International Journal of Imaging Systems and Technology最新文献

筛选
英文 中文
Study of Retinal Vessel Segmentation Algorithm Based on Receptive Field Expansion and Feature Refinement 基于感受野扩展和特征细化的视网膜血管分割算法研究
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-19 DOI: 10.1002/ima.70164
Xinghua Wang, Jiawen Cao, Runxin Meng, Xiaolong Liu, Jie Wang, Yuting Tang, Ruijin Sun
{"title":"Study of Retinal Vessel Segmentation Algorithm Based on Receptive Field Expansion and Feature Refinement","authors":"Xinghua Wang,&nbsp;Jiawen Cao,&nbsp;Runxin Meng,&nbsp;Xiaolong Liu,&nbsp;Jie Wang,&nbsp;Yuting Tang,&nbsp;Ruijin Sun","doi":"10.1002/ima.70164","DOIUrl":"https://doi.org/10.1002/ima.70164","url":null,"abstract":"<div>\u0000 \u0000 <p>Missing blood vessels, fracturing blood vessels, and mistaking nonvascular features for blood vessels are major problems in retinal vessel segmentation tasks. This paper suggests an enhanced model that incorporates the Inception module and attention mechanism, based on the U-Net network topology, to solve these problems. In order to get richer scale information and enhance the model's recognition of vascular details, the encoder portion of the model first employs convolution kernels of varying sizes to collect multilevel characteristics of the picture. Second, to enhance feature processing between codecs and highlight significant features, an attention module is integrated into skip connections to extract spatial location information and interchannel interactions. This information is then coupled with residual connections. Finally, in the decoding stage, a residual attention module was constructed to extract vascular features and improve processing speed. On the DRIVE standard fundus image dataset, the proposed algorithm demonstrates significant performance enhancements compared to the conventional U-Net baseline. Specifically, it achieves absolute improvements of 1.94% in sensitivity, 1.07% in Jaccard index, 0.75% in Dice correlation coefficient, and 0.74% in Matthews correlation coefficient. Compared with other algorithms, it also has certain advantages and can effectively perform retinal vessel segmentation.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657690","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}
引用次数: 0
MaxGlaViT: A Novel Lightweight Vision Transformer-Based Approach for Early Diagnosis of Glaucoma Stages From Fundus Images MaxGlaViT:一种基于眼底图像的轻型视觉转换器的青光眼早期诊断方法
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-19 DOI: 10.1002/ima.70159
Mustafa Yurdakul, Kübra Uyar, Şakir Taşdemir
{"title":"MaxGlaViT: A Novel Lightweight Vision Transformer-Based Approach for Early Diagnosis of Glaucoma Stages From Fundus Images","authors":"Mustafa Yurdakul,&nbsp;Kübra Uyar,&nbsp;Şakir Taşdemir","doi":"10.1002/ima.70159","DOIUrl":"https://doi.org/10.1002/ima.70159","url":null,"abstract":"<div>\u0000 \u0000 <p>Glaucoma is a prevalent eye disease that often progresses without symptoms and can lead to permanent vision loss if not detected early. The limited number of specialists and overcrowded clinics worldwide make it difficult to detect the disease at an early stage. Deep learning-based computer-aided diagnosis (CAD) systems are a solution to this problem, enabling faster and more accurate diagnosis. In this study, we proposed MaxGlaViT, a novel Vision Transformer model based on MaxViT to diagnose different stages of glaucoma. The architecture of the model is constructed in three steps: (i) the Multi Axis Vision Transformer (MaxViT) structure is scaled in terms of the number of blocks and channels, (ii) low-level feature extraction is improved by integrating the attention mechanism into the stem block, and (iii) high-level feature extraction is improved by using the modern convolutional structure. The MaxGlaViT model was tested on the HDV1 fundus image data set and compared to a total of 80 deep learning models. The results show that the MaxGlaViT model, which contains effective block structures, outperforms previous literature methods in terms of both parameter efficiency and classification accuracy. The model performs particularly high success in detecting the early stages of glaucoma. MaxGlaViT is an effective solution for multistage diagnosis of glaucoma with low computational cost and high accuracy. In this respect, it can be considered as a candidate for a scalable and reliable CAD system applicable in clinical settings.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657719","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}
引用次数: 0
Differentiating Sporadic Colonic Hamartoma From Adenomas in Narrow Band Imaging Using a Novel AI Network: Attention Based Multi-Scale CNN (AM-Net) 基于注意力的多尺度CNN (AM-Net)在窄带成像中鉴别散发性结肠错构瘤和腺瘤
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-19 DOI: 10.1002/ima.70168
Aditi Jain, Saugata Sinha, Bhargava Chinni, Srijan Mazumdar
