Low-contrast Images Research Articles

Breast Lesion Detection for Ultrasound Images Using MaskFormer

This study evaluates the performance of the MaskFormer model for segmenting and classifying breast lesions using ultrasound images, addressing ultrasound’s limitations. Ultrasound used for breast cancer detection faces challenges like low image contrast and difficulty in the detection of small or multiple lesions, further complicated by variability based on operator skill. Initial experiments with U-Net and other CNN-based models revealed constraints, such as early plateauing in model loss, which indicated suboptimal learning and performance. In contrast, MaskFormer demonstrated continuous improvement, achieving higher precision in breast lesion segmentation and significantly reducing both false positives and false negatives. Comparative analysis showed MaskFormer’s superior performance, with the highest precision and recall rates for malignant lesions and an overall mean average precision (mAP) of 0.943. The model’s ability to detect a diverse range of breast lesions, including those potentially missed by the human eye, especially by less experienced practitioners, underscores its potential. These findings suggest that integrating AI models like MaskFormer could greatly enhance ultrasound performance for breast cancer detection, providing reliable, operator-independent image analysis and potentially improving patient outcomes on a global scale.

X-ray coronary angiography background subtraction by adaptive weighted total variation regularized online RPCA

Objective.X-ray coronary angiograms (XCA) are widely used in diagnosing and treating cardiovascular diseases. Various structures with independent motion patterns in the background of XCA images and limitations in the dose of injected contrast agent have resulted in low-contrast XCA images. Background subtraction methods have been developed to enhance the visibility and contrast of coronary vessels in XCA sequences, consequently reducing the requirement for excessive contrast agent injections.Approach.The current study proposes an adaptive weighted total variation regularized online RPCA (WTV-ORPCA) method, which is a low-rank and sparse subspaces decomposition approach to subtract the background of XCA sequences. In the proposed method, the images undergo initial preprocessing using morphological operators to eliminate large-scale background structures and achieve image homogenization. Subsequently, the decomposition algorithm decomposes the preprocessed images into background and foreground subspaces. This step applies an adaptive weighted TV constraint to the foreground subspace to ensure the spatial coherency of the finally extracted coronary vessel images.Main results.To evaluate the effectiveness of the proposed background subtraction method, some qualitative and quantitative experiments are conducted on two clinical and synthetic low-contrast XCA datasets containing videos from 21 patients. The obtained results are compared with six state-of-the-art methods employing three different assessment criteria. By applying the proposed method to the clinical dataset, the mean values of the global contrast-to-noise ratio, local contrast-to-noise ratio, structural similarity index, and reconstruction error (RE) are obtained as5.976,3.173,0.987, and0.026, respectively. These criteria over the low-contrast synthetic dataset were4.851,2.942,0.958, and0.034, respectively.Significance.The findings demonstrate the superiority of the proposed method in improving the contrast and visibility of coronary vessels, preserving the integrity of the vessel structure, and minimizing REs without imposing excessive computational complexity.

Effective low-exposure color image enhancement based on histogram equalization with spatial contextual information

Abstract Color image enhancement refers to improving the visual quality of color images. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis , and remote sensing . In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram, the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasets with low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameters like AMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images using the aforementioned techniques were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

Illumination Correction and Enhancement Algorithm for Defective Powder Spreading Images in Selective Laser Sintering

Abstract Based on the uneven illumination distribution and low defect contrast in the powder spreading images acquired during the selective laser sintering manufacturing process, which affects subsequent defect detection, an adaptive nonlinear illumination correction and enhancement algorithm is proposed. Local illumination correction is performed using gray valueitting curves and is combined with global gamma correction to improve correction capability in overly dark areas. A nonlinear enhancement function is constructed to boost the contrast of the image’s illumination component. Finally, a gradient-based L0 norm minimization algorithm is used to smooth non-defect areas. Experimental results show that the proposed algorithm signi icantly improves correction and enhancement, outperforming comparison algorithms overall. Compared to the original images, the coef icient of variation decreases by an average of 0.179, and the standard deviation increases by an average of 26.7%, demonstrating the effectiveness of the proposed algorithm.

