Segment Boundaries Research Articles

A GM-JMNS-CPHD Filter for Different-Fields-of-View Stochastic Outlier Selection for Nonlinear Motion Tracking.

Most multi-target movements are nonlinear in the process of movement. The common multi-target tracking filtering methods directly act on the multi-target tracking system of nonlinear targets, and the fusion effect is worse under the influence of different perspectives. Aiming to determine the influence of different perspectives on the fusion accuracy of multi-sensor tracking in the process of target tracking, this paper studies the multi-target tracking fusion strategy of a nonlinear system with different perspectives. A GM-JMNS-CPHD fusion technique is introduced for random outlier selection in multi-target tracking, leveraging sensors with limited views. By employing boundary segmentation from distinct perspectives, the posterior intensity function undergoes decomposition into multiple sub-intensities through SOS clustering. The distribution of target numbers within the respective regions is then characterized by the multi-Bernoulli reconstruction cardinal distribution. Simulation outcomes demonstrate the robustness and efficacy of this approach. In comparison to other algorithms, this method exhibits enhanced robustness even amidst a decreased detection probability and heightened clutter rates.

Whole embryo biomechanics with reverberant optical coherence elastography

Many morphogenesis processes during embryo development are fundamentally biomechanical processes, and disruption of these events can lead to debilitating congenital abnormalities. Imaging the biomechanical properties of embryos could provide insight into developmental disorders and could open new therapy avenues. However, current methods are invasive and are incapable of producing viscoelasticity maps of live samples in 3D. To overcome these limitations, we propose the use of reverberant shear wave fields in combination with optical coherence tomography (OCT) for high-resolution elastography at different developmental stages of murine embryos. A 1 kHz quasi-harmonic stimulation was applied to induce the diffuse shear field, which leveraged the heterogeneous microstructure and boundaries of the different tissue segments in the embryos. With this approach, we show how the shear wave speed (i.e., stiffness) of the spine, heart, and mid-brain increased as the embryo developed from embryonic day (E) 9.5 to E 11.5 at five separate stages. This noncontact technique is a promising method for imaging the biomechanical properties of different embryo structures during development with important applications for understanding developmental diseases and exploring treatments.

TD Swin-UNet: Texture-Driven Swin-UNet with Enhanced Boundary-Wise Perception for Retinal Vessel Segmentation.

Retinal vessel segmentation plays a crucial role in medical image analysis, aiding ophthalmologists in disease diagnosis, monitoring, and treatment guidance. However, due to the complex boundary structure and rich texture features in retinal blood vessel images, existing methods have challenges in the accurate segmentation of blood vessel boundaries. In this study, we propose the texture-driven Swin-UNet with enhanced boundary-wise perception. Firstly, we designed a Cross-level Texture Complementary Module (CTCM) to fuse feature maps at different scales during the encoding stage, thereby recovering detailed features lost in the downsampling process. Additionally, we introduced a Pixel-wise Texture Swin Block (PT Swin Block) to improve the model's ability to localize vessel boundary and contour information. Finally, we introduced an improved Hausdorff distance loss function to further enhance the accuracy of vessel boundary segmentation. The proposed method was evaluated on the DRIVE and CHASEDB1 datasets, and the experimental results demonstrate that our model obtained superior performance in terms of Accuracy (ACC), Sensitivity (SE), Specificity (SP), and F1 score (F1), and the accuracy of vessel boundary segmentation was significantly improved.

Boundary-Repairing Dual-Path Network for Retinal Layer Segmentation in OCT Image with Pigment Epithelial Detachment.

