International Journal of Imaging Systems and Technology最新文献

{"title":"A Time-Adaptive Diffusion-Based CT Image Denoising Method by Processing Directional and Non-Local Information","authors":"Farzan Niknejad Mazandarani,&nbsp;Paul Babyn,&nbsp;Javad Alirezaie","doi":"10.1002/ima.70067","DOIUrl":"https://doi.org/10.1002/ima.70067","url":null,"abstract":"<p>Low-dose computed tomography (CT) images are prone to noise and artifacts caused by photon starvation and electronic noise. Recently, researchers have explored the use of transformer-based neural networks combined with generative diffusion models, showing promising results in denoising CT images. Despite their high performance, these approaches often struggle to process crucial information in the input data, resulting in suboptimal image quality. To address this limitation, we propose Starformer, a novel transformer-based operation designed to extract non-local directional features essential for diagnostic accuracy while maintaining an acceptable computational complexity overhead. Starformer is seamlessly integrated into the time-adaptive schedules of a diffusion model, dynamically balancing global structural extraction and fine texture refinement throughout the diffusion process. This enables the generation of high-quality, realistic textures in the final denoised images. Extensive experimental results demonstrate the effectiveness of both approaches in enhancing CT image quality, with improvements of up to 15% in PSNR and 36% in SSIM, highlighting their superiority over state-of-the-art methods.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.70067","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Generating Medical Reports With a Novel Deep Learning Architecture","authors":"Murat Ucan,&nbsp;Buket Kaya,&nbsp;Mehmet Kaya","doi":"10.1002/ima.70062","DOIUrl":"https://doi.org/10.1002/ima.70062","url":null,"abstract":"<p>The writing of medical reports by doctors in hospitals is a critical and sensitive process that is time-consuming, prone to human error, and requires medical experts on site. Existing work on autonomous medical report generation using medical images as input has not achieved sufficiently high success. The goal of this paper is to present a new, fast, and high-performance method. For the autonomous generation of paragraph-level medical reports. A deep learning-based hybrid encoder–decoder architecture called G-CNX is developed to generate meaningful reports. ConvNeXtBase is used on the encoder side, and GRU-based RNN is used on the decoder side. Images and reports from the Indiana University Chest X-ray and ROCOv2 data sets were used in the training, validation, and testing processes of the study. The results of the experiments showed that the autonomously generated medical reports had the highest performance compared to other studies in the literature. In the Indiana University Chest X-ray data set, success rates of 0.6544, 0.5035, 0.3682, 0.2766, 0.2766, and 0.4277 were obtained in Bleu-1, Bleu-2, Bleu-3, Bleu-4, and Rouge evaluation metrics, respectively. In the ROCOv2 data set, success scores of 0.5593 and 0.3990 were obtained in Bleu-1 and Rouge evaluation metrics, respectively. In addition to numerical quantifiable analysis, the results of the study were also analyzed observationally and based on density plots. Statistical significance tests were also conducted to prove the reliability of the results. The results show that the test results obtained in the study have semantic properties similar to those of reports written by real doctors and that the autonomous reports produced are consistent and reliable. The proposed method can improve the efficiency of medical reporting, reduce the workload of specialized doctors, and improve the quality of diagnosis and treatment processes.</p>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ima.70062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602468","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D Microscopic Images Segmenter Modeling by Applying Two-Stage Optimization to an Ensemble of Segmentation Methods Using a Genetic Algorithm","authors":"Muamer Kafadar,&nbsp;Zikrija Avdagic,&nbsp;Ingmar Besic,&nbsp;Samir Omanovic","doi":"10.1002/ima.70058","DOIUrl":"https://doi.org/10.1002/ima.70058","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents research related to segmentation based on supervisory control, at multiple levels, of optimization of parameters of segmentation methods, and adjustment of 3D microscopic images, with the aim of creating a more efficient segmentation approach. The challenge is how to improve the segmentation of 3D microscopic images using known segmentation methods, but without losing processing speed. In the first phase of this research, a model was developed based on an ensemble of 11 segmentation methods whose parameters were optimized using genetic algorithms (GA). Optimization of the ensemble of segmentation methods using GA produces a set of segmenters that are further evaluated using a two-stage