images is crucial for precise treatment planning. However, current segmentation methods often use addition or concatenation to fuse PET and CT images, which potentially overlooks the nuanced interplay between these modalities. Additionally, these methods often neglect multi-view information that is helpful for more accurately locating and segmenting the target structure. This study aims to address these disadvantages and develop a deep learning-based algorithm for joint segmentation of tumors in PET-CT images.Approach. To address these limitations, we propose the Multi-view Information Enhancement and Multi-modal Feature Fusion Network (MIEMFF-Net) for joint tumor segmentation in three-dimensional PET-CT images. Our model incorporates a dynamic multi-modal fusion strategy to effectively exploit the metabolic and anatomical information from PET and CT images and a multi-view information enhancement strategy to effectively recover the lost information during upsamping. A Multi-scale Spatial Perception Block is proposed to effectively extract information from different views and reduce redundancy interference in the multi-view feature extraction process.Main results. The proposed MIEMFF-Net achieved a Dice score of 83.93%, a Precision of 81.49%, a Sensitivity of 87.89% and an IOU of 69.27% on the Soft Tissue Sarcomas dataset and a Dice score of 76.83%, a Precision of 86.21%, a Sensitivity of 80.73% and an IOU of 65.15% on the AutoPET dataset.Significance. Experimental results demonstrate that MIEMFF-Net outperforms existing state-of-the-art models which implies potential applications of the proposed method in clinical practice.

Can eye-tracking help to create a new method for X-ray analysis of rheumatoid arthritis patients, including joint segmentation and scoring methods?

Reading hand and foot X-rays in rheumatoid arthritis patients is difficult and time-consuming. In research, physicians use the modified Sharp van der Heijde Sharp (mvdH) score by reading of hand and foot radiographs. The aim of this study was to create a new method of determining the mvdH via eye tracking and to study its concordance with the mvdH score. We created a new method of quantifying the mvdH score based on reading time of a reader monitored via eye tracking (Tobii Pro Lab software) after training with the aid of a metronome. Radiographs were read twice by the trained eye-tracking reader and once by an experienced reference radiologist. A total of 440 joints were selected; 416 could be interpreted for erosion, and 396 could be interpreted for joint space narrowing (JSN) when read by eye tracking (eye tracking could not measure the time spent when two pathological joints were too close together). The agreement between eye tracking mvdH Sharp score and classical mvdH Sharp score yes (at least one erosion or JSN) versus no (no erosion or no JSN) was excellent for both erosions (kappa 0.97; 95% CI: 0.96-0.99) and JSN (kappa: 0.95; 95% CI: 0.93-0.097). This agreement by class (0 to 10) remained excellent for both erosions (kappa 0.82; 95% CI: 0.79-0.0.85) and JSN (kappa: 0.68; 95% CI: 0.65-0.0.71). To conclude, eye-tracking reading correlates strongly with classical mvdH-Sharp and is useful for assessing severity, segmenting joints and establishing a rapid score for lesions.

Using 2D U-Net convolutional neural networks for automatic acetabular and proximal femur segmentation of hip MRI images and morphological quantification: a preliminary study in DDH

BackgroundDevelopmental dysplasia of the hip (DDH) is a common pediatric orthopedic condition characterized by varying degrees of acetabular dysplasia and hip dislocation. Current 2D imaging methods often fail to provide sufficient anatomical detail for effective treatment planning, leading to higher rates of misdiagnosis and missed diagnoses. MRI, with its advantages of being radiation-free, multi-planar, and containing more anatomical information, can provide the crucial morphological and volumetric data needed to evaluate DDH. However, manual techniques for measuring parameters like the center–edge angle (CEA) and acetabular index (AI) are time-consuming. Automating these processes is essential for accurate clinical assessments and personalized treatment strategies.MethodsThis study employed a U-Net-based CNN model to automate the segmentation of hip MRI images in children. The segmentation process was validated using a leave-one-out method during training. Subsequently, the segmented hip joint images were utilized in clinical settings to perform automated measurements of key angles: AI, femoral neck angle (FNA), and CEA. This automated approach aimed to replace manual measurements and provide an objective reference for clinical assessments.ResultsThe U-Net-based network demonstrates high effectiveness in hip segmentation compared to manual radiologist segmentations. In test data, it achieves average DSC values of 0.9109 (acetabulum) and 0.9244 (proximal femur), with a 91.76% segmentation success rate. The average ASD values are 0.3160 mm (acetabulum) and 0.6395 mm (proximal femur) in test data, with Ground Truth (GT) edge points and predicted segmentation maps having a mean distance of less than 1 mm. Using automated segmentation models for clinical hip angle measurements (CEA, AI, FNA) shows no statistical difference compared to manual measurements (p > 0.05).ConclusionUtilizing U-Net-based image segmentation and automated measurement of morphological parameters significantly enhances the accuracy and efficiency of DDH assessment. These methods improve precision in automatic measurements and provide an objective basis for clinical diagnosis and treatment of DDH.

Выдавливание замкового блока при строительстве транспортных тоннелей большого диаметра

Purpose: to consider the problem of the key-segment extrusion that occurs during the construction of subway tunnels in St. Petersburg using prefabricated high-precision reinforced concrete linings with increased water resistance with a diameter of 10.3 m. To analyze the possible processes leading to this phenomenon and methods of solving the problem in construction conditions. To determine the consequences of the currently used solutions to the problem. To develop an analytical methodology for determining the magnitude of the friction force acting on the radial joints planes of contacting segments based on classical structural mechanics methods (displacement method). To carry out mathematical modeling of the extrusion process by the finite element method and compare its results with those obtained analytically. To develop recommendations for the elimination of block extrusion, which can be both applied in construction conditions and taken into account when designing lining structures. To analyze the effectiveness of the proposed recommendations. Methods: full-scale data from constructed objects, analytical calculations and mathematical modeling by the finite element method were used. Results: an analytical method is proposed for determining the magnitude of the pushing force of the key-segment and the friction force in the radial segment joints. The results of mathematical modeling showed high convergence of the results obtained by the two methods. Recommendations have been developed to eliminate the phenomenon of extrusion, and their effectiveness has been evaluated. Practical importance: the results of the study can be used at the design stage of prefabricated reinforced concrete linings with increased water resistance for large-diameter tunnels, as well as in construction conditions at the time of installation of the key segment.

MultiJSQ: Direct joint segmentation and quantification of left ventricle with deep multitask‐derived regression network

MultiJSQ: Direct joint segmentation and quantification of left ventricle with deep multitask‐derived regression network