DAAM-Net: A dual-encoder U-Net network with adjacent auxiliary module for pituitary tumor and jaw cyst segmentation

In the diagnosis of various diseases, accurate segmentation of lesions in medical images is crucial. However, when faced with the challenges of blurred edges, noise, and low contrast in images of pituitary tumors and jaw cysts, existing methods do not perform well in solving these problems. To overcome these problems, we present a new approach based on U-Net architecture. Firstly, a more comprehensive semantic information capture is realized by superimposing two U-Net network structures. Following that, the method incorporates atrous spatial pyramid pooling (ASSP) technology to enable adjustable multi-scale feature extraction. Thirdly, an adjacent auxiliary module (AAM) is designed to smoothly fuse features from different levels, effectively suppressing noise interference and reducing feature ambiguity. By testing on the dataset provided by Quzhou People’s Hospital, the IoU, Dice, Specificity, and Mcc of the method were 77.46%, 87.18%, 99.64% and 86.97% on the pituitary tumor dataset, and 81.32%, 89.53%, 99.91% and 89.55% on the jaw cyst dataset, respectively. Compared with existing algorithms, our method exhibits superior segmentation performance and is expected to become a more reliable auxiliary tool in the diagnosis of medical diseases.

CRML-Net: Cross-Modal Reasoning and Multi-Task Learning Network for tooth image segmentation

Data from a single modality may suffer from noise, low contrast, or other imaging limitations that affect the model’s accuracy. Furthermore, due to the limited amount of data, most models trained on single-modality data tend to overfit the training set and perform poorly on out-of-domain data. Therefore, in this paper, we propose a network named Cross-Modal Reasoning and Multi-Task Learning Network (CRML-Net), which combines cross-modal reasoning and multi-task learning, aiming to leverage the complementary information between different modalities and tasks to enhance the model’s generalization ability and accuracy. Specifically, CRML-Net consists of two stages. In the first stage, our network extracts a new morphological information modality from the original image and then performs cross-modal fusion with the original modality image, aiming to leverage the morphological information to enhance the model’s robustness to out-of-domain datasets. In the second stage, based on the output of the previous stage, we introduce a multi-task learning mechanism, aiming to improve the model’s performance on unseen data by sharing surface detail information from auxiliary tasks. We validated our method on a publicly available tooth cone beam computed tomography dataset. Our evaluation demonstrates that our method outperforms state-of-the-art approaches.

Thermal Battery Multi-Defects Detection and Discharge Performance Analysis Based on Computed Tomography Imaging

To address the typical structural defects that are prone to occur during the preparation and storage processes of thermal battery, experiments of battery image acquisition were designed based on X-ray computed tomography system. An improved Yolov5s network was employed to achieve high-precision automatic detection of typical defects. Through the discharge experiment of thermal battery, discharge performance curves of normal batteries and three defective batteries were constructed. The impact and mechanisms of different defects on the discharge performance were analyzed based on the voltage curve. By designing an automatic stitching scheme, the phenomenon of interlayer information overlap caused by the increase of cone angle in digital radiography images was suppressed. To address the issues of low image contrast and limited defect data in thermal battery imaging, the defect dataset was expanded using the designed image preprocessing steps and improving the contrast of the images. For subtle defects that are difficult to identify, the introduced multi-head self-attention mechanism in Transformer and the use of Focal Loss instead of cross-entropy loss function were employed to improve the recognition accuracy of subtle defects while ensuring the detection speed. The comparative experiment shows that the improved network model has higher recognition accuracy compared to Faster R-CNN, SSD, Cascade R-CNN, EfficientDet and the original Yolov5s network. The recognition accuracy of typical defects in thermal batteries can reach 98.7%.

Enhancing Image Contrast in Breast USCT Reflection Imaging Using Coherence Factor Beamforming

Abstract Breast cancer is the most common cancer worldwide, and early screening is essential. Existing breast ultrasound is affordable and convenient, but its 2D image quality is poor, and it heavily relies on the doctor’s skills. The novel breast ultrasound computed tomography (USCT) can use a ring array probe to implement hundreds of single transducer emission for full-view focusing synthetic aperture reflection imaging, thereby obtaining high-resolution 3D images. However, single transducer emission results in low acoustic intensity, which further leads to low signal-to-noise ratio and low contrast of reflection images. To improve image contrast, this study incorporated beamforming with coherence factor (CF) into reflection imaging using ring transducers, promising to produce high-contrast USCT reflection images. For evaluation, we developed a USCT prototype with 2048 elements and conducted phantom experiments. We conducted phantom experiments. Results showed that the contrast achieved by the coherence factor method of the breast phantom image is improved from 24.54 dB to 63.18 dB, and the artifacts are significantly suppressed. It can be seen that the CF method could significantly improve the contrast of the breast USCT reflection image.