voting system, with the aim of finding the best segmenter configuration according to multiple criteria. In the second phase of this research, the final segmenter model is developed as a result of two-level optimization. The best obtained segmenter does not affect the speed of image processing in the exploitation process as its operating speed is practically equal to the processing speed of the basic segmentation method. Objective selection and fine-tuning of the segmenter was done using multiple segmentation methods. Each of these methods has been subject to an intensive process of a significant number of two-stage optimization cycles. The metric has been specifically created for objective analysis of segmenter performance and was used as a fitness function during GA optimization and result validation. Compared to the expert manual segmentation, segmenter score is 99.73% according to the best mean segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set). Segmenter score is 99.49% according to the most stable segmenter principle (average segmentation score for each 3D slice image with respect to the entire sample set and considering the reference image classes MGTI median, MGTI voter and GGTI).</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595620","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"HSCFNet: Lightweight Convolutional Neural Network for the Classification of Infectious and Non-Infectious Skin Diseases","authors":"Xiangyu Deng,&nbsp;Yapeng Zheng","doi":"10.1002/ima.70052","DOIUrl":"https://doi.org/10.1002/ima.70052","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate differentiation between infectious and non-infectious skin diseases is crucial in the field of dermatological diagnosis, and although deep learning techniques have achieved remarkable results in the classification of a wide range of dermatological diseases, there is still a lack of effective unified frameworks to achieve this goal. To this end, this paper proposes a lightweight convolutional neural network, HSCFNet, for classifying 9 mainstream infectious skin diseases and 10 non-infectious skin diseases. HSCFNet consists of two core modules, the multi-gate hybrid convolution module (MGHC) and triple residual fusion module (TRF). MGHC integrates standard convolution and improved deformable convolution to form two branches and selects different branches for feature extraction through parameter control, while introducing a gating mechanism for feature selection of the extracted features to strengthen the ability of extracting important features. The TRF module facilitates the information interaction between features by fusing three different resolutions of features, which further improves the classification performance of the model. The experimental results show that the accuracy, precision, recall, specificity, and F1 score of HSCFNet reach 97.87%, 97.76%, 97.26%, 99.88%, and 97.43%, respectively, and the size of the model is only 26.1 MB, which is lightweight while maintaining high performance. Compared with 10 existing mainstream classification models, HSCFNet demonstrates the best classification performance. This study provides an efficient and lightweight solution for clinical skin disease diagnosis, which is important for accurately distinguishing mainstream infectious and non-infectious skin diseases.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595622","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CLA-UNet: Convolution and Focused Linear Attention Fusion for Tumor Cell Nucleus Segmentation","authors":"Wei Guo,&nbsp;Zhanxu Liu,&nbsp;Yu Ou","doi":"10.1002/ima.70041","DOIUrl":"https://doi.org/10.1002/ima.70041","url":null,"abstract":"<div>\u0000 \u0000 <p>The accurate diagnosis of tumors is crucial for improving treatment outcomes. To precisely delineate the nucleus regions of tumor cells in hematoxylin and eosin (H&amp;E) stained tissue images and reduce computational overhead, we propose a novel encoder-decoder architecture named Convolution and focused linear attention fusion UNet (CLA-UNet), which integrates depthwise separable convolution and convolution-focused linear attention into the U-Net network. The innovation of this study is reflected in the following three aspects: first, at the skip connections, it utilizes the Global–Local Feature Fusion and Split-Input Transformer (GLFS Transformer) block to extract global feature information, which is then input to the corresponding layers of the decoder; second, it employs depthwise separable convolution blocks to construct the backbone network, thereby deepening the network; finally, it adds a channel attention module at the decoder to focus on important channel information. Experimental results on the MoNuSeg public database of tumor cells show that the algorithm achieves an IoU, Dice score, precision, and recall of 66.18%, 79.57%, 83.23%, and 76.91%, respectively. Compared with other segmentation methods, this algorithm demonstrates superior segmentation performance. The model proposed in this study significantly outperforms other comparison models in segmentation results, while maintaining an extremely low parameter count and computational cost. The lightweight design of the model facilitates the promotion and application of this research.