Segment Boundaries Research Articles

Monetised sustainability impacts of integrated planning in the manufactured construction products industry: A transport perspective from New Zealand

<p style="text-align: justify;"><span style="font-size: 14pt; font-family: 'times new roman', times, serif;">The extensive, interdisciplinary nature of construction supply chains make them prone to inefficiencies at organisational interfaces. Inefficiencies are accentuated by the project-centric delivery paradigm, and complex logistics systems between multiple stakeholders. They manifest as a multitude of concurrent activities, processes, and systems both on and off-site. Transportation is the largest component of the logistics domain. Transport operations are inherently fragmented, intrinsic to every business, while vehicle ownership and deployment is typically externalised. Differentiated waste removal and materials delivery further disintegrate the already fragmented construction supply chain. Inefficiencies from the insularity of the planning process across segmental boundaries aggregate incrementally, with impacts visible at the macro level. Re-configuration of activities, resources and actors are acknowledged strategies for optimising logistics and transportation function. This paper investigates the impact of three integration strategies on a manufactured construction products supply chain efficiency. These strategies include vertical integration of distribution, integrated planning for transport operations, and integration of reverse logistics into operations. Sustainability impacts are evaluated according to domestically determined monetary parameters in order to benchmark performance at the business and national scale.</span></p>

Selecting the Right Metric: A Detailed Study on Image Segmentation Evaluation

<p dir="ltr"><span>Image segmentation is critical role in various computer vision tasks, such as object recognition, medical imaging, autonomous driving, and document analysis. To evaluate the performance of segmentation algorithms, various metrics have been developed, each providing insights into different aspects of segmentation accuracy, object completeness, and boundary precision. This paper presents a comprehensive review of the key evaluation metrics used in image segmentation, including popular metrics such as Intersection over Union, Dice Coefficient, F1-score, and boundary-based metrics like average and maximum boundary distance. We explore the taxonomy of evaluation techniques, distinguishing between supervised and unsupervised methods, and discuss the strengths, limitations, and applications of each metric. Furthermore, we address the challenges associated with image segmentation, including handling noise, occlusions, illumination variation, and the scarcity of annotated datasets for training. By offering a detailed analysis of evaluation metrics, this paper aims to guide researchers and practitioners in selecting appropriate metrics for specific tasks and improving algorithm performance through informed comparisons and parameter tuning.</span></p><div><span><br /></span></div>

Semantic segmentation network for mangrove tree species based on UAV remote sensing images

Mangroves are special vegetation that grows in the intertidal zone of the coast and has extremely high ecological and environmental value. Different mangrove species exhibit significant differences in ecological functions and environmental responses, so accurately identifying and distinguishing these species is crucial for ecological protection and monitoring. However, mangrove species recognition faces challenges, such as morphological similarity, environmental complexity, target size variability, and data scarcity. Traditional mangrove monitoring methods mainly rely on expensive and operationally complex multispectral or hyperspectral remote sensing sensors, which have high data processing and storage costs, hindering large-scale application and popularization. Although hyperspectral monitoring is still necessary in certain situations, the low identification accuracy in routine monitoring severely hinders ecological analysis. To address these issues, this paper proposes the UrmsNet segmentation network, aimed at improving identification accuracy in routine monitoring while reducing costs and complexity. It includes an improved lightweight convolution SCConv, an Adaptive Selective Attention Module (ASAM), and a Cross-Layer Feature Fusion Module (CLFFM). ASAM adaptively extracts and fuses features of different mangrove species, enhancing the network’s ability to characterize mangrove species with similar morphology and in complex environments. CLFFM combines shallow details and deep semantic information to ensure accurate segmentation of mangrove boundaries and small targets.Additionally, this paper constructs a high-quality RGB image dataset for mangrove species segmentation to address the data scarcity problem. Compared to traditional methods, our approach is more precise and efficient. While maintaining relatively low parameters and computational complexity (FLOPs), it achieves excellent performance with mIoU and mPA metrics of 92.21% and 95.98%, respectively. This performance is comparable to the latest methods using multispectral or hyperspectral data but significantly reduces cost and complexity. By combining periodic hyperspectral monitoring with UrmsNet-supported routine monitoring, a more comprehensive and efficient mangrove ecological monitoring can be achieved.These research findings provide a new technical approach for large-scale, low-cost monitoring of important ecosystems such as mangroves, with significant theoretical and practical value. Furthermore, UrmsNet also demonstrates excellent performance on LoveDA, Potsdam, and Vaihingen datasets, showing potential for wider application.