CafeNet: A Novel Multi‐Scale Context Aggregation and Multi‐Level Foreground Enhancement Network for Polyp Segmentation

ABSTRACTThe detection of polyps plays a significant role in colonoscopy examinations, cancer diagnosis, and early patient treatment. However, due to the diversity in the size, color, and shape of polyps, as well as the presence of low image contrast with the surrounding mucosa and fuzzy boundaries, precise polyp segmentation remains a challenging task. Furthermore, this task requires excellent real‐time performance to promptly and efficiently present predictive results to doctors during colonoscopy examinations. To address these challenges, a novel neural network, called CafeNet, is proposed in this paper for rapid and accurate polyp segmentation. CafeNet utilizes newly designed multi‐scale context aggregation (MCA) modules to adapt to the extensive variations in polyp morphology, covering small to large polyps by fusing simplified global contextual information and local information at different scales. Additionally, the proposed network utilizes newly designed multi‐level foreground enhancement (MFE) modules to compute and extract differential features between adjacent layers and uses the prediction output from the adjacent lower‐layer decoder as a guidance map to enhance the polyp information extracted by the upper‐layer encoder, thereby improving the contrast between polyps and the background. The polyp segmentation performance of the proposed CafeNet network is evaluated on five benchmark public datasets using six evaluation metrics. Experimental results indicate that CafeNet outperforms the state‐of‐the‐art networks, while also exhibiting the least parameter count along with excellent real‐time operational speed.

STU3Net: An Improved U‐Net With Swin Transformer Fusion for Thyroid Nodule Segmentation

ABSTRACTThyroid nodules are a common endocrine system disorder for which accurate ultrasound image segmentation is important for evaluation and diagnosis, as well as a critical step in computer‐aided diagnostic systems. However, the accuracy and consistency of segmentation remains a challenging task due to the presence of scattering noise, low contrast and resolution in ultrasound images. Therefore, we propose a deep learning‐based CAD (computer‐aided diagnosis) method, STU3Net in this paper, aiming at automatic segmentation of thyroid nodules. The method employs a modified Swin Transformer combined with a CNN encoder, which is capable of extracting morphological features and edge details of thyroid nodules in ultrasound images. In decoding through the features for image reconstruction, we introduce a modified three‐layer U‐Net network with cross‐layer connectivity to further enhance image reduction. This cross‐layer connectivity enhances the network's capture and representation of the contained image feature information by creating skip connections between different layers and merging the detailed information of the shallow network with the abstract information of the deeper network. Through comparison experiments with current mainstream deep learning methods on the TN3K and BUSI datasets, we validate the superiority of the STU3Net method in thyroid nodule segmentation performance. The experimental results show that STU3Net outperforms most of the mainstream models on the TN3K dataset, with Dice and IoU reaching 0.8368 and 0.7416, respectively, which are significantly better than other methods. The method demonstrates excellent performance on these datasets and provides radiologists with an effective auxiliary tool to accurately detect thyroid nodules in ultrasound images.

NIR-II femtosecond laser-induced nonlinear absorption facilitates foreground-extraction photoacoustic imaging by monolayer WS2-PVP nanosheets

Exogenous contrast agents have been extensively applied in photoacoustic (PA) molecular imaging for disease diagnosis, benefiting from their advantageous optical, thermal, and internal delivery properties. However, their in vivo performance was inevitably interfered by background tissue optical absorption, resulting in low imaging contrast and sensitivity. Here, we report a NIR-II femtosecond laser-induced nonlinearly enhanced PA imaging technique based on two-photon absorption of monolayer WS2-PVP nanosheets (NSs), which facilitates foreground extraction of the targeting region with the background signal being significantly suppressed. The optical nonlinearity of the monolayer WS2-PVP NSs was first demonstrated by a Z-scan system under the irradiation of a femtosecond laser to be 0.38, with an antithesis of virtually zero for the tissue-mimicking sample. Experiments on tissue-mimicking phantoms and in vitro chicken breast showed that the nonlinear PA enhancement of monolayer WS2-PVP NSs can facilitate foreground-extraction imaging at deep-seated position up to 4 mm. In addition, the in vivo foreground-extraction imaging ability by using monolayer WS2-PVP NSs was further demonstrated by mouse tumor models, where the tumor regions were specifically extracted with high imaging contrast. This work proposed a nonlinearly enhanced contrast mechanism of PA nanoprobes, prefiguring great potential for high-contrast and high-specificity PA molecular imaging.

