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The processing and analysis of computed tomography (CT) imaging is important for both basic scientific development and clinical applications. In AutoCT, we provide a comprehensive pipeline that integrates an end-to-end automatic preprocessing, registration, segmentation, and quantitative analysis of 3D CT scans. The engineered pipeline enables atlas-based CT segmentation and quantification leveraging diffeomorphic transformations through efficient forward and inverse mappings. The extracted localized features from the deformation field allow for downstream statistical learning that may facilitate medical diagnostics. On a lightweight and portable software platform, AutoCT provides a new toolkit for the CT imaging community to underpin the deployment of artificial intelligence-driven applications.

Estimates of the land area occupied by wind energy differ by orders of magnitude due to data scarcity and inconsistent methodology. We developed a method that combines machine learning-based imagery analysis and geographic information systems and examined the land area of 318 wind farms (15,871 turbines) in the U.S. portion of the Western Interconnection. We found that prior land use and human modification in the project area are critical for land-use efficiency and land transformation of wind projects. Projects developed in areas with little human modification have a land-use efficiency of 63.8 ± 8.9 W/m2 (mean ±95% confidence interval) and a land transformation of 0.24 ± 0.07 m2/MWh, while values for projects in areas with high human modification are 447 ± 49.4 W/m2 and 0.05 ± 0.01 m2/MWh, respectively. We show that land resources for wind can be quantified consistently with our replicable method, a method that obviates >99% of the workload using machine learning. To quantify the peripheral impact of a turbine, buffered geometry can be used as a proxy for measuring land resources and metrics when a large enough impact radius is assumed (e.g., >4 times the rotor diameter). Our analysis provides a necessary first step toward regionalized impact assessment and improved comparisons of energy alternatives.

The work presents a newly designed penalty function to be added with an existing Cross Entropy based fitness function [3] for optimal selection of multi-level thresholds for image segmentation. The extended fitness function so designed is tested here for Brain magnetic resonance (MR) image segmentation using nature-inspired meta-heuristics such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Whale Optimization Algorithm (WOA) and Moth-flame Optimization (MFO), and MFO is finally selected. The proposed method excels in Brain MR image segmentation when tested on WBA database and BrainWeb MR image database with other nature-inspired meta-heuritic based methods. It outperformed the other methods in terms of the three performance metrics Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Feature Similarity Index (FSIM). The best results shown by the proposed method on WBA database in terms of these metrics - PSNR, SSIM and FSIM - are 29.77, 0.927 and 0.899 respectively. On BrainWeb database, the proposed method yields 26.91, 0.864 and 0.892 for the three respective metrics. [ABSTRACT FROM AUTHOR]

In the present situation human health is a very important factor. With today's growing technology, health conditions are deteriorating day by day. Malnutrition, lifestyle play a very important role. These are leading to many diseases such as respiratory infections, heart disease, diabetes, Cancer, Alzheimer's, strokes and many more. Among these, few diseases are curable, due to the facilities available. Whereas few may be non-curable if care is not taken in the initial stages. Cancer poses the biggest threat among all life-threatening diseases. An efficient lesion segmentation method is introduced for the detection of Melanoma skin cancer disease at the preliminary stages using Adaptive Contour Model (ACM). High-quality segmentation is achieved based on contour features and sharp edge detection using ACM. The performance of the proposed Adaptive Contour Model (ACM) is tested upon PH2 and ISIC Challenge 2017 Dataset. The Performance matrices for the segmentation process are measured in terms of Jaccard index (JA), Dice coefficient (DI) and accuracy that are found to be 79.23, 87.26 and 94.63 considering ISIC dataset and 89.14, 93.98 and 96.95 which is quite high considering PH2 dataset. A method for detection of melanoma has been the critical need of the day. The proposed method shows that the performance of the Adaptive Contour Model (ACM) is more than 95%, which is better than other methods. [ABSTRACT FROM AUTHOR]

Image steganalysis is used to detect the presence of hidden data. Recent studies have shown that deep convolutional neural networks (CNNs) applied to steganalysis exhibit excellent performance. However, current network architectures have deepened layers to pursue an ultimate local receptive field, overlooking the boundary and overall information of the image. As a result, the network fails to effectively extract steganographic feature information. In this paper, we propose a method that effectively captures both boundary and global information. We process the images through segmentation and padding, followed by treatment with four symmetric sub-networks with shared parameters and structures to acquire more comprehensive steganographic features. By integrating two loss functions into the traditional cross-entropy loss, we can train a more compact feature space, thereby enhancing network performance. Experiments were conducted on the BOSSbase1.01 dataset, using two widely employed steganography methods, namely WOW (wavelet obtained weights) and SUNIWARD (spatial universal wavelet relative distortion), for comparison. Results show the proposed model demonstrates superior performance on various payloads. [ABSTRACT FROM AUTHOR]

The primary pathological feature of Membranous Nephropathy (MN) is the presence of tiny spike-like projections on the glomerular basement membrane. Early detection and efficient treatment of spike-like projections are essential in halting disease progression. Renal pathology biopsy stands as the gold standard for diagnosing MN, and the accurate identification of glomerular spike-like projections plays a vital role in aiding diagnosis. Nevertheless, the tiny spike-like projection lesions and constraints in data quantity pose considerable challenges for supervised learning-based glomerular classification and quantification. We develop a Multi-Scale Annotation-based Multiple Instance Learning (MSA-MIL) model to address the issues. MSA-MIL utilizes image labels and box-level labels to jointly enhance the classification performance of the MIL model. Specifically, we first employ U-Net for glomerular image edge segmentation and subsequently train the MIL model on the dataset with image-level labels. Then, to overcome the limitations arising from the scarcity of positive instances and the relatively small size of spike-like projection features, we manually augment the number of instances with spike-like projections via using box-level annotation to further enhance the MIL model's classification performance. The designed MSA-MIL model enables the classification, visualization, and quantitative analysis of glomeruli with spike-like projections in renal pathology images. We validated and evaluated the designed MSA-MIL model. The model performed exceptionally well, achieving a high accuracy of 0.9847 and demonstrating a high recall rate, effectively preventing misdiagnosis. Additionally, we utilized heatmaps to visualize the locations of spike-like projections within glomeruli, enhancing the model's interpretability. Furthermore, through an analysis of the correlation between the stages of membranous nephropathy and the proportion of spike-like projections, we observed that as the disease advances, the proportion of spike-like projections increases. This finding serves to further validate the results obtained by the model. The MSA-MIL model is the first one specifically designed for classifying glomerular spike-like projections. It not only enhances classification performance but also proves to be more suitable for categorizing minute lesions compared to conventional Convolutional Neural Network (CNN) models. The visualization of glomerular lesions and the proportion of spike-like projections provide doctors with insights into the model's inference process, offering intuitive assistance for accurate diagnoses. This model brings significant hope for advancing research and diagnosis in the field of kidney diseases. [ABSTRACT FROM AUTHOR]