CLFNet: a multi-modal data fusion network for traffic sign extraction

Abstract When using image data for signage extraction, poor visibility conditions such as insufficient light, rainy days, and low light intensity are encountered, leading to low accuracy and poor boundary segmentation in vision-based detection methods. To address this problem, we propose a cross-modal latent feature fusion network for signage detection, which obtains rich boundary information by combining images with light detection and ranging depth images, thus compensating for the pseudo-boundary phenomenon that may occur when using a single RGB image segmentation. First, HRNet is utilized as the backbone network to extract the boundary information of the point cloud depth map and RGB image by introducing the boundary extraction module; Second, the sensitivity to the boundary is enhanced by applying the feature aggregation module to deeply fuse the extracted boundary information with the image features; Finally, boundary Intersection over Union (IOU) is introduced as an evaluation index. The results show that the method performs more superiorly compared to the mainstream RGBD network, with an improvement of 5.5% and 6.1% in IOU and boundary IOU, and an accuracy of 98.3% and 96.2%, respectively, relative to the baseline network.

Graph Regulation Network for Point Cloud Segmentation.

In point cloud, some regions typically exist nodes from multiple categories, i.e., these regions have both homophilic and heterophilic nodes. However, most existing methods ignore the heterophily of edges during the aggregation of the neighborhood node features, which inevitably mixes unnecessary information of heterophilic nodes and leads to blurred boundaries of segmentation. To address this problem, we model the point cloud as a homophilic-heterophilic graph and propose a graph regulation network (GRN) to produce finer segmentation boundaries. The proposed method can adaptively adjust the propagation mechanism with the degree of neighborhood homophily. Moreover, we build a prototype feature extraction module, which is utilised to mine the homophily features of nodes from the global prototype space. Theoretically, we prove that our convolution operation can constrain the similarity of representations between nodes based on their degree of homophily. Extensive experiments on fully and weakly supervised point cloud semantic segmentation tasks demonstrate that our method achieves satisfactory performance. Especially in the case of weak supervision, that is, each sample has only 1%-10% labeled points, the proposed method has a significant improvement in segmentation performance.

A Parkinson's disease-related nuclei segmentation network based on CNN-Transformer interleaved encoder with feature fusion.

Automatic segmentation of Parkinson's disease (PD) related deep gray matter (DGM) nuclei based on brain magnetic resonance imaging (MRI) is significant in assisting the diagnosis of PD. However, due to the degenerative-induced changes in appearance, low tissue contrast, and tiny DGM nuclei size in elders' brain MRI images, many existing segmentation models are limited in the application. To address these challenges, this paper proposes a PD-related DGM nuclei segmentation network to provide precise prior knowledge for aiding diagnosis PD. The encoder of network is designed as an alternating encoding structure where the convolutional neural network (CNN) captures spatial and depth texture features, while the Transformer complements global position information between DGM nuclei. Moreover, we propose a cascaded channel-spatial-wise block to fuse features extracted by the CNN and Transformer, thereby achieving more precise DGM nuclei segmentation. The decoder incorporates a symmetrical boundary attention module, leveraging the symmetrical structures of bilateral nuclei regions by constructing signed distance maps for symmetric differences, which optimizes segmentation boundaries. Furthermore, we employ a dynamic adaptive region of interests weighted Dice loss to enhance sensitivity towards smaller structures, thereby improving segmentation accuracy. In qualitative analysis, our method achieved optimal average values for PD-related DGM nuclei (DSC: 0.854, IOU: 0.750, HD95: 1.691 mm, ASD: 0.195 mm). Experiments conducted on multi-center clinical datasets and public datasets demonstrate the good generalizability of the proposed method. Furthermore, a volumetric analysis of segmentation results reveals significant differences between HCs and PDs. Our method holds promise for assisting clinicians in the rapid and accurate diagnosis of PD, offering a practical method for the imaging analysis of neurodegenerative diseases.