Automatic retinal layer segmentation in optical coherence tomography (OCT) images is crucial for the diagnosis of ocular diseases. Currently, automatic retinal layer segmentation works well with normal OCT images. However, pigment epithelial detachment (PED) dramatically alters the retinal structure, causing blurred boundaries and partial disappearance of the Bruch's Membrane (BM), thus posing challenges to the segmentation. To tackle these problems, we propose a novel dual-path U-shaped network for simultaneous layer segmentation and boundary regression. This network first designs a feature interaction fusion (FIF) module to strengthen the boundary shape constraints in the layer path. To address the challenge posed by partial BM disappearance and boundary-blurring, we propose a layer boundary repair (LBR) module. This module aims to use contrastive loss to enhance the confidence of blurred boundary regions and refine the segmentation of layer boundaries through the re-prediction head. In addition, we introduce a novel bilateral threshold distance map (BTDM) designed for the boundary path. The BTDM serves to emphasize information within boundary regions. This map, combined with the updated probability map, culminates in topology-guaranteed segmentation results achieved through a topology correction (TC) module. We investigated the proposed network on two severely deformed datasets (i.e., OCTA-500 and Aier-PED) and one slightly deformed dataset (i.e., DUKE). The proposed method achieves an average Dice score of 94.26% on the OCTA-500 dataset, which was 1.5% higher than BAU-Net and outperformed other methods. In the DUKE and Aier-PED datasets, the proposed method achieved average Dice scores of 91.65% and 95.75%, respectively.

In-Plane crushing performance of bionic glass Sponge-Type honeycomb structures

Honeycomb structures are commonly adopted due to their superior energy absorption capacity. In this study, a new bionic glass sponge–type honeycomb structure (BSH) with a quadrilateral octagonal mesh microstructure inspired by the sea sponge structure was proposed. The in-plane crushing performance of the BSH with different geometrical parameters under different crushing speeds was investigated by ABAQUS/Explicit. The numerical findings suggested that the BSH displayed stronger energy absorption in contrast to the square, hexagonal and hierarchical honeycombs at both quasi-static and dynamic crushing conditions. More plastic hinges and more unit walls involved in deformation resulted in a high energy absorption capacity. In addition, three typical deformation modes of the BSH under different loading speeds were discussed, and the empirical model to predict the plateau stress of the BSH was established based on the shock wave theory. Finally, the effect of boundary segmentation parameter m on crushing performance was also illustrated. The energy absorption capacity reaches a maximum at m = 3 for quasi–static loading, whereas at m = 5 for dynamic loading. These findings provide valuable insights into the optimization of bionic honeycombs.

BMFA-Net: Boundary Constraint Multi-Level Feature Aggregation Framework for Precise Polyp Segmentation

Colorectal cancer is one of the three most common cancers worldwide. Polyps are complex and have unclear boundaries, often leading to inaccurate boundary segmentation and missed detections. To address these challenges, we propose a boundary constraint multi-level feature aggregation framework called BMFA-Net to precisely segment polyps. The framework comprises four key modules. First, the parallel partial decoder is introduced to aggregate high-level features within the network to generate a globally informative semantic map serving as the initial guidance region for reverse erasing the foreground. Second, we propose an efficient atrous convolution attention module to effectively aggregate local and global contextual information over multiple levels. Additionally, a multi-level feature aggregation mechanism is designed and placed among the efficient atrous convolution attention modules to enable the network to capture a large amount of semantic structure while preserving intricate details. Finally, a boundary constraint reverse attention module is proposed to perform the boundary constraint while removing the foreground to improve the quality of boundary segmentation. Extensive experiments demonstrated the superiority and versatility of our framework compared with state-of-the-art methods; specifically, it achieved a mean Dice score of 0.922 on the CVC-300 dataset.