In this study, we redefine FCM algorithm by integrating fuzzy set theory, fuzzy metrics, and Sugeno negation principles. This innovative approach overcomes the limitations inherent in conventional machine learning models, especially in situations characterized by uncertainty, noise, and ambiguity. Our model utilizes the membership degrees from fuzzy set theory, and transforms the concept of proximity defined by fuzzy metrics into a minimization problem. This transformation is achieved using a linguistic negation operator, which is crucial for optimizing FCM algorithm's objective function. A significant innovation in our research is the use of GA for optimizing parameters within the contexts of fuzzy metrics and Sugeno negation. The precise optimization capabilities of GA greatly enhance the sensitivity and adaptability of FCM algorithm, thereby improving overall performance. By leveraging the meticulous parameter adjustments provided by GA, our approach has shown superior results in practical applications, such as brain MRI image segmentation, surpassing traditional methods. Experimental results highlight the considerable enhancements our proposed FCM algorithms bring over existing methods across various performance metrics. In conclusion, this study makes a valuable addition to the field of fuzzy-based machine learning methodologies. It combines the optimization strength of GA with the flexible classification capabilities of fuzzy logic. The integration of Sugeno negation and fuzzy metrics not only improves the accuracy and precision of FCM algorithm but also provides significant benefits in handling complex and ambiguous datasets. This research signifies a major advance in machine learning and fuzzy logic, setting the stage for future applications and studies. [ABSTRACT FROM AUTHOR]

Recent years have witnessed notable advancements in medical image segmentation through deep convolutional neural networks. However, a notable limitation lies in the local operation of convolution, which hinders the ability to fully exploit global semantic information. To overcome the challenges prevalent in medical image segmentation, the feature ensemble network with multi‐scale atrous transformer is proposed. At the core of the approach lies the multi‐scale contextual integration module, which is based on the multi‐scale atrous transformer and facilitates contextual integration of multi‐level features. To extract discriminative fine‐grained features of the target region, a hybrid attention mechanism that synergistically combines spatial and channel attention, thereby sharpening the model's focus on crucial target information within high‐level features, is incorporated. Additionally, the channel‐aware feature reconstruction module is introduced as an innovative component engineered to tackle feature similarity issues across different categories. This module performs feature reconstruction based on channel perception, effectively widening the feature gap between categories and enhancing the segmentation capability. It is worth mentioning that our approach surpasses the state‐of‐the‐art method using three benchmark datasets in medical image segmentation. [ABSTRACT FROM AUTHOR]

Traditional video steganography methods primarily rely on modifying concealed spaces for embedding, thereby exhibiting a certain degree of security and embedding capacity. Nevertheless, these methods do not fully capitalize on the rich semantic information inherent in videos, limiting their overall effectiveness. In this paper, an AI‐generated video steganography scheme based on semantic segmentation is proposed. The mapping relationship between secret and semantic information is established by using a semantic segmentation model. The secret information can be converted into semantic labels by semantic histograms or pixels means, and semantic labels containing secret information are obtained and input into the video‐to‐video model to drive the generation of stego videos. After receiving the stego video, the receiver extracts the secret information using a pre‐defined specific embedding mode, including the methods of sub‐block partitioning and embedding capacity per frame. The experimental results show that the stego video has good visual quality, security, and robustness against various noise attacks. [ABSTRACT FROM AUTHOR]

Effective extraction of building from remote sensing images requires both global and local information. Despite convolutional neural networks (CNNs) excelling at capturing local details, their intrinsic focus on local operations poses challenge in effectively extracting global features, especially in the context of large‐scale buildings. In contrast, transformers excel at capturing global information, but compared to CNNs, they tend to overly rely on large‐scale datasets and pre‐trained parameters. To tackle the challenge, this paper presents the multi‐scale and dual‐branch feature fusion network (MDTrans). Specifically, the CNN and transformer branches are integrated in a dual‐branch parallel manner during both encoding and decoding stages, local information for small‐scale buildings is extracted by utilizing Dense Connection Blocks in the CNN branch, while crucial global information for large‐scale buildings is effectively captured through Swin Transformer Block in the transformer branch. Additionally, Dual Branch Information Fusion Block is designed to fuse local and global features from the two branches. Furthermore, Multi‐Convolutional Block is designed to further enhance the feature extraction capability of buildings with different sizes. Through extensive experiments on the WHU, Massachusetts, and Inria building datasets, MDTrans achieves intersection over union (IoU) scores of 91.36%, 64.69%, and 79.25%, respectively, outperforming other state‐of‐the‐art models. [ABSTRACT FROM AUTHOR]

Automatic segmentation of renal tumours during renal cell carcinoma surgery can help doctors accurately locate the tumour region, protect the tissues and organs around the kidneys, enhance surgical efficiency, and reduce the possibility of leakage and misdiagnosis. However, since general polyp endoscopic image segmentation models have many problems when facing the task of renal tumour segmentation, there needs to be more research on the segmentation of endoscopic images of renal tumours. This paper proposes a multi‐scale dynamic segmentation kernel network for endoscopic image segmentation of kidney tumours. First, a spatial receptive field module is proposed to augment the feature information and improve the performance of the whole network. Second, an enhanced cross‐attention module is offered to attenuate the effect of a high‐similarity segmentation background. Finally, a multi‐scale dynamic segmentation kernel module is introduced to gradually refine the segmentation results from small to large sizes to obtain more accurate tumour boundaries. Extensive experiments on the established kidney tumour endoscopic dataset and publicly available endoscopic datasets show that this method exhibits enhanced performance and generalization capabilities compared to existing techniques. On this renal tumour dataset, MDSK‐Net achieved excellent results of 94.1% and 90.1% on mDice and mIoU. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) applications are rapidly expanding across multiple domains and are not limited to prototyping purposes. However, achieving flawless parts in medical, aerospace, and automotive applications is critical for the widespread adoption of AM in these industries. Since AM is a complex process consisting of multiple interdependent factors, deep learning (DL) approaches are adopted widely to correlate the AM process physics to the part quality. Typically, in AM processes, computer vision-based DL is performed by extracting the machine's sensor data and layer-wise images through camera-based systems. This paper presents an overview of computer vision-assisted patch-wise defect localization and pixel-wise segmentation methods reported for AM processes to achieve error-free parts. In particular, these deep learning methods localize and segment defects in each layer, such as porosity, melt-pool regions, and spattering, during in situ processes. Further, knowledge of these defects can provide an in-depth understanding of fine-tuning optimal process parameters and part quality through real-time feedback. In addition to DL architectures to identify defects, we report on applications of DL extended to adjust the AM process variables in closed-loop feedback systems. Although several studies have investigated deploying closed-loop systems in AM for defect mitigation, specific challenges exist due to the relationship between inter-dependent process parameters and hardware constraints. We discuss potential opportunities to mitigate these challenges, including advanced segmentation algorithms, vision transformers, data diversity for improved performance, and predictive feedback approaches. [ABSTRACT FROM AUTHOR]