Segmentation of coronary artery based on discriminative frequency learning and coronary-geometric refinement

Coronary artery segmentation is crucial for physicians to identify and locate plaques and stenosis using coronary computed tomography angiography (CCTA). However, the low contrast of CCTA images and the intricate structures of coronary arteries make this task challenging. To address these difficulties, we propose a novel model, the DFS–PDS network. This network comprises two subnetworks: a discriminative frequency segment subnetwork (DFS) and a position domain scales subnetwork (PDS). DFS introduced a gated mechanism within the feed-forward network, leveraging the Joint Photographic Experts Group (JPEG) compression algorithm, to discriminatively determine which low- and high-frequency information of the features should be preserved for latent image segmentation. The PDS aims to learn the shape prototype by predicting the radius. Additionally, our model has the consistent ability to guarantee region and boundary features through boundary consistency loss. During training, both subnetworks are optimized jointly, and in the testing stage, the coarse segmentation and radius prediction are produced. A coronary-geometric refinement method refines the segmentation masks by leveraging the shape prior to being reconstructed from the radius map, reducing the difficulty of segmenting coronary artery structures from complex surrounding structures. The DFS–PDS network is compared with state-of-the-art (SOTA) methods on two coronary artery datasets to evaluate its performance. The experimental results demonstrate that the DFS–PDS network performs better than the SOTA models, including Vnet, nnUnet, DDT, CS2-Net, Unetr, and CAS-Net, in terms of Dice or connectivity evaluation metrics.

Automatic machine learning model for enhanced partition and identification of breast disorders in breast MRI scan

ABSTRACT The rapid identification and categorisation of breast cancers using low-contrast MRI images presents a significant challenge due to the disease’s prevalence among women of all ages. Nowadays, it is very difficult to classify and generalise models from low-contrast MRI datasets due to the prevalence of class imbalance caused by a wide range of symptoms and untrustworthy data sources. Using ensemble learning with optimised k-means helps with these problems; it reduces overfitting and improves reliability by using methods like k-means clustering, frog feap algorithm (FLA), boosting, and bagging to balance datasets and show the complexity of symptoms. In this research, we present a new strategy that makes use of a hybrid optimised K-Means algorithm combined with the FLA, along with thresholding and morphological approaches, and ensemble learning. There are two main stages to our work. In the initial phase, we preprocess low-contrast MRI images and delineate the breast tumour area from the segmented MRI images. Furthermore, we employ Discrete Wavelet Transformation (DWT) to extract the most salient features. In the next step, the retrieved features will be used as input parameters for an ensemble-learned breast tumour classifier. To determine whether a low-contrast breast tumour MRI picture has a benign or malignant tumour after model training, we use a variety of classifiers, such as k-nearest neighbours (KNN), decision trees (DT), gradient boosting (GB), random forests (RF), and artificial neural networks (ANN). Through a voting mechanism that averages the accuracy of all models, our suggested framework achieves a remarkable 98.8% accuracy rate. In addition, proposed model is quite efficient & reliable to detect the kind of breast tumour or cancer with a sensitivity of 98.02% and a specificity of 97.5%.