Spiral complete coverage path planning based on conformal slit mapping in multi-connected domains

The generation of smoother and shorter spiral complete coverage paths in multi-connected domains is a crucial research topic in path planning for robotic cavity machining and other related fields. Traditional methods for spiral path planning in multi-connected domains typically incorporate a subregion division procedure that leads to excessive subregion bridging, requiring longer, more sharply turning, and unevenly spaced spirals to achieve complete coverage. To address this issue, this paper proposes a novel spiral complete coverage path planning method using conformal slit mapping. It takes advantage of the fact that conformal slit mapping can transform multi-connected domains into regular disks or annuluses without the need for subregion division. Firstly, a slit mapping calculation technique is proposed for segmented cubic spline boundaries with corners. Secondly, a spiral path spacing control method is developed based on the maximum inscribed circle radius between adjacent conformal slit mapping iso-parameters. Thirdly, the spiral coverage path is derived by offsetting iso-parameters. Numerical experiments indicate that our method shares a comparable order-of-magnitude in computation time with the traditional PDE-based spiral complete coverage path method, but it excels in optimizing total path length, smoothness, and spacing consistency. Finally, we performed experiments on cavity milling and dry runs to compare the new method with the traditional PDE-based method in terms of machining duration and steering impact, respectively. The comparison reveals that, with both algorithms achieving complete coverage, the new algorithm reduces machining time and steering impact by 12.34% and 22.78%, respectively, compared with the traditional PDE-based method.

Saliency and boundary guided segmentation framework for cell counting in microscopy images

Improving the segmentation accuracy of cells from microscopy images directly benefits medical practitioners for differential cell counting, disease diagnosis, and treatment. However, separating each cell continues to be challenging, especially when the cells appear in clumps. Recent advancements in deep learning have demonstrated success in automating cell segmentation and counting, but they necessitate large volumes of annotated data for training. Creating precise ground truth for cell segmentation is laborious and demands expertise due to the intricate and varied characteristics of microscopy images. Consequently, this complexity poses a challenge to the practicality of implementing deep neural network-based solutions for microscopy cell segmentation. This paper addresses a significant research gap by exploring the underutilized traditional image features to build a lightweight deep-learning framework for cell boundary segmentation that can be effectively trained with a limited amount of labeled data. We have developed Saliency Guided Attention Module Network (SGAM-net), which uses perceptual features inherent along the cell boundaries and poses cell segmentation as a thick boundary prediction problem with the aim to separate the cells. SGAM-net uses a specialized attention mechanism to combine the perceptual feature maps with the learned features. When tested on the three public bench-mark microscopy datasets, with respect to the base Unet model, there is an increase of 1.36%, 1.29%, and 6.35% in Dice score and 2.39%, 2.31% and 6.99% in Intersection Over Union (IOU) score. The model requires only 23.07% less parameters than Unet++ model. The potential of SGAM-net in segmenting boundaries semantically has been demonstrated, and it is further processed to compute cell counts. The model outperformed the state-of-the-art instance separation method ‘Stardist’ in cell counting by giving a reduction ratio of 2.34, 1.89, and 2.03 on the metric ‘mean absolute error’.

Local crack arrestability and deformation microstructure evolution of hydrogen-related fracture in martensitic steel

Intergranular cracks in the uncharged specimen were arrested at low-angle prior austenite grain boundary (PAGB) segments of several micrometers. In contrast, even small, low-angle PAGB segments with sub-micrometer sizes impeded the propagation of hydrogen-related intergranular crack. At the hydrogen-related quasi-cleavage crack tip, the crystallographic orientation changed abruptly, and deformation microstructures developed, including the formation of low-energy dislocation structures. A certain degree of crack growth resistance (intrinsic crack growth resistance) in the hydrogen-related fractures could be attributed to the intense localized plastic works involved in the arrest of intergranular cracks and the propagation of quasi-cleavage cracks.