Cervical cancer is a prevalent disease affecting the cervix cells in women and is one of the leading causes of mortality for women globally. The Pap smear test determines the risk of cervical cancer by detecting abnormal cervix cells. Early detection and diagnosis of this cancer can effectively increase the patient's survival rate. The advent of artificial intelligence facilitates the development of automated computer-assisted cervical cancer diagnostic systems, which are widely used to enhance cancer screening. This study emphasizes the segmentation and classification of various cervical cancer cell types. An intuitive but effective segmentation technique is used to segment the nucleus and cytoplasm from histopathological cell images. Additionally, handcrafted features include different properties of the cells generated from the distinct cervical cytoplasm and nucleus area. Two feature rankings techniques are conducted to evaluate this study's significant feature set. Feature analysis identifies the critical pathological properties of cervical cells and then divides them into 30, 40, and 50 sets of diagnostic features. Furthermore, a graph dataset is constructed using the strongest correlated features, prioritizes the relationship between the features, and a robust graph convolution network (GCN) is introduced to efficiently predict the cervical cell types. The proposed model obtains a sublime accuracy of 99.11% for the 40-feature set of the SipakMed dataset. This study outperforms the existing study, performing both segmentation and classification simultaneously, conducting an in-depth feature analysis, attaining maximum accuracy efficiently, and ensuring the interpretability of the proposed model. To validate the model's outcome, we tested it on the Herlev dataset and highlighted its robustness by attaining an accuracy of 98.18%. The results of this proposed methodology demonstrate the dependability of this study effectively, detecting cervical cancer in its early stages and upholding the significance of the lives of women. [ABSTRACT FROM AUTHOR]

Skin cancer is a disorder that is becoming more prevalent around the world and is responsible for numerous mortality. Skin cancer starts in one organ and slowly moves to other parts of the body before killing the patient. Early detection of skin cancer is important for reducing the number of deaths all over the world. Because it takes time and money to manually diagnose skin cancer, it is critical to create automated diagnostic techniques to categorize skin lesions more accurately. Medical image enhancement and deep learning-based segmentation techniques are developed in this proposed work. Cancer affected and non-affected skin images are given as input for the proposed method. Data collection consists of raw data that cannot produce high accuracy. So, a certain pre-processing technique is used in the proposed method to achieve high accuracy. Dingo Optimized Texture based Histogram Equalization (DOTHE) strategy is utilized to improve the skin image. Then the pre-processed image is partitioned into different parts or regions according to the features and properties of the pixels in the image. U-Net network architecture is used in the proposed method to segment the enhanced image. The performances of the proposed model are analyzed using the Convolutional Neural Network (CNN) model. This proposed model is tested with several metrics which attain better performances like 97% accuracy, 96% sensitivity, 95% specificity, 94% precision, and 3% error. Thus the designed model enhances and segments the image effectively, and it is useful for effective skin cancer prediction. [ABSTRACT FROM AUTHOR]

Numerical simulation studies of water–rock interaction mechanisms and pore-scale multiphase flow properties often require high computational efficiency and realistic geometries to enable a fast and accurate description of hydrodynamic behavior. In this paper, we have chosen to use deep learning models to achieve these requirements, firstly by using encoder structures to refine the image segmentation of void-solid structures on complex geometries of scanning electron microscopy (SEM) images of porous media through few-shot learning (FSL), not only obtaining an accuracy of 0.97, but also reducing the amount of annotation work. We then focus on pore-scale three-dimensional (3D) structural reconstruction using the unpaired image-to-image translation method, optimizing the cycle-consistent adversarial network (cycle-GAN) model via sliced Wasserstein distance (SWD) to transfer marine sedimentary sandstone features to the initial image, and the geometric stochastic reconstruction problems are transformed into optimization problems. Subsequently, the computational efficiency was improved by a factor of 21 by implementing the lattice Boltzmann simulation method (LBM) accelerated by GPU through compute-unified device architecture (CUDA). The flow field distribution and absolute permeability of the extracted 2D samples and the reconstructed 3D porous media structure were simulated. The results showed that our method could rapidly and accurately reconstruct the 3D structures of a given feature, ensuring statistical equivalence between the 3D reconstructed structures and 2D samples. We solve the problem of extrapolation-based 3D reconstruction of porous media and significantly reduce the time spent on structure extraction and numerical calculations. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Objectives Methods Results Conclusion Artificial intelligence (AI)‐based tools have shown promise in histopathology image analysis in improving the accuracy of oral squamous cell carcinoma (OSCC) detection with intent to reduce human error.This systematic review and meta‐analysis evaluated deep learning (DL) models for OSCC detection on histopathology images by assessing common diagnostic performance evaluation metrics for AI‐based medical image analysis studies.Diagnostic accuracy studies that used DL models for the analysis of histopathological images of OSCC compared to the reference standard were analyzed. Six databases (PubMed, Google Scholar, Scopus, Embase, ArXiv, and IEEE) were screened for publications without any time limitation. The QUADAS‐2 tool was utilized to assess quality. The meta‐analyses included only studies that reported true positives (TP), true negatives (TN), false positives (FP), and false negatives (FN) in their test sets.Of 1267 screened studies, 17 studies met the final inclusion criteria. DL methods such as image classification (n = 11) and segmentation (n = 3) were used, and some studies used combined methods (n = 3). On QUADAS‐2 assessment, only three studies had a low risk of bias across all applicability domains. For segmentation studies, 0.97 was reported for accuracy, 0.97 for sensitivity, 0.98 for specificity, and 0.92 for Dice. For classification studies, accuracy was reported as 0.99, sensitivity 0.99, specificity 1.0, Dice 0.95, F1 score 0.98, and AUC 0.99. Meta‐analysis showed pooled estimates of 0.98 sensitivity and 0.93 specificity.Application of AI‐based classification and segmentation methods on image analysis represents a fundamental shift in digital pathology. DL approaches demonstrated significantly high accuracy for OSCC detection on histopathology, comparable to that of human experts in some studies. Although AI‐based models cannot replace a well‐trained pathologist, they can assist through improving the objectivity and repeatability of the diagnosis while reducing variability and human error as a consequence of pathologist burnout. [ABSTRACT FROM AUTHOR]