Effects of Language Proficiency on Selective Attention Patterns at Segmenting Boundaries in English Audio Sentences

Background/Objectives: Normative perceptual segmentation facilitates event perception, comprehension, and memory. Given that native English listeners’ normative perceptual segmentation of English speech streams coexists with a highly selective attention pattern at segmentation boundaries, it is significant to test whether Chinese learners of English have a different attention pattern at boundaries, thereby checking whether they perform a normative segmentation. Methods: Thirty Chinese learners of English with relatively higher language proficiency (CLH) and 26 with relatively lower language proficiency (CLL) listened to a series of English audio sentences. Meanwhile, they were asked to press the key whenever a phonetic probe “ba” occurred. Response time to “ba” reflects the attention where “ba” is located at segmentation boundaries. Results: The results revealed that, (1) relative to native English listeners’ highly selective attention pattern, the CLH group showed a relatively selective attention pattern, while the CLL group displayed a non-selective attention pattern. (2) Both the CLH group and natives had better recognition memory than the CLL group. (3) Both the CLH group and natives’ attention at segmentation boundaries was not correlated with their memory for sentences, while the CLL group’s attention at boundaries was correlated with memory. Conclusions: These findings suggest that (1) Chinese learners of English did not perform a normative segmentation, which shows the effect of English proficiency on perceptual segmentation; (2) English proficiency has a superior effect on memory for sentences, while perceptual segmentation would come next to support memory by providing structure for memory construction if English proficiency is not high; (3) a comparison of attention patterns between Chinese learners and natives can provide a reference for potential intervention to rectify non-natives, thereby improving their perception of English speech streams.

MBF-Net: Multi-scale boundary-aware aggregation for bi-directional information exchange and feature reshaping for medical image segmentation

As a critical component of computer-aided diagnosis systems, medical image segmentation plays a vital role in assisting clinicians in making rapid and accurate decisions and formulating treatment plans. Nevertheless, precise medical image segmentation still presents a number of challenges, including insufficient feature extraction capabilities in the presence of limited sample sizes, blurred segmentation boundaries, and information loss between the encoder and decoder. In order to address these issues, we propose a Multi-Scale Boundary-Aware Aggregation Network with Bidirectional Information Exchange and Feature Refinement (MBF-Net) for medical image segmentation. Initially, we design a Multi-Scale Boundary-Aware Aggregation Encoder (MBAE) that aggregates features from different scales and pixel levels within the input images, capturing fine-grained boundary information in deep features and establishing comprehensive global and local multi-scale contextual dependencies. This design significantly enhances the model's understanding of the overall image structure and its ability to discern subtle differences between lesions and background. Subsequently, a Multi-Scale Bidirectional Information Transmission (MBIT) module is introduced, which integrates bidirectional information flow between low-level and high-level features, enabling multi-scale features to flow bidirectionally across different layers. The MBIT module effectively preserves crucial boundary details during cross-layer information transmission, thereby bridging the semantic gap between the encoder and decoder, and thereby improving the clarity of the segmentation boundaries. Finally, we develop a Feature Refinement and Aggregation Fusion (FRAF) module, designed to integrate feature information from various semantic levels, which alleviates discrepancies between features at varying scales, thus enhancing the segmentation accuracy of the network. The generalisation and effectiveness of MBF-Net are validated through comprehensive experiments on a range of tasks, including nuclear segmentation, breast cancer segmentation, polyp segmentation and skin lesion segmentation. Both subjective and objective evaluations demonstrate that MBF-Net significantly outperforms current state-of-the-art methods, achieving average Dice Similarity Coefficient (DSC) and Intersection over Union (IoU) scores of 86.34 % and 78.37 %, respectively. The superior performance of MBF-Net in terms of segmentation accuracy and quality is demonstrated across five public datasets.

CRNet: Cascaded Refinement Network for polyp segmentation

Technology for automatic segmentation plays a crucial role in the early diagnosis and treatment of ColoRectal Cancer (CRC). Existing polyp segmentation methods often focus on advanced feature extraction while neglecting detailed low-level features, This somewhat limits the enhancement of segmentation performance. This paper proposes a new technique called the Cascaded Refinement Network (CRNet), designed to improve polyp segmentation performance by combining low-level and high-level features through a cascaded contextual network structure. To accurately capture the morphological variations of polyps and enhance the clarity of segmentation boundaries, we have designed the Multi-Scale Feature Optimization (MFO) module and the Contextual Edge Guidance (CEG) module. Additionally, to further enhance feature fusion and utilization, we introduced the Cascaded Local Feature Fusion (CLFF) module, which effectively integrates cross-layer correlations, allowing the network to understand complex polyp structures better. By conducting a large number of experiments, our model achieved a 0.3% and 3.1% higher mDice score than the latest MMFIL-Net in the two main datasets of Kvasir-SEG and CVC-ClinicDB, respectively. Ablation studies show that MFO improves the baseline score by 4%, and the network without CLFF and CEG results in a reduction of 2.4% and 1.7% in mDice scores, respectively. This further validates the contribution of each module to the polyp segmentation performance. CRNet enhances model performance through the introduction of multiple modules but also increases model complexity. Future work will explore how to reduce computational complexity and improve inference speed while maintaining high performance. The source code for this paper can be found at https://github.com/l1986036/CRNet.