(Invited) Nano-Characterization of the Internal Morphology of Polymer Electrolyte Membranes with Cryogenic Scanning Transmission Electron Microscopy

Polymer membranes play important roles in electrochemical devices such as polymer electrolyte fuel cells. In recent years, there has been a growing interest in alkaline anion exchange membrane (AAEM) fuel cells as the alkaline environment offers a potential low-cost alternative to commercial proton exchange membrane (PEM) fuel cells [1]. Nonetheless, optimization of polymer properties such as ion conductivity and mechanical durability is still necessary to achieve commercially viable AAEM based fuel cells. Often, semicrystalline polymer backbones are selected for AAEM design, aiming to replicate the exceptional performance of Nafion in PEM fuel cells where the crystalline domains are believed to act as cross-linkers, mitigating excessive water uptake and mechanical degradation [2,3].Characterizing the local polymer morphology at the nanoscale is a crucial step in understanding how to optimize the design of AAEMs with enhanced durability and ion conductivity. Imaging polymers using conventional transmission electron microscopy (TEM) techniques is challenging however, due to their inherently-high beam sensitivity and low image contrast. Here, we demonstrate nanometer-resolution mapping of the ordering in polymer electrolyte membranes using cryogenic four-dimensional scanning transmission electron microscopy (cryo-4D-STEM). Conventionally used for mapping inorganic crystalline materials, the 4D-STEM technique has more recently also found applications in mapping semicrystalline, soft materials as well [4,5]. Here, using a model system of semicrystalline hydrogenated polynorbornene (hPN) AAEMs [6], we demonstrate the effects of polymer synthesis including molecular weight and thermal history on crystalline morphology and in turn on the polymer performance. Further, we compare the crystalline architecture of the hPN copolymers to that of Nafion 212 (Figure 1). Our results suggest that polymers with homogeneously distributed, nanometer-size crystalline domains lead to improved water management, a property directly related to the mechanical integrity of the polymer, with possible impacts on ion conductivity. More broadly, the results give new insights into how we can tune AAEM design in order to improve their performance.[1] Dekel, Journal of Power Sources, 375, 158-169. (2018).[2] Yang, et al. Chemical Reviews, 122(6), 6117-6321. (2022).[3] Mauritz & Moore, Chemical reviews, 104(10), 4535-4586. (2004).[4] Panova, et al. Nature materials, 18(8), 860-865. (2019).[5] Bustillo, et al. Accounts of chemical research, 54(11), 2543-2551. (2021).[6] Treichel, et al. Macromolecules, 53(19), 8509-8518. (2020).Figure 1: Cryo-4D-STEM crystallinity maps of hPN-co-iPrMe copolymers with varying syntheses and Nafion 212. We observe improvement in the water management of the copolymer membranes as the crystalline domains are reduced in volume and distributed homogeneously. The chemical structures of the polymers are listed below the crystallinity maps. Figure 1

Cold SegDiffusion: A novel diffusion model for medical image segmentation

Medical image segmentation is crucial in accurately identifying and delineating regions of interest in medical images, which can inform the diagnosis and treatment of various diseases. Therefore, developing high-performance computer-aided diagnosis systems for medical image segmentation has become a prominent focus within computer vision. With the development of deep learning, diffusion models have exhibited remarkable performance in medical image segmentation tasks. However, traditional segmentation diffusion models typically employ random Gaussian noise to generate segmentation masks, resulting in non-unique segmentation masks that fail to guarantee the reproducibility of segmentation results. To tackle this issue, this paper introduces a novel method, Cold SegDiffusion, for general medical image segmentation based on the diffusion model. In this method, medical image segmentation is conceptualized as a denoising problem. The segmentation masks covering medical images serve as input for the segmentation encoder, addressing the challenge of generating non-unique masks due to noise randomness. Additionally, the contrast enhancement module is designed to translate features into the frequency domain, addressing the issues of low contrast and boundary disappearance in medical images. Furthermore, the suggested conditional cross-attention module utilizes the conditional encoder and cross-attention weights to enhance important features of the segmentation encoder output, improving the network’s capacity to focus on target regions. The proposed method is validated across three medical image segmentation datasets with different modalities. Experimental results demonstrate that Cold SegDiffusion outperforms mainstream segmentation methods. The code is available at https://github.com/TimesXY/Cold-SegDiffusion.