The CoAtNet deep neural model has been shown to achieve state-of-the-art performance by stacking convolutional and self-attention layers. In particular, the initial layers of CoAtNet apply efficient convolutions for extracting local features out of the input image and the initial fine-resolution feature maps. In turn, the final layers apply more cumbersome Transformers in order to extract global features from the coarse-resolution feature maps. The model's outcome directly depends on those final global features. This paper proposes an extension of the original CoAtNet model based on the introduction of a dual stream of convolution and self-attention blocks applied at the final layers of CoAtNet. In this way, those final layers automatically aggregate both local and global features extracted from the initial feature maps. Two dual-stream topologies have been proposed and evaluated. This Dual-Stream CoAtNet model exhibits a significant improvement on the segmentation accuracy of breast ultrasound images, thus contributing to the development of more robust tumor detection methods. [ABSTRACT FROM AUTHOR]

In order to better handle images with intensity inhomogeneity and noise, an adaptive multi-level set active contour model based on block search is proposed in this paper. This model first defines a multiple edge extension criterion for the input image to block the image and avoid the loss of image edge information; Then, the proposed adaptive block search method is used to find the level set that is considered redundant, and the remaining parts are fused to obtain a rough binary mask; Finally, the target is extracted by using the newly defined energy functional and the edge contours of the extracted binary mask. The experimental results show that the average jaccard similarity coefficients of the proposed model for segmenting images with intensity inhomogeneity and real images are 97.81% and 98.41%, respectively, and the accuracy of the segmentation results is higher than that of other models participating in the comparison. Similarly, the results of the ablation experiment once again validated the robustness of the proposed model. [ABSTRACT FROM AUTHOR]

Unsupervised domain adaptation (UDA) aims to solve the lack of annotation in a new dataset which has non-independent identity distribution compare with training data. It has the potential to help the annotation process in medical image segmentation. Existing self-training based UDA approaches utilize the pseudo labels as ground truth labels for domain adaptation, whereas the generated pseudo labels inevitably introducing the noise when training the model for the target domain, which make the training process unstable and the model is difficult to converge. In the meanwhile, most of the methods ignore the class imbalanced problem. To tackle the issue, we propose a class-aware multi-stage unsupervised domain adaptation framework for prostate zonal segmentation task. We devise a class-specific knowledge guidance strategy for training a better pseudo labels generation model. Extensive experimental results show the effectiveness of our approach against existing state-of-the-art approaches on the UDA problem of prostate zonal segmentation benchmark. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Ablation zone segmentation in contrast‐enhanced computed tomography (CECT) images enables the quantitative assessment of treatment success in the ablation of liver lesions. However, fully automatic liver ablation zone segmentation in CT images still remains challenging, such as low accuracy and time‐consuming manual refinement of the incorrect regions.Therefore, in this study, we developed a semi‐automatic technique to address the remaining drawbacks and improve the accuracy of the liver ablation zone segmentation in the CT images.Our approach uses a combination of a CNN‐based automatic segmentation method and an interactive CNN‐based segmentation method. First, automatic segmentation is applied for coarse ablation zone segmentation in the whole CT image. Human experts then visually validate the segmentation results. If there are errors in the coarse segmentation, local corrections can be performed on each slice via an interactive CNN‐based segmentation method. The models were trained and the proposed method was evaluated using two internal datasets of post‐interventional CECT images (n1$n_{1}$ = 22, n2$n_{2}$ = 145; 62 patients in total) and then further tested using an external benchmark dataset (n3$n_{3}$ = 12; 10 patients).To evaluate the accuracy of the proposed approach, we used Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Hausdorff distance (HD), and volume difference (VD). The quantitative evaluation results show that the proposed approach obtained mean DSC, ASSD, HD, and VD scores of 94.0%, 0.4 mm, 8.4 mm, 0.02, respectively, on the internal dataset, and 87.8%, 0.9 mm, 9.5 mm, and −0.03, respectively, on the benchmark dataset. We also compared the performance of the proposed approach to that of five well‐known segmentation methods; the proposed semi‐automatic method achieved state‐of‐the‐art performance on ablation segmentation accuracy, and on average, 2 min are required to correct the segmentation. Furthermore, we found that the accuracy of the proposed method on the benchmark dataset is comparable to that of manual segmentation by human experts (p$p$ = 0.55, t$t$‐test).The proposed semi‐automatic CNN‐based segmentation method can be used to effectively segment the ablation zones, increasing the value of CECT for an assessment of treatment success. For reproducibility, the trained models, source code, and demonstration tool are publicly available at https://github.com/lqanh11/Interactive_AblationZone_Segmentation. [ABSTRACT FROM AUTHOR]

Objectives: This article focuses on the detection of cells in low-contrast brightfield microscopy images; in our case, it is chronic lymphocytic leukaemia cells. The automatic detection of cells from brightfield time-lapse microscopic images brings new opportunities in cell morphology and migration studies; to achieve the desired results, it is advisable to use state-of-the-art image segmentation methods that not only detect the cell but also detect its boundaries with the highest possible accuracy, thus defining its shape and dimensions. Methods: We compared eight state-of-the-art neural network architectures with different backbone encoders for image data segmentation, namely U-net, U-net++, the Pyramid Attention Network, the Multi-Attention Network, LinkNet, the Feature Pyramid Network, DeepLabV3, and DeepLabV3+. The training process involved training each of these networks for 1000 epochs using the PyTorch and PyTorch Lightning libraries. For instance segmentation, the watershed algorithm and three-class image semantic segmentation were used. We also used StarDist, a deep learning-based tool for object detection with star-convex shapes. Results: The optimal combination for semantic segmentation was the U-net++ architecture with a ResNeSt-269 background with a data set intersection over a union score of 0.8902. For the cell characteristics examined (area, circularity, solidity, perimeter, radius, and shape index), the difference in mean value using different chronic lymphocytic leukaemia cell segmentation approaches appeared to be statistically significant (Mann–Whitney U test, P <.0001). Conclusion: We found that overall, the algorithms demonstrate equal agreement with ground truth, but with the comparison, it can be seen that the different approaches prefer different morphological features of the cells. Consequently, choosing the most suitable method for instance-based cell segmentation depends on the particular application, namely, the specific cellular traits being investigated. [ABSTRACT FROM AUTHOR]