Pyramid boundary attention network for breast lesion segmentation in ultrasound images

Breast cancer is one of the most common causes of death among women worldwide and poses a major threat to women’s health. Currently, ultrasound examination is widely used for early detection of breast cancer, and accurate segmentation of breast lesions from ultrasound images is beneficial for early diagnosis of breast cancer. In this study, a pyramid boundary attention network was developed to automatically and stably segment breast lesions. In addition, a pyramid squeeze attention module was introduced into U-Net to increase the network’s receptive field and establish global attention by using convolutional kernels of different sizes, thereby improving the network’s detection and segmentation capabilities for tumors of different sizes. Additionally, to address the problem of fuzzy boundaries of breast tumor in ultrasound images, a boundary branch network was designed that used the contextual local attention module to extract the feature maps of different dimensions from the encoder and integrated local spatial and high-level semantic context information. The boundary fusion module was used to extract global and local information of tumor boundaries to obtain the boundary map of tumors, thereby refining the boundary segmentation of tumors and improving the segmentation accuracy of tumors. The proposed method was validated on two public datasets, BUSI and Dataset B, with dice coefficients of 78.98% and 84.5%, respectively. Compared with other medical image segmentation methods and the latest semantic segmentation methods, the proposed method showed superior performance in segmenting breast lesion in ultrasound images.

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.

FEBE-Net: Feature Exploration Attention and Boundary Enhancement Refinement Transformer Network for Bladder Tumor Segmentation

The automatic and accurate segmentation of bladder tumors is a key step in assisting urologists in diagnosis and analysis. At present, existing Transformer-based methods have limited ability to restore local detail features and insufficient boundary segmentation capabilities. We propose FEBE-Net, which aims to effectively capture global and remote semantic features, preserve more local detail information, and provide clearer and more precise boundaries. Specifically, first, we use PVT v2 backbone to learn multi-scale global feature representations to adapt to changes in bladder tumor size and shape. Secondly, we propose a new feature exploration attention module (FEA) to fully explore the potential local detail information in the shallow features extracted by the PVT v2 backbone, eliminate noise, and supplement the missing fine-grained details for subsequent decoding stages. At the same time, we propose a new boundary enhancement and refinement module (BER), which generates high-quality boundary clues through boundary detection operators to help the decoder more effectively preserve the boundary features of bladder tumors and refine and adjust the final predicted feature map. Then, we propose a new efficient self-attention calibration decoder module (ESCD), which, with the help of boundary clues provided by the BER module, gradually and effectively recovers global contextual information and local detail information from high-level features after calibration enhancement and low-level features after exploration attention. Extensive experiments on the cystoscopy dataset BtAMU and five colonoscopy datasets have shown that FEBE-Net outperforms 11 state-of-the-art (SOTA) networks in segmentation performance, with higher accuracy, stronger robust stability, and generalization ability.

Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application.

To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application. 825 condyles of 490 CBCT images from 3 centers of Stomatology hospitalaffliated to Zhejiang University School of Medicinewere collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed. The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755mm (cortex), 0.826mm (marrow), and 0.760mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved. The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.

Along-strike extent of earthquakes on multi-segment reverse faults; insights from the Nevis-Cardrona Fault, Aotearoa New Zealand

Evaluating fault segmentation is important for our understanding of seismic hazard assessment and fault growth. However, it is still unclear what controls if reverse fault earthquakes will rupture across segment boundaries. Here, we combine fault mapping and trench data from the low slip rate (0.04-0.15 mm/yr) multi-segment Nevis-Cardrona Fault (NCF) in the South Island of Aotearoa New Zealand to assess if it has ruptured in single or multi-segment earthquakes during the late Quaternary. Two new trenches on its Nevis segment provide stratigraphic evidence for two surface rupturing earthquakes, which through Optically Stimulated Luminscence dating and OxCal modelling, are constrained to have occurred at 28.9 +12.9 -9.1 ka and 12.8 ± 4.9 ka. The most recent timing is only weakly correlated to surface rupture timings from two trenches along the NCF's NW Cardrona segment. Furthermore, the 2 ± 1 m Nevis segment single event displacements we estimate would be unusually low for a ~85 km long NCF multi-segment rupture. We therefore surmise that late Quaternary NCF surface rupturing earthquakes did not rupture through ~30-50° bends that link these segments. Our trench data and fault mapping also indicate lower slip rates on the Nevis segment than previous studies (0.04-0.1 mm/yr vs 0.4 mm/yr).