MSGAT: Multi-scale gated axial reverse attention transformer network for medical image segmentation

Medical image segmentation can help doctors accurately identify and locate various structures, tissues, or lesions in the patient's body, providing an important basis for clinical diagnosis. However, the segmentation of medical images is seriously affected by the complexity of the structures in the images, the variety of morphologies, the uneven contrast, and the blurring of the boundaries between the target tissues and the background. Therefore, we propose a Multiscale Gated Axial Reverse Attention Transformer (MSGAT) for medical image segmentation. First, we use the Pyramid Vision Transformer (PVT) as the backbone network to extract the high-level features and introduce the Channel Received Field Block (CRFB) to filter the interfering information in the high-level features, and then we augment and aggregate the high-level features separately. Specifically, on the one hand, the Multiscale Feature Enhancement Module (MSFEM) is used to obtain multiscale contextual information to enhance the positional information of the target, and on the other hand, Multiscale Parallel Partial Decoder (MSPPD) is employed to aggregate the high-level feature maps to generate rough segmentation maps. Finally, the rough segmentation map is used to guide the gated axial reverse attention (GARA) combined with the position information in the enhanced feature map to refine the segmentation boundaries and gradually mine the detailed information of the target. MSGAT is evaluated against state-of-the-art methods on skin cancer (ISIC2016, PH2), polyp (Kvair), and gland (GlaS) datasets. Results demonstrate MSGAT's superiority, with an average improvement of 6.19% and 9.35% in mIoU and DSC metrics, respectively, compared to existing methods.

Sensitivity of Post-TAVR Hemodynamics to the Distal Aortic Arch Anatomy: A High-Fidelity CFD Study.

Patient-specific simulations of transcatheter aortic valve (TAV) using computational fluid dynamics (CFD) often rely on assumptions regarding proximal and distal anatomy due to the limited availability of high-resolution imaging away from the TAV site and the primary research focus being near the TAV. However, the influence of these anatomical assumptions on computational efficiency and resulting flow characteristics remains uncertain. This study aimed to investigate the impact of different distal aortic arch anatomies-some of them commonly used in literature-on flow and hemodynamics in the vicinity of the TAV using large eddy simulations (LES). Three aortic root anatomical configurations with four representative distal aortic arch types were considered in this study. The arch types included a 90-degree bend, an idealized distal aortic arch anatomy, a clipped version of the idealized distal aortic arch, and an anatomy extruded along the normal of segmented anatomical boundary. Hemodynamic parameters both instantaneous and time-averaged such as Wall Shear Stress (WSS), and Oscillatory Shear Index (OSI) were derived and compared from high-fidelity CFD data. While there were minor differences in flow and hemodynamics across the configurations examined, they were generally not significant within our region of interest i.e., the aortic root. The choice of extension type had a modest impact on TAV hemodynamics, especially in the vicinity of the TAV with variations observed in local flow patterns and parameters near the TAV. However, these differences were not substantial enough to cause significant deviations in the overall flow and hemodynamic characteristics. The results suggest that under the given configuration and boundary conditions, the type of outflow extension had a modest impact on hemodynamics proximal to the TAV. The findings contribute to a better understanding of flow dynamics in TAV configurations, providing insights for future studies in TAV-related experiments as well as numerical simulations. Additionally, they help mitigate the uncertainties associated with patient-specific geometries, offering increased flexibility in computational modeling.

VENet: Variational energy network for gland segmentation of pathological images and early gastric cancer diagnosis of whole slide images

Background and objective: Gland segmentation of pathological images is an essential but challenging step for adenocarcinoma diagnosis. Although deep learning methods have recently made tremendous progress in gland segmentation, they have not given satisfactory boundary and region segmentation results of adjacent glands. These glands usually have a large difference in glandular appearance, and the statistical distribution between the training and test sets in deep learning is inconsistent. These problems make networks not generalize well in the test dataset, bringing difficulties to gland segmentation and early cancer diagnosis.Methods: To address these problems, we propose a Variational Energy Network named VENet with a traditional variational energy Lv loss for gland segmentation of pathological images and early gastric cancer detection in whole slide images (WSIs). It effectively integrates the variational mathematical model and the data-adaptability of deep learning methods to balance boundary and region segmentation. Furthermore, it can effectively segment and classify glands in large-size WSIs with reliable nucleus width and nucleus-to-cytoplasm ratio features.Results: The VENet was evaluated on the 2015 MICCAI Gland Segmentation challenge (GlaS) dataset, the Colorectal Adenocarcinoma Glands (CRAG) dataset, and the self-collected Nanfang Hospital dataset. Compared with state-of-the-art methods, our method achieved excellent performance for GlaS Test A (object dice 0.9562, object F1 0.9271, object Hausdorff distance 73.13), GlaS Test B (object dice 94.95, object F1 95.60, object Hausdorff distance 59.63), and CRAG (object dice 95.08, object F1 92.94, object Hausdorff distance 28.01). For the Nanfang Hospital dataset, our method achieved a kappa of 0.78, an accuracy of 0.9, a sensitivity of 0.98, and a specificity of 0.80 on the classification task of test 69 WSIs.Conclusions: The experimental results show that the proposed model accurately predicts boundaries and outperforms state-of-the-art methods. It can be applied to the early diagnosis of gastric cancer by detecting regions of high-grade gastric intraepithelial neoplasia in WSI, which can assist pathologists in analyzing large WSI and making accurate diagnostic decisions.