Identifying pigments from colored relics is essential for their color restoration and for facsimile creation. A workflow for identifying pigment information is constructed based on visible spectral imaging technology, aligned with the drawing process of colored relics. This workflow includes three steps: boundary extraction, material identification and prediction of mixture proportions. The methods for segmenting visible spectral images, identifying chemical compositions, and predicting mixture proportions of pigments are extensively reviewed. Future research trends of these methods are also analyzed. The influence of the pigment particle size is currently underexplored but can be accomplished by multidisciplinary research. [ABSTRACT FROM AUTHOR]

Problem: Leishmaniasis is a disease caused by protozoan parasites of the genus Leishmania and has a high prevalence and impact on global health. Currently, the available drugs for its treatment have drawbacks, such as high toxicity, resistance of the parasite, and high cost. Therefore, the search for new, more effective, and safe drugs is a priority. The effectiveness of an anti-leishmanial drug is analyzed through in vitro studies in which a technician manually counts the intracellular form of the parasite (amastigote) within macrophages, which is slow, laborious, and prone to errors. Objective(s): To develop a computational system that facilitates the detection and counting of amastigotes in microscopy images obtained from in vitro studies using image processing techniques. Methodology: Segmentation of objects in the microscope image that might be Leishmania amastigotes was performed using the multilevel Otsu method on the saturation component of the hue, saturation, and intensity color model. In addition, morphological operations and the watershed transform combined with the weighted external distance transform were used to separate clustered objects. Then positive (amastigote) objects were detected (and consequently counted) using a classifier algorithm, the selection of which as well as the definition of the features to be used were also part of this research. MATLAB was used for the development of the system. Results and discussion: The results were evaluated in terms of sensitivity, precision, and the F-measure and suggested a favorable effectiveness of the proposed method. Conclusions: This system can help researchers by allowing large volumes of images of amastigotes to be counted using an automatic image analysis technique. [ABSTRACT FROM AUTHOR]

Deformable image registration (DIR) plays an important part in many clinical tasks, and deep learning has made significant progress in DIR over the past few years.To propose a fast multiscale unsupervised deformable image registration (referred to as FMIRNet) method for monomodal image registration.We designed a multiscale fusion module to estimate the large displacement field by combining and refining the deformation fields of three scales. The spatial attention mechanism was employed in our fusion module to weight the displacement field pixel by pixel. Except mean square error (MSE), we additionally added structural similarity (ssim) measure during the training phase to enhance the structural consistency between the deformed images and the fixed images.Our registration method was evaluated on EchoNet, CHAOS and SLIVER, and had indeed performance improvement in terms of SSIM, NCC and NMI scores. Furthermore, we integrated the FMIRNet into the segmentation network (FCN, UNet) to boost the segmentation task on a dataset with few manual annotations in our joint leaning frameworks. The experimental results indicated that the joint segmentation methods had performance improvement in terms of Dice, HD and ASSD scores.Our proposed FMIRNet is effective for large deformation estimation, and its registration capability is generalizable and robust in joint registration and segmentation frameworks to generate reliable labels for training segmentation tasks. [ABSTRACT FROM AUTHOR]

Stroke, the second leading cause of mortality globally, predominantly results from ischemic conditions. Immediate attention and diagnosis, related to the characterization of brain lesions, play a crucial role in patient prognosis. Standard stroke protocols include an initial evaluation from a non-contrast CT to discriminate between hemorrhage and ischemia. However, non-contrast CTs lack sensitivity in detecting subtle ischemic changes in this phase. Alternatively, diffusion-weighted MRI studies provide enhanced capabilities, yet are constrained by limited availability and higher costs. Hence, we idealize new approaches that integrate ADC stroke lesion findings into CT, to enhance the analysis and accelerate stroke patient management. This study details a public challenge where scientists applied top computational strategies to delineate stroke lesions on CT scans, utilizing paired ADC information. Also, it constitutes the first effort to build a paired dataset with NCCT and ADC studies of acute ischemic stroke patients. Submitted algorithms were validated with respect to the references of two expert radiologists. The best achieved Dice score was 0.2 over a test study with 36 patient studies. Despite all the teams employing specialized deep learning tools, results reveal limitations of computational approaches to support the segmentation of small lesions with heterogeneous density. [ABSTRACT FROM AUTHOR]

UNet architecture has achieved great success in medical image segmentation applications. However, these models still encounter several challenges. One is the loss of pixel-level information caused by multiple down-sampling steps. Additionally, the addition or concatenation method used in the decoder can generate redundant information. These limitations affect the localization ability, weaken the complementarity of features at different levels and can lead to blurred boundaries. However, differential features can effectively compensate for these shortcomings and significantly enhance the performance of image segmentation. Therefore, we propose MGRAD-UNet (multi-gated reverse attention multi-scale differential UNet) based on UNet. We utilize the multi-scale differential decoder to generate abundant differential features at both the pixel level and structure level. These features which serve as gate signals, are transmitted to the gate controller and forwarded to the other differential decoder. In order to enhance the focus on important regions, another differential decoder is equipped with reverse attention. The features obtained by two differential decoders are differentiated for the second time. The resulting differential feature obtained is sent back to the controller as a control signal, then transmitted to the encoder for learning the differential feature by two differential decoders. The core design of MGRAD-UNet lies in extracting comprehensive and accurate features through caching overall differential features and multi-scale differential processing, enabling iterative learning from diverse information. We evaluate MGRAD-UNet against state-of-theart (SOTA) methods on two public datasets. Our method surpasses competitors and provides a new approach for the design of UNet. [ABSTRACT FROM AUTHOR]