TopoSinGAN: Learning a Topology-Aware Generative Model from a Single Image

Generative adversarial networks (GANs) have significantly advanced synthetic image generation, yet ensuring topological coherence remains a challenge. This paper introduces TopoSinGAN, a topology-aware extension of the SinGAN framework, designed to enhance the topological accuracy of generated images. TopoSinGAN incorporates a novel, differentiable topology loss function that minimizes terminal node counts along predicted segmentation boundaries, thereby addressing topological anomalies not captured by traditional losses. We evaluate TopoSinGAN using agricultural and dendrological case studies, demonstrating its capability to maintain boundary continuity and reduce undesired loop openness. A novel evaluation metric, Node Topology Clustering (NTC), is proposed to assess topological attributes independently of geometric variations. TopoSinGAN significantly improves topological accuracy, reducing NTC index values from 15.15 to 3.94 for agriculture and 14.55 to 2.44 for dendrology, compared to the baseline SinGAN. Modified FID evaluations also show improved realism, with lower FID scores: 0.1914 for agricultural fields compared to 0.2485 for SinGAN, and 0.0013 versus 0.0014 for dendrology. The topology loss enables end-to-end training with direct topological feedback. This new framework advances the generation of topologically accurate synthetic images, with applications in fields requiring precise structural representations, such as geographic information systems (GIS) and medical imaging.

Multi-Modal LiDAR Point Cloud Semantic Segmentation with Salience Refinement and Boundary Perception

Point cloud segmentation is essential for scene understanding, which provides advanced information for many applications, such as autonomous driving, robots, and virtual reality. To improve the accuracy and robustness of point cloud segmentation, many researchers have attempted to fuze camera images to complement the color and texture information. The common fusion strategy is the combination of convolutional operations with concatenation, element-wise addition or element-wise multiplication. However, conventional convolutional operators tend to confine the fusion of modal features within their receptive fields, which can be incomplete and limited. In addition, the inability of encoder–decoder segmentation networks to explicitly perceive segmentation boundary information results in semantic ambiguity and classification errors at object edges. These errors are further amplified in point cloud segmentation tasks, significantly affecting the accuracy of point cloud segmentation. To address the above issues, we propose a novel self-attention multi-modal fusion semantic segmentation network for point cloud semantic segmentation. Firstly, to effectively fuze different modal features, we propose a self-cross fusion module (SCF), which models long-range modality dependencies and transfers complementary image information to the point cloud to fully leverage the modality-specific advantages. Secondly, we design the salience refinement module (SR), which calculates the importance of channels in the feature maps and global descriptors to enhance the representation capability of salient modal features. Finally, we propose the local-aware anisotropy loss measure the element-level importance in the data and explicitly provide boundary information for the model, which alleviates the inherent semantic ambiguity problem in segmentation networks. Extensive experiments on two benchmark datasets demonstrate that our proposed method surpasses current state-of-the-art methods.

Toward assessment of rupture risk predictors in abdominal aortic aneurysms including intraluminal thrombus based on 3D+t ultrasound images.

Image-based patient-specific rupture risk analysis for abdominal aortic aneurysms (AAAs) has shown considerable promise. However, clinical translation has been hampered by the use of invasive and costly imaging modalities. Despite being a promising alternative, ultrasound (US) makes a full analysis, including intraluminal thrombus (ILT), not trivial. This study explored the feasibility of assessing AAA rupture risk parameters, e.g., peak wall stress (PWS) and peak wall rupture index (PWRI), using US-based models of the AAA wall, finally including ILT. Three-dimensional US data were segmented from a group of AAA patients whose CT data were available within 30 days. The segmented vessel wall and ILT boundaries were converted into a mesh including and excluding ILT to evaluate the effect of adding ILT on the model output. US-based rupture risk parameters (PWS and PWRI) were compared to CT-based results. The US-based PWS and PWRI, including ILT, showed good agreement with CT-based results, and the model excluding ILT showed no significant bias in wall stress or rupture index. When including ILT, a lower US-based wall stress and rupture index of 7.2% and 3.8% were found, respectively. The intraclass correlation coefficient (ICC) of PWS was 0.60. The highest ICC was found for the PWRI (ICC = 0.86), indicating good absolute agreement. This study showed that PWRI can be estimated with US when including the ILT, yielding comparable results to CT, and good absolute agreement. Future work should focus on improving the contrast of ILT in US, since this will be essential to performing large-scale studies in AAA cohorts.