AM-UNet: Field Ridge Segmentation of Paddy Field Images Based on an Improved MultiResUNet Network

In order to solve the problem of image boundary segmentation caused by the irregularity of paddy fields in southern China, a high-precision segmentation method based on the improved MultiResUNet model for paddy field mapping is proposed, combining the characteristics of paddy field scenes. We introduce the attention gate (AG) mechanism at the end of the encoder–decoder skip connections in the MultiResUNet model to generate the weights and highlight the response of the field ridge area, add an atrous spatial pyramid pooling (ASPP) module after the end of the encoder down-sampling, use an appropriate combination of expansion rates to improve the identification of small-scale edge details, use 1 × 1 convolution to improve the range of the sensory field after bilinear interpolation to increase the segmentation accuracy, and, thus, construct the AM-UNet paddy field ridge segmentation model. The experimental results show that the IoU, precision, and F1 value of the AM-UNet model are 88.74%, 93.45%, and 93.95%, respectively, and that inference time for a single image is 168ms, enabling accurate and real-time segmentation of field ridges in a complex paddy field environment. Thus, the AM-UNet model can provide technical support for the development of vision-based automatic navigation systems for agricultural machines.

Automated Classification of Abdominal Ultrasound lmages of the Pancreas Based on the Spectral Representation of the Border’s Contours

The purpose of the research is to develop a methodology for classifying complexly structured halftone images based on a multimodal approach using methods of morphological analysis, spectral analysis and neural network modeling.Methods. А method for classifying the contours of the boundaries of segments of a complexly structured image is described. Тhe method is based on the fact that in chronic diseases of the pancreas, there is a violation of the integrity of the contour of its border and its waviness increases due to retractions and bulges caused by an alterative inflammatory process. Тhe method includes the stages of normalization of ultrasound images and image segmentation with the selection of the contour of the object of interest. Тo classify the contour of a segment boundary, it is proposed to use Fourier analysis and neural network technologies. Тhe method is illustrated using the example of classifying the contour of the border of the pancreas on its transcutaneous acoustic image.Results. Еxperimental studies of the proposed methods and means for classifying medical risk were carried out on diagnostic tasks according to the following classes: "chronic pancreatitis" – "without pathology". For experimental studies, video sequences of ultrasound images of the pancreas provided by an endoscopist were used. Тhe purpose of the experimental studies was to analyze the classification quality indicators of image classifiers with class segments "Chronic pancreatitis" and "Without pathology". Тhe training sample of video images (frames of video sequences) included 200 examples, one hundred from each class. Тhe quality indicator "Sensitivity" of classification for two classes is 85,7%, the indicator "Specificity" is 87,1%.Тhe use of the contour analysis method in classifiers of ultrasound images of the pancreas opens up new opportunities for accessible and objective diagnosis of pancreatic diseases, expanding the capabilities of intelligent clinical decision support systems.