Scanning Electron Microscope (SEM) is a crucial tool for studying microstructures of ceramic materials. However, the current practice heavily relies on manual efforts to extract porosity from SEM images. To address this issue, we propose PSTNet (Pyramid Segmentation Transformer Net) for grain and pore segmentation in SEM images, which merges multi-scale feature maps through operations like recombination and upsampling to predict and generate segmentation maps. These maps are used to predict the corresponding porosity at ceramic grain boundaries. To increase segmentation accuracy and minimize loss, we employ several strategies. (1) We train the micro-pore detection and segmentation model using publicly available Al2O3 and custom Y2O3 ceramic SEM images. We calculate the pixel percentage of segmented pores in SEM images to determine the surface porosity at the corresponding locations. (2) Utilizing high-temperature hot pressing sintering, we prepared and captured scanning electron microscope images of Y2O3 ceramics, with which a Y2O3 ceramic dataset was constructed through preprocessing and annotation. (3) We employed segmentation penalty cross-entropy loss, smooth L1 loss, and structural similarity (SSIM) loss as the constituent terms of a joint loss function. The segmentation penalty cross-entropy loss helps suppress segmentation loss bias, smooth L1 loss is utilized to reduce noise in images, and incorporating structural similarity into the loss function computation guides the model to better learn structural features of images, significantly improving the accuracy and robustness of semantic segmentation. (4) In the decoder stage, we utilized an improved version of the multi-head attention mechanism (MHA) for feature fusion, leading to a significant enhancement in model performance. Our model training is based on publicly available laser-sintered Al2O3 ceramic datasets and self-made high-temperature hot-pressed sintered Y2O3 ceramic datasets, and validation has been completed. Our Pix Acc score improves over the baseline by 12.2%, 86.52 vs. 76.01, and the mIoU score improves from by 25.5%, 69.10 vs. 51.49. The average relative errors on datasets Y2O3 and Al2O3 were 6.9% and 6.36%, respectively. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions Cone beam computed tomography (CBCT) image segmentation is crucial in prostate cancer radiotherapy, enabling precise delineation of the prostate gland for accurate treatment planning and delivery. However, the poor quality of CBCT images poses challenges in clinical practice, making annotation difficult due to factors such as image noise, low contrast, and organ deformation.The objective of this study is to create a segmentation model for the label‐free target domain (CBCT), leveraging valuable insights derived from the label‐rich source domain (CT). This goal is achieved by addressing the domain gap across diverse domains through the implementation of a cross‐modality medical image segmentation framework.Our approach introduces a multi‐scale domain adaptive segmentation method, performing domain adaptation simultaneously at both the image and feature levels. The primary innovation lies in a novel multi‐scale anatomical regularization approach, which (i) aligns the target domain feature space with the source domain feature space at multiple spatial scales simultaneously, and (ii) exchanges information across different scales to fuse knowledge from multi‐scale perspectives.Quantitative and qualitative experiments were conducted on pelvic CBCT segmentation tasks. The training dataset comprises 40 unpaired CBCT‐CT images with only CT images annotated. The validation and testing datasets consist of 5 and 10 CT images, respectively, all with annotations. The experimental results demonstrate the superior performance of our method compared to other state‐of‐the‐art cross‐modality medical image segmentation methods. The Dice similarity coefficients (DSC) for CBCT image segmentation results is 74.6±9.3$74.6 \pm 9.3$ %, and the average symmetric surface distance (ASSD) is 3.9±1.8mm$3.9\pm 1.8\;\mathrm{mm}$. Statistical analysis confirms the statistical significance of the improvements achieved by our method.Our method exhibits superiority in pelvic CBCT image segmentation compared to its counterparts. [ABSTRACT FROM AUTHOR]

Background: To investigate the correlation between retinal vascular changes and ICA stenosis by measuring retinal vessels using full-width-at-half-maximum (FWHM) and intelligent image recognition. Methods: This research selected patients who were admitted to the Vascular Surgery Department of Quzhou People's Hospital from January 2018 to December 2020 and were preparing for Carotid Artery Stenting (CAS). Participants were divided into two groups: without ICA stenosis (Group 0) and with ICA stenosis (Group 1). A total of 109 cases were included in the study, with 50 cases in Group 1 and 59 cases in Group 0. Vascular images of superior temporal zone B of the retina were obtained by spectral domain optical coherence tomography (SD-OCT). The edges of retinal vessels were identified by FWHM. Each vessel of all subjects was measured three times with the FWHM, and the average value was taken to obtain the retinal arteriolar lumen diameter (RALD), retinal arteriolar outer diameter (RAOD), retinal venular lumen diameter (RVLD), and retinal venular outer diameter (RVOD),Arterial Wall Thickness (AWT), Venular Wall Thickness (VWT)=(RVOD-RVLD)/2, Arteriovenous Ratio (AVR) = RAOD/RVOD. Results: We found that compared to Group 0, Group 1 had smaller RALD (P < 0.001) and RAOD (P < 0.001), and wider RVOD (P < 0.001), with thicker VWT (P < 0.001). When compared with the contralateral eye in Group 1, the ipsilateral eye exhibited even smaller RALD, RAOD and AVR (P < 0.001, P < 0.001, P < 0.001). After CAS, the RALD, RAOD and AVR in Group 1 increased (P < 0.001, P < 0.001, P < 0.001),while the RVLD and RVOD decreased (P < 0.05, P < 0.001). Our research reveals a significant correlation between retinal vascular changes and internal ICA stenosis. Conclusion: Utilizing SD-OCT in conjunction with the FWHM, we achieved a non-invasive, intelligent, stable, and precise acquisition of data pertaining to retinal vessels. These findings underscore a significant correlation between alterations in retinal vascular structure and the presence of ICA stenosis, as demonstrated by our research. [ABSTRACT FROM AUTHOR]