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"3D U-Net-Based Brain Tumor Semantic Segmentation Using a Modified Data Generator","authors":"Dinesh Kumar,&nbsp;Dimple Sethi,&nbsp;Wagaye Tadele Kussa,&nbsp;Yeabsira Mengistu Dana,&nbsp;Hitesh Kag","doi":"10.1002/ima.70056","DOIUrl":"https://doi.org/10.1002/ima.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>Brain tumors, particularly gliomas, pose a significant global health challenge, causing numerous fatalities annually. Among gliomas, glioblastoma stands out as a highly aggressive type, often resulting in severe symptoms. Accurate segmentation of brain tumors from multimodal magnetic resonance imaging (MRI) data is crucial for effective diagnosis and treatment planning. This study introduces a novel 3D U-Net semantic segmentation model with a modified data generator approach, specifically tailored for the brain tumor segmentation (BraTS) 2020 dataset. The modified data generator is unique in that it performs on-the-fly data augmentation, generating diverse and distinct data samples during training. This approach reduces overfitting and enhances generalization, which is critical for handling the variability of brain tumor presentations. The model was trained end-to-end without weight transfer, optimizing the dice score as the primary evaluation metric. The proposed model achieved dice scores of 82.2%, 90.3%, and 77.8% for tumor core, whole tumor, and enhancing tumor regions, respectively, on the BraTS 2020 validation dataset. The minimal variation from training data underscores the model's robustness and reliability in segmenting different tumor subtypes. The modified data generator approach presents a promising advancement for brain tumor segmentation, with the potential for significant improvements in treatment planning and patient outcomes. This model could support more accurate and robust segmentation in clinical applications by effectively addressing data variability.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of MR-Guided Focused Ultrasound System: Comparative Study of Water Bolus and Electrically Optimized Material Using Automated Machine Learning","authors":"Eunwoo Lee,&nbsp;Taewoo Nam,&nbsp;Daniel Hernandez,&nbsp;Eugene Ozhinsky,&nbsp;Kazim Narsinh,&nbsp;Leo Sugrue,&nbsp;Yeji Han,&nbsp;Kisoo Kim,&nbsp;Kyoung-Nam Kim","doi":"10.1002/ima.70061","DOIUrl":"https://doi.org/10.1002/ima.70061","url":null,"abstract":"<div>\u0000 \u0000 <p>Magnetic resonance-guided focused ultrasound (MRgFUS) is a therapeutic technology designed for the treatment of neurological disorders, enabling precise focal heating under magnetic resonance imaging (MRI) guidance. However, electromagnetic (EM) interaction between the radiofrequency (RF) coil and the MRgFUS system leads to increased specific absorption rate (SAR), reduced RF transmit magnetic (|B₁⁺|)-field homogeneity, and decreased signal-to-noise ratio (SNR). In this study, we compared a conventional water bolus containing sodium chloride and sterile water with an electrically optimized material (EOM) optimized using an automated machine learning (Auto-ML) approach to minimize SAR while maximizing |B₁⁺|-field quality. EM simulation results demonstrated that our EOM achieved significant improvements in |B₁⁺|-field homogeneity and a reduction in peak spatial SAR averaged over 10 g (psSAR_10g) compared to the conventional water bolus. These findings suggest that Auto-ML-based EOM can enhance the safety and efficiency of MRgFUS procedures.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MARS-Net: Multi-Scale Attention Residual Spatiotemporal Network for Robust Left Ventricular Ejection Fraction Prediction in Echocardiography Videos","authors":"Shun Cheng,&nbsp;Fangqi Guo,&nbsp;Qihui Guo,&nbsp;Haobo Chen,&nbsp;Zhou Xu,&nbsp;Bo Zhang,&nbsp;Jiaqi Zhao,&nbsp;Qi Zhang","doi":"10.1002/ima.70059","DOIUrl":"https://doi.org/10.1002/ima.70059","url":null,"abstract":"<div>\u0000 \u0000 <p>Left ventricular ejection fraction (LVEF) is a key measure of heart pumping performance, playing a pivotal role in the ongoing management and efficacy assessment of cardiovascular disease treatments. By quantifying the percentage of blood that is pumped out of the left ventricle with each heartbeat, LVEF provides invaluable insights into the overall efficiency of the heart's function, enabling clinical professionals to make informed decisions regarding point of care and therapeutic strategies. However, accurate LVEF measurement faces challenges such as large observer variability, poor image quality, and the complexity of cardiac motion. To address these issues, a residual spatiotemporal network with a multi-scale attention mechanism is proposed for robust LVEF prediction in transthoracic echocardiographic videos, named the Multi-scale