A Novel Deep Learning Approach for Brain Tumor Segmentation and Classification

Automated brain tumor detection and segmentation are crucial in medical diagnostics as they give details about functional structures and also potential unusual tissue that is required to define surgical strategies. Segmentation of images is one of the most difficult issues in the area of MRI scan analysis research. However, due to low contrast, automatic tumor segmentation, accuracy issues, and ill-defined boundaries remain difficult. For the prevention of brain tumors, an investigation was developed for implementing a novel framework. To overcome the problem faced by traditional existing techniques, this paper proposed an automatic brain tumor segmentation and detection system using the proposed dense convolution neural network without human intervention, where an intrinsic set of features are extracted to perform the classification task. The main aim of this paper is to segment the tumor portion from the affected MRI using a novel clustering approach. Moreover, a lot of scholars have researched the diagnosis of brain tumors, yet the accuracy-based performance metric in diagnosis results is low. A deep learning framework is proposed to evaluate brain disease more accurately using a Black Widow Optimization driven Dense Convolution Neural Network (BW-DCNN). The various performances are assessed, and the results are compared to those of other existing classifiers and approaches.

Bento: a toolkit for subcellular analysis of spatial transcriptomics data

The spatial organization of molecules in a cell is essential for their functions. While current methods focus on discerning tissue architecture, cell–cell interactions, and spatial expression patterns, they are limited to the multicellular scale. We present Bento, a Python toolkit that takes advantage of single-molecule information to enable spatial analysis at the subcellular scale. Bento ingests molecular coordinates and segmentation boundaries to perform three analyses: defining subcellular domains, annotating localization patterns, and quantifying gene–gene colocalization. We demonstrate MERFISH, seqFISH + , Molecular Cartography, and Xenium datasets. Bento is part of the open-source Scverse ecosystem, enabling integration with other single-cell analysis tools.

The application of Hyperpixel segmentation algorithm in Chinese painting

Chinese painting, as an important component of traditional Chinese art, not only expresses Chinese culture and history, but also contains rich artistic connotations. Therefore, extracting and analyzing the main objects of Chinese painting is of great significance. The research introduces a fusion deep network based super pixel segmentation algorithm for image segmentation, and utilizes user marked segmentation target objects and backgrounds. The maximum similarity region merging idea is adopted to extract the main subject objects in Chinese painting, thereby achieving a deep understanding of the work. The results show that the boundary recall and segmentation accuracy of the Chinese painting subject object segmentation algorithm based on super pixels on the BuptPainting dataset are as high as 94.38% and 98.06%, respectively, with an undersegmentation error rate of only 7.69%. Meanwhile, the average accuracy value, optimal data scale value, and optimal image scale value of this method on the BuptPainting dataset are as high as 0.657, 0.541, and 0.529, respectively. The method proposed by the research institute has significant advantages in segmenting the main objects of Chinese painting, providing new ideas and methods for image analysis and processing of modern Chinese painting, and helping to further explore the artistic charm and cultural connotations of Chinese painting.

Hemodynamic property incorporated brain tumor segmentation by deep learning and density-based analysis of dynamic susceptibility contrast-enhanced magnetic resonance imaging (MRI).