At present, the cleaning of water plants in crab pond is primarily relied on manual operations, which is characterized by high labor intensity and low efficiency. The automatic water plant cleaning boat can greatly reduce the labor intensity and improve the operation efficiency, and determining the water plant distribution in crab pond is the key basis for planning the efficient operation path of the cleaning boat. Addressing the challenges posed by the high similarity between water plants and shoreline vegetation, as observed in drone-captured images of the crab pond, which complicates the accurate distinction between them, a region segmentation and localization method for water plants in crab pond based on improved YOLOv8n-seg was proposed in this paper. The focus of the improved model lies in the reduction of the model size and the enhancement of the recall rate. Firstly, in order to diminish the model size, the lightweight HGNetv2 (hierarchical graph network) based on RTDETR (real-time detection transformer) was used as the backbone feature extraction network. Secondly, the neck network was reconstructed based on the lightweight structure of Efficient Rep to reduce the number of parameters and enhance the multiscale feature fusion ability of the mode. Finally, the SegNext attention mechanism was introduced in the feature extraction layer to enhance the sensitivity of the model to the water plant area. In order to eliminate the redundant regions generated by the model in the recognition process and further improve the segmentation accuracy, the binarization processing was used to optimize the segmentation results, and the image processing algorithm was combined to screen the area of the water plant area. After coordinate conversion, the precise longitude and latitude coordinates of water plant contour were obtained. The experimental results showed that the improved model exhibited a strong discriminatory and segmentation effect on water plants in crab ponds. The parameter number, calculation quantity and model size of the improved model were only 1.49 M, 8.4 GFLOPs, and 3.27 MB, respectively. Compared with the original YOLOv8n-seg model, the parameter number, calculation quantity and model size of the improved model were reduced by 54.3%, 30.6% and 49.7%, respectively. The model before and after improvement was deployed to the Jetson Orin Nano embedded AI computer for testing. It was found that the preprocessing speed of the improved model was almost the same as that of the YOLOv8n-seg model. The inference speed, post processing speed and segment speed were increased by 30.6%, 46.4% and 32.3%, respectively. In addition, the recall, precision, and mean average precision of the improved model achieved values of 91.5%, 89.3% and 95.6%, respectively. The improved model achieved the best balance of calculation, parameters, recall and detection accuracy comparing with YOLOv5sseg, YOLOv8s-SwinTransformer, YOLOv8s-seg and YOLOv8n-GoldYOLO models. The coordinate conversion test showed that the minimum distance error of water plant location accuracy was 0.13m, the maximum distance error was 0.33 m, and the average distance error was 0.22 m, which verified that the improved model can meet the requirements of water plant region segmentation and localization in crab pond. The finding can provide an important reference for the automatic operation path planning of water plant cleaning boat. [ABSTRACT FROM AUTHOR]

To evaluate the feasibility of a novel method for occipitocervical fixation (OCF) through the endonasal corridor. Thin-cut computed tomography scans were obtained for 5 cadaveric specimens. Image segmentation was used to reconstruct 3D models of each O-C1 joint complex. Using computer-aided design software, plates were custom-designed to span each O-C1 joint, sit flush onto the bony surface, and accommodate screws. The final models were 3D-printed in titanium. For implantation, specimens were held in pin-fixation and registered to neuronavigation. A rigid 0º endoscope was used for endonasal visualization. An inverted U-shaped nasopharyngeal flap was raised to expose the occipital condyles and C1. The plates were introduced and fixed with bone screws. Computed tomography scans were obtained to assess screw accuracy and proximity to critical neurovascular structures. Screw entry points and trajectories were recorded. Endonasal OCF was performed on 5 cadaveric specimens. The mean starting point for occipital condyle screws was 6.17 mm lateral and 5.38 mm rostral to the medial O-C1 joint. Mean axial and sagittal trajectories were 7.98° and 6.71°, respectively. The mean starting point for C1 screws was 16.11 mm lateral to the C1 anterior tubercle and 6.39 mm caudal to the medial O-C1 joint. Mean axial and sagittal trajectories were 10.97° and −9.91°, respectively. Endonasal OCF is technically and anatomically feasible. The application of this technique may allow for same-stage endonasal decompression and fixation, offering a minimally invasive alternative to current methods of fixation and advancing surgeons' ability to treat pathology of the craniovertebral junction. Next steps will focus on biomechanical testing. [ABSTRACT FROM AUTHOR]

Thanks to the proliferation of open-source tools, we are seeing an exponential growth of machine-learning applications, and its integration has become more accessible, particularly for segmentation tools in neuroimaging. This article explores a generalized methodology that harnesses these tools and aims/enables to expedite and enhance the reproducibility of clinical research. Herein, critical considerations include hardware, software, neural network training strategies, and data labeling guidelines. More specifically, we advocate an iterative approach to model training and transfer learning, focusing on internal validation and outlier handling early in the labeling process and fine-tuning later. The iterative refinement process allows experts to intervene and improve model reliability while cutting down on their time spent in manual work. A seamless integration of the final model's predictions into clinical research is proposed to ensure standardized and reproducible results. In short, this article provides a comprehensive framework for accelerating research using machine-learning techniques for image segmentation. [ABSTRACT FROM AUTHOR]

Fingerprint recognition systems hold a pivotal role across various modern applications, such as criminal investigation, civil identification, and access control. Fingerprint segmentation is commonly the first stage of fingerprint recognition systems, focusing on extracting the foreground from captured fingerprint images. This helps to reduce extracting false minutiae, speed up the extraction process, and hence, improve the overall system performance. In this paper, we introduce a novel fingerprint segmentation method by first proposing new frequency and intensity features with employing fuzzy-c-means and genetic algorithm, accompanied by proposing a new cluster assignment strategy that involves features weighting and cluster assignment probabilities thresholding which helps to improve the accuracy compared to the standard cluster assignment method. Finally, we introduce a new morphological post-processing technique in order to minimize the misclassified pixels and obtain the final mask. Furthermore, we carry out a comprehensive performance evaluation against leading fingerprint segmentation methods on various known databases. As a result, the proposed method achieves an average error rate of 2.52%, which is much lower than the corresponding errors obtained in the literature. By these experimental results, we show that the proposed method outperforms the best existing methods. [ABSTRACT FROM AUTHOR]

Skin disease image segmentation faces two major challenges: the complex and varied lesion morphology and the presence of interfering image backgrounds. To address these difficulties in skin disease image segmentation, we propose a Residual U-Net architecture with Channel-Space Separate Attention based on depthwise separable convolutions. The multi-scale residual U-Net modules in the encoder efficiently capture multi-scale texture information in lesions and backgrounds within a single stage, overcoming the limitations of U-Net in extracting just local features. The introduction of ConvMixer Block for global contextual modeling contributes to suppress complex background interference and enhances the overall understanding of lesion morphology. Additionally, we employ a Channel-Space Separate Attention mechanism with depthwise separable convolutions(CSSA-DSC) for feature fusion, effectively addressing the limited expressiveness issue associated with U-Net’s direct skip-connection concatenation. Experimental results on the PH2, ISIC 2017, and ISIC 2018 datasets demonstrate our method’s strong multi-scale modeling and feature expression capabilities. [ABSTRACT FROM AUTHOR]