KACM: A KIS-awared active contour model for low-contrast image segmentation

Low-contrast image segmentation is a challenging task, as it requires distinguishing target objects from the background with minimal intensity differences, while also dealing with factors such as noise and blur. In recent years, the active contour model (ACM) has gained popularity for its high accuracy and efficiency in image segmentation. However, it often struggles with low-contrast images segmentation. To address this issue, we propose a KIS-awared ACM (KACM) in this paper. Firstly, based on the Koschmieder imaging system (KIS), the observed low-contrast image is modeled as a combination of the true image and two imaging factors: the scene transmission function and the global intensity of light in the imaging environment. Next, we pursue the true image in the logarithmic domain using the maximum a posteriori (MAP) criterion, and establish the active contour model. In this model, the true image follows a piecewise lognormal distribution, while the scene transmission function is described as a Markov random field (MRF), with its prior probability being defined as a Gibbs energy function. Lastly, an alternating iterative algorithm that combines variation calculus and gradient descent of the three-step time-splitting method is introduced to solve the proposed model. We validate the proposed model through qualitative and quantitative experiments, demonstrating its effectiveness in low-contrast image segmentation. Compared to several state-of-the-art models, the proposed KACM exhibits competitive performance in terms of both accuracy and efficiency.

Low-contrast X-ray image defect segmentation via a novel core-profile decomposition network

Accurate X-ray image defect segmentation is of paramount importance in industrial contexts, as it is the foundation for product quality control and production safety. Deep learning (DL) has demonstrated powerful image scene understanding capabilities and has achieved unprecedented performance in defect segmentation tasks. However, existing DL methods suffer from significant performance degradation when facing low-contrast X-ray images, as the core information of defects is often obscured and the profile details are ambiguous. To address this issue, this paper explicitly decomposes the X-ray defect segmentation task into two subtasks: core feature learning and elasticity profile refinement, allowing task “serial” decomposition and performance “parallel” improvement. On this basis, a novel core-profile decomposition network (CPDNet) is developed to achieve accurate defect segmentation of X-ray images. Specifically, the core feature learning module is designed to construct the effective feature space from two views, discriminative and structural, to extract defect-related core features from X-ray images. Subsequently, the elasticity profile refinement module is developed to further improve the defect segmentation performance, which makes the first attempt to define the profile refinement as an out-of-distribution detection and leverage the elasticity score to refine the profile details at the pixel level. To fully evaluate the presented method, we conduct a series of experiments using two real-world X-ray defect datasets, and the results demonstrate that the CPDNet outperforms state-of-the-art methods.

An advanced heat source localization technology for intelligent warehousing: A multi-source fusion image segmentation approach leveraging infrared and visible light data

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness.

Generative adversarial network-based reconstruction of healthy anatomy for anomaly detection in brain CT scans.

To help radiologists examine the growing number of computed tomography (CT) scans, automatic anomaly detection is an ongoing focus of medical imaging research. Radiologists must analyze a CT scan by searching for any deviation from normal healthy anatomy. We propose an approach to detecting abnormalities in axial 2D CT slice images of the brain. Although much research has been done on detecting abnormalities in magnetic resonance images of the brain, there is little work on CT scans, where abnormalities are more difficult to detect due to the low image contrast that must be represented by the model used. We use a generative adversarial network (GAN) to learn normal brain anatomy in the first step and compare two approaches to image reconstruction: training an encoder in the second step and using iterative optimization during inference. Then, we analyze the differences from the original scan to detect and localize anomalies in the brain. Our approach can reconstruct healthy anatomy with good image contrast for brain CT scans. We obtain median Dice scores of 0.71 on our hemorrhage test data and 0.43 on our test set with additional tumor images from publicly available data sources. We also compare our models to a state-of-the-art autoencoder and a diffusion model and obtain qualitatively more accurate reconstructions. Without defining anomalies during training, a GAN-based network was used to learn healthy anatomy for brain CT scans. Notably, our approach is not limited to the localization of hemorrhages and tumors and could thus be used to detect structural anatomical changes and other lesions.

A Neural-Network-Based Cost-Effective Method for Initial Weld Point Extraction from 2D Images

This paper presents a novel approach for extracting 3D weld point information using a two-stage deep learning pipeline based on readily available 2D RGB cameras. Our method utilizes YOLOv8s for object detection, specifically targeting vertices, followed by semantic segmentation for precise pixel localization. This pipeline addresses the challenges posed by low-contrast images and complex geometries, significantly reducing costs compared with traditional 3D-based solutions. We demonstrated the effectiveness of our approach through a comparison with a 3D-point-cloud-based method, showcasing the potential for improved speed and efficiency. This research advances the field of automated welding by providing a cost-effective and versatile solution for extracting key information from 2D images.