Attention Residual Spatiotemporal Network (MARS-Net). The MARS-Net excels at extracting spatiotemporal features from echocardiographic videos, accurately capturing heart dynamics and morphology while demonstrating robust performance across multi-center data. The sub-video division block is first designed to partition echocardiographic videos into smaller sub-videos, capturing key cardiac motion. The input embedding block compresses these sub-videos for efficient processing. Then the multi-scale attention residual block enhances spatiotemporal feature extraction by combining multi-scale convolutions with attention mechanisms to improve focus on important details. Finally, the output convolutional block transforms the extracted features into the final LVEF prediction, ensuring accurate measurement. Through extensive evaluations, our MARS-Net outperforms comparative deep learning models in LVEF prediction, offering exceptional promise for diagnosing heart dysfunction. Notably, it has achieved commendable results in three medical centers, underscoring its generalizability and reliability across varied clinical environments.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143594844","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GRASP-Net: Grouped Residual Convolution U-Net With Attention Mechanism and Atrous Spatial Pyramid Pooling for Prostate Zone Segmentation Using MR Images","authors":"R. Deiva Nayagam,&nbsp;D. Selvathi","doi":"10.1002/ima.70060","DOIUrl":"https://doi.org/10.1002/ima.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>Prostate cancer is a prevalent disease in men, especially among the elderly, and magnetic resonance imaging is the leading acquisition method for the diagnosis and evaluation of the prostate. Accurate segmentation of the prostate, particularly the transition zone and peripheral zone, is crucial for early detection and effective treatment planning. This work introduces GRASP-Net as an innovative deep learning-based model to improve prostate MRI zonal segmentation accuracy. GRASP-Net integrates grouped residual convolutional modules, attention mechanisms, convolutional block attention module, and atrous spatial pyramid pooling blocks to enhance feature extraction and boundary segmentation. The model has been evaluated on the Medical Segmentation Decathlon Task 05 Prostate dataset, comparing its performance against other well-known models. Overall, the GRASP-Net model achieved higher segmentation results with a dice similarity coefficient of 0.928 for the transition zone and 0.864 for the peripheral zone, surpassing previous state-of-the-art results. Additionally, the model exhibits significant performance on 95 percentile Hausdorff Distance, Average Surface Distance, and Sensitivity values and proving its accuracy in anatomical prostate structure localization. These advancements emphasize the promising prospect of the GRASP-Net model to advance prostate cancer diagnosis and treatment, presenting an effective tool for clinical usage.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"EMViT-BCC: Enhanced Mobile Vision Transformer for Breast Cancer Classification","authors":"Jacinta Potsangbam,&nbsp;Salam Shuleenda Devi","doi":"10.1002/ima.70053","DOIUrl":"https://doi.org/10.1002/ima.70053","url":null,"abstract":"<div>\u0000 \u0000 <p>Breast cancer (BC) accounts for most cancer-related deaths worldwide, so it is crucial to consider it as a prominent issue and emphasize proper diagnosis and timely detection. This study introduces a deep learning strategy called EMViT-BCC for the BC histopathology image classification to two class and eight class. The proposed model utilizes the Mobile Vision Transformer (MobileViT) block, which captures local and global features and extracts necessary features for the classification task. The proposed approach is trained and evaluated on the standard BreaKHis dataset. The model is evaluated with both the original raw histopathology images as well as the stain-normalized images for the analysis of the classification task. Extensive experiments demonstrate that the proposed EMViT-BCC achieves higher accuracy and robustness in classifying benign and malignant images and identifying various subtypes of BC. Our results demonstrate that by incorporating further layers, the classification performance of MobileViT can be greatly enhanced, with 99.43% for two-class and 93.61% for eight-class classification. These findings suggest that while stain normalization can standardize variations, original image data retain crucial details that enhance model performance. In comparison with the existing works, the proposed methodology surpasses the state-of-the-art (SOTA) methods for BC histopathology image classification. The proposed approach offers a promising solution for reliable BC classification for both binary and multi-class.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 2","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554839","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}