Magnetic resonance imaging (MRI) is a primary non-invasive imaging modality for tumor segmentation, leveraging its exceptional soft tissue contrast and high resolution. Current segmentation methods typically focus on structural MRI, such as T1-weighted post-contrast-enhanced or fluid-attenuated inversion recovery (FLAIR) sequences. However, these methods overlook the blood perfusion and hemodynamic properties of tumors, readily derived from dynamic susceptibility contrast (DSC) enhanced MRI. This study introduces a novel hybrid method combining density-based analysis of hemodynamic properties in time-dependent perfusion imaging with deep learning spatial segmentation techniques to enhance tumor segmentation. First, a U-Net convolutional neural network (CNN) is employed on structural images to delineate a region of interest (ROI). Subsequently, Hierarchical Density-Based Scans (HDBScan) are employed within the ROI to augment segmentation by exploring intratumoral hemodynamic heterogeneity through the investigation of tumor time course profiles unveiled in DSC MRI. The approach was tested and evaluated using a cohort of 513 patients from the open-source University of Pennsylvania glioblastoma database (UPENN-GBM) dataset, achieving a 74.83% Intersection over Union (IoU) score when compared to structural-only segmentation. The algorithm also exhibited increased precision and localized predictions of heightened segmentation boundary complexity, resulting in a 146.92% increase in contour complexity (ICC) compared to the reference standard provided by the UPENN-GBM dataset. Importantly, segmenting tumors with the developed new approach uncovered a negative correlation of the tumor volume with the scores in the Karnofsky Performance Scale (KPS) clinically used for assessing the functional status of patients (-0.309), which is not observed with the prevailing segmentation standard. This work demonstrated that including hemodynamic properties of tissues from DSC MRI can improve existing structural or morphological feature-based tumor segmentation techniques with additional information on tumor biology and physiology. This approach can also be applied to other clinical indications that use perfusion MRI for diagnosis or treatment monitoring.

A Real-time augmented reality robot integrated with artificial intelligence for skin tumor surgery - experimental study and case series.

Skin tumors affect many people worldwide, and surgery is the first treatment choice. Achieving precise preoperative planning and navigation of intraoperative sampling remains a problem and is excessively reliant on the experience of surgeons, especially for Mohs surgery for malignant tumors. To achieve precise preoperative planning and navigation of intraoperative sampling, we developed a real-time augmented reality (AR) surgical system integrated with artificial intelligence (AI) to enhance three functions: AI-assisted tumor boundary segmentation, surgical margin design, and navigation in intraoperative tissue sampling. Non-randomized controlled trials were conducted on manikin, tumor-simulated rabbits, and human volunteers in xxx Laboratory to evaluate the surgical system. The results showed that the accuracy of the benign and malignant tumor segmentation were 0.9556 and 0.9548, respectively, and the average AR navigation mapping error was 0.644mm. The proposed surgical system was applied in 106 skin tumor surgeries, including intraoperative navigation of sampling in 16 Mohs surgery cases. Surgeons who have used this system highly recognize it. The surgical system highlighted the potential to achieve accurate treatment of skin tumors and to fill the gap in global research on skin tumor surgery systems.

BUA-Net: Boundary and uncertainty-aware attention network for lumbar multi-region magnetic resonance imaging segmentation

Background and Objective:Multi-region segmentation by axial lumbar magnetic resonance imaging (MRI) is important for the diagnosis of lumbar spinal stenosis (LSS) and lumbar disc herniation (LDH) disease. Deep learning-based methods have been widely used in this field, but the segmentation task is still challenging due to intra-class grayscale unevenness, inter-class low contrast and unclear boundaries. Methods:To solve the above problems, we propose a novel boundary and uncertainty-aware attention network (BUA-Net). It is structured around a single encoder and two decoders, each dedicated to either multi-region or boundary segmentation. We use an uncertainty-aware attention (UA) module to extract regions with large segmentation uncertainty and generate an attention mask. The learning and supervisory capabilities of the model are enhanced by a fine seg (FS) module and an attention supervision loss (ASL) in the boundaries and blurred regions. Moreover, a multi-scale spatial channel attention (MSCA) module is proposed that refines the high-level features without increasing any layers. Results:The results of the BUA-Net experiments were tested against seven networks and showed a 0.26% improvement in Dice scores, a 0.86% improvement in precision, and a 0.2% improvement in recall. In metrics related to boundary segmentation effectiveness, our method nearly matches the state-of-the-art in the ASD metric, surpasses it by 2.32 in the 95HD metric, and leads by 2.04, 2.68, and 1.22 in the IVD, TS, and PE categories, respectively. Conclusions:The experimental results demonstrate that our proposed method outperforms other state-of-the-art methods and shows excellent segmentation ability in the boundaries and blurred regions.