RSPS-SAM: A Remote Sensing Image Panoptic Segmentation Method Based on SAM

Satellite remote sensing images contain complex and diverse ground object information and the images exhibit spatial multi-scale characteristics, making the panoptic segmentation of satellite remote sensing images a highly challenging task. Due to the lack of large-scale annotated datasets for panoramic segmentation, existing methods still suffer from weak model generalization capabilities. To mitigate this issue, this paper leverages the advantages of the Segment Anything Model (SAM), which can segment any object in remote sensing images without requiring any annotations and proposes a high-resolution remote sensing image panoptic segmentation method called Remote Sensing Panoptic Segmentation SAM (RSPS-SAM). Firstly, to address the problem of global information loss caused by cropping large remote sensing images for training, a Batch Attention Pyramid was designed to extract multi-scale features from remote sensing images and capture long-range contextual information between cropped patches, thereby enhancing the semantic understanding of remote sensing images. Secondly, we constructed a Mask Decoder to address the limitation of SAM requiring manual input prompts and its inability to output category information. This decoder utilized mask-based attention for mask segmentation, enabling automatic prompt generation and category prediction of segmented objects. Finally, the effectiveness of the proposed method was validated on the high-resolution remote sensing image airport scene dataset RSAPS-ASD. The results demonstrate that the proposed method achieves segmentation and recognition of foreground instances and background regions in high-resolution remote sensing images without the need for prompt input, while providing smooth segmentation boundaries with a panoptic segmentation quality (PQ) of 57.2, outperforming current mainstream methods.

Mamba- and ResNet-Based Dual-Branch Network for Ultrasound Thyroid Nodule Segmentation.

Accurate segmentation of thyroid nodules in ultrasound images is crucial for the diagnosis of thyroid cancer and preoperative planning. However, the segmentation of thyroid nodules is challenging due to their irregular shape, blurred boundary, and uneven echo texture. To address these challenges, a novel Mamba- and ResNet-based dual-branch network (MRDB) is proposed. Specifically, the visual state space block (VSSB) from Mamba and ResNet-34 are utilized to construct a dual encoder for extracting global semantics and local details, and establishing multi-dimensional feature connections. Meanwhile, an upsampling-convolution strategy is employed in the left decoder focusing on image size and detail reconstruction. A convolution-upsampling strategy is used in the right decoder to emphasize gradual feature refinement and recovery. To facilitate the interaction between local details and global context within the encoder and decoder, cross-skip connection is introduced. Additionally, a novel hybrid loss function is proposed to improve the boundary segmentation performance of thyroid nodules. Experimental results show that MRDB outperforms the state-of-the-art approaches with DSC of 90.02% and 80.6% on two public thyroid nodule datasets, TN3K and TNUI-2021, respectively. Furthermore, experiments on a third external dataset, DDTI, demonstrate that our method improves the DSC by 10.8% compared to baseline and exhibits good generalization to clinical small-scale thyroid nodule datasets. The proposed MRDB can effectively improve thyroid nodule segmentation accuracy and has great potential for clinical applications.

Enhancing Brain Tumor Diagnosis with L-Net: A Novel Deep Learning Approach for MRI Image Segmentation and Classification.

Background: Brain tumors are highly complex, making their detection and classification a significant challenge in modern medical diagnostics. The accurate segmentation and classification of brain tumors from MRI images are crucial for effective treatment planning. This study aims to develop an advanced neural network architecture that addresses these challenges. Methods: We propose L-net, a novel architecture combining U-net for tumor boundary segmentation and a convolutional neural network (CNN) for tumor classification. These two units are coupled such a way that the CNN classifies the MRI images based on the features extracted by the U-net while segmenting the tumor, instead of relying on the original input images. The model is trained on a dataset of 3064 high-resolution MRI images, encompassing gliomas, meningiomas, and pituitary tumors, ensuring robust performance across different tumor types. Results: L-net achieved a classification accuracy of up to 99.6%, surpassing existing models in both segmentation and classification tasks. The model demonstrated effectiveness even with lower image resolutions, making it suitable for diverse clinical settings. Conclusions: The proposed L-net model provides an accurate and unified approach to brain tumor segmentation and classification. Its enhanced performance contributes to more reliable and precise diagnosis, supporting early detection and treatment in clinical applications.