Deep learning techniques have attained substantial progress in various face‐related tasks, such as face recognition, face inpainting, and facial expression recognition. To prevent infection or the spread of the virus, wearing of masks in public places has been mandated following the COVID‐19 epidemic, which has led to face occlusion and posed significant challenges for face recognition systems. Most prominent masked face recognition solutions rely on mask segmentation tasks. Therefore, segmentation can be used to mitigate the negative impacts of wearing a mask and improve recognition accuracy. Mask region segmentation suffers from two main problems: there is no standard type of masks that people wear, they come in different colours and designs, and there is no publicly available masked face dataset with appropriate ground truth for the mask region. In order to address these issues, we propose an encoder–decoder framework that utilizes a boundary‐aware attention network combined with a new hybrid loss to provide a map, patch, and pixel‐level supervision. We also introduce a dataset called MFSD, with 11,601 images and 12,758 masked faces for masked face segmentation. Furthermore, we compare the performance of different cutting‐edge deep learning semantic segmentation models on the presented dataset. Experimental results on the MSFD dataset reveal that the suggested approach outperforms state‐of‐the‐art, algorithms with 97.623% accuracy, 93.814% IoU, and 96.817% F1‐score rate. Our dataset of masked faces with mask region labels and source code will be available online. [ABSTRACT FROM AUTHOR]

Medical image segmentation through the use of deep learning is becoming a trend targeting to automate disease detection and provide effective treatment. Traditionally huge manual efforts are associated with medical image processing by medical staff. The wide use of neural networks driven by the progressive advancement in computing power and the availability of training data provides instrumental means to automate medical image processing. A polyp refers to an abnormal growth that can occur in various parts of the body. While the majority of polyps are noncancerous or benign, there are instances where certain types can develop into cancer. Detecting and segmenting polyps is highly valuable for identifying early signs and potentially enabling more effective treatment of colon cancer. In this paper, we introduce a novel method for segmenting polyp images. The proposed method utilizes convolutional neural networks (CNNs) and employs an enhanced attention mechanism. To cater to a more granular attention view, this paper enhances the Convolutional Block Attention Module by introducing the multi‐focal channel attention (MFCA) concept, which we call MFCA. In the proposed MFCA channel attention, focal attention spots allow us to consider scattered areas of interest more effectively. To evaluate the effectiveness of the proposed method, multiple experiments are conducted on five well‐known benchmark datasets for polyp image segmentation: Kvasir, CVC‐Clinic DB, CVC‐Colon DB, CVC‐T, and ETIS‐Larib. Various testing scenarios are examined, including the impact of two focal attention spots and four focal attention spots. The experimental results demonstrated that the proposed method achieved superior performance compared to previous state‐of‐the‐art techniques on two of the benchmark datasets, and ranked second on a third dataset. [ABSTRACT FROM AUTHOR]

Accurate landslide segmentation from remote sensing data is pivotal for efficient emergency response and risk management. In recent years, data-driven deep learning approaches have emerged as a significant area of focus in this domain. However, the limited availability of landslide data often restricts the effectiveness of these approaches. This study introduces the StyleGAN2-transformer framework for landslide segmentation, utilizing generative adversarial networks (GANs) for the first time to create synthetic, high-quality landslide images to address the data scarcity issue that undermines landslide segmentation model performance. Two datasets were developed: one containing a limited set of real landslide images and the other supplemented with synthetic landslide images generated by StyleGAN2. These datasets facilitated comparative experiments to quantitatively assess the impact of synthetic data on the performance of both convolutional neural network (CNN) and transformer series models, employing a suite of metrics for thorough evaluation. The findings indicate that adding synthetic landslide images from StyleGAN2 improves the overall accuracy of most landslide segmentation models significantly, achieving more than a 10% increase. Moreover, integrating StyleGAN2 with transformer models presents an optimized approach, as transformer models surpass CNN models in accuracy when adequate training data are available. Finally, the results also confirm that the StyleGAN2-transformer framework exhibits strong generalizability in a variety of scenarios. [ABSTRACT FROM AUTHOR]

The Bayesian optimization algorithm uses Bayesian networks as the probability model of its solution space. Although the research on this algorithm has steadily developed, there are still some problems in its application process, such as excessive computational complexity. To solve various problems in Bayesian algorithm, reduce its computational complexity, and enable it to better achieve image segmentation. The study chooses to improve the Bayesian algorithm on the basis of immune algorithm, and solves the problem of computational complexity by reducing the number of Bayesian network construction times, thereby improving the individual fitness of the population. Through simulation experiments, it has been shown that the average number of times the improved Bayesian algorithm reaches the optimal value is 30, which is higher than the traditional algorithm's 20 times. Its excellent optimization ability searches for the optimal threshold to complete image segmentation. The improved Bayesian optimization algorithm based on immune algorithm can effectively reduce computational complexity, shorten computational time, and improve convergence. And applying Bayesian algorithm to image segmentation has broadened the application field of the algorithm and found new exploration directions for image segmentation. [ABSTRACT FROM AUTHOR]

Deep learning has been widely applied in the fields of image classification and segmentation, while adversarial attacks can impact the model's results in image segmentation and classification. Especially in medical images, due to constraints from factors like shooting angles, environmental lighting, and diverse photography devices, medical images typically contain various forms of noise. In order to address the impact of these physically meaningful disturbances on existing deep learning models in the application of burn image segmentation, we simulate attack methods inspired by natural phenomena and propose an adversarial training approach specifically designed for burn image segmentation. The method is tested on our burn dataset. Through the defensive training using our approach, the segmentation accuracy of adversarial samples, initially at 54%, is elevated to 82.19%, exhibiting a 1.97% improvement compared to conventional adversarial training methods, while substantially reducing the training time. Ablation experiments validate the effectiveness of individual losses, and we assess and compare training results with different adversarial samples using various metrics. [ABSTRACT FROM AUTHOR]

Traditional thresholding methods are widely used to extract objects of interest from image backgrounds in various practical applications. However, these methods often face challenges in complex scenes due to poor uniformity, noise, and low contrast. To overcome these limitations, this paper proposes a peak-weaken Otsu method (PWOTSU) that improves the segmentation performance of the Otsu method for automatically extracting objects in complex scenes. The proposed approach uses a set of cross parameters as weights for the Otsu criterion function to adaptively weaken the between-class variance at the peak of the histogram. This ensures that an appropriate threshold value is always obtained for images with different types of histogram distribution. The improved criterion function has the advantage of obtaining a more accurate threshold value without the need for additional parameters, making it easily applicable to various practical applications. Experimental results demonstrate that the proposed method effectively improves the segmentation accuracy and robustness compared to the standard Otsu method and its modifications, as evidenced by qualitative and quantitative evaluations. [ABSTRACT FROM AUTHOR]