{"title":"Differentiating Sporadic Colonic Hamartoma From Adenomas in Narrow Band Imaging Using a Novel AI Network: Attention Based Multi-Scale CNN (AM-Net)","authors":"Aditi Jain,&nbsp;Saugata Sinha,&nbsp;Bhargava Chinni,&nbsp;Srijan Mazumdar","doi":"10.1002/ima.70168","DOIUrl":"https://doi.org/10.1002/ima.70168","url":null,"abstract":"<div>\u0000 \u0000 <p>There are no existing protocols for optical diagnosis of Sporadic colonic hamartomas, which are benign polyps, using the narrow-band imaging (NBI). Efficient detection of hamartoma polyps is difficult due to the similar appearances in NBI with other polyp types. Differentiating hamartoma from adenomatous is necessary for efficient utilization of “diagnose and leave” or “resect and discard” strategies during colonoscopy procedure. To address the above challenge, we conducted a study where suitably trained AI algorithms were employed for automatic differentiation of hamartoma and adenomatous polyps. An Attention based Multi-scale CNN (AM-Net), that integrates a Multi-scale Residual Network (MRN) with a parallel attention module (PAM) was introduced in this study. The Multi-scale Residual Network (MRN) structure enables the model to capture local multi-scale features while the attention module identifies “where to focus” and “what to focus on” through channel and spatial dimensional attention. To the best of our knowledge, AM-Net is the first AI-based model designed to differentiate colonic hamartomas from adenomatous polyps using NBI colonoscopy videos. In this study the performance of AM-Net was evaluated using a real-life colonoscopy polyp video comprising 1706 NBI polyp frames collected from 45 patients at a tertiary care hospital. The dataset includes 761 frames of hamartoma polyps and 945 frames of adenomatous polyps. The results demonstrated that efficient differentiation between hamartoma and adenomatous polyps is possible using a suitably designed and trained AI network. The proposed AM-Net achieved an accuracy of 86.97%, precision of 82.84%, F1-score of 87.75%, and AUC of 0.95, outperforming existing state-of-the-art CNN architectures and attention mechanisms across all metrics by effectively capturing structural details such as polyp mucosal patterns, textures, and boundaries, showcasing its ability to substantially enhance the accurate classification of hamartoma polyps.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657715","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}
引用次数: 0
Development of an Optical Breast Cancer Diagnosis System Using Laser Speckle and Machine Learning-Assisted Fusion of Texture Maps 基于激光散斑和机器学习辅助纹理图融合的光学乳腺癌诊断系统的开发
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-19 DOI: 10.1002/ima.70169
Doaa Youssef, Somia A. M. Soliman, Jala El-Azab, Rasha Wessam, Tawfik Ismail
{"title":"Development of an Optical Breast Cancer Diagnosis System Using Laser Speckle and Machine Learning-Assisted Fusion of Texture Maps","authors":"Doaa Youssef,&nbsp;Somia A. M. Soliman,&nbsp;Jala El-Azab,&nbsp;Rasha Wessam,&nbsp;Tawfik Ismail","doi":"10.1002/ima.70169","DOIUrl":"https://doi.org/10.1002/ima.70169","url":null,"abstract":"<div>\u0000 \u0000 <p>Breast cancer remains one of the most prevalent diagnosed cancers that represents a serious threat to public health. It is associated with many aggressive pathological features and lower survival rates, especially in young women. The unique absorption and scattering properties of the different constituents of breast tissue give rise to the idea of using light as a noninvasive method for identifying breast lesions. In this study, we introduce a low-cost and nondestructive optical diagnosis system based on laser speckles for the early detection of breast cancer. The proposed optical system is implemented using two independent low-power laser sources operating at 532 and 632 nm to generate sets of speckle patterns from ex vivo breast tissue samples. We then present a novel feature extraction method to capture any structure modifications caused by breast masses from such information-rich patterns. This method proposes texture map analysis based on multi-neighborhood local entropy and Gabor filter bank. To assess the discriminatory power of the extracted features, three independent supervised classification models are utilized. The experimental results indicate that features extracted from speckle patterns generated at 632 nm present higher performance than those built with 532 <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>nm</mi>\u0000 </mrow>\u0000 <annotation>$$ mathrm{nm} $$</annotation>\u0000 </semantics></math>. The merged features from both laser radiations provide a comprehensive assessment of the breast tissue characteristics. The proposed method demonstrated an enhanced performance of classification models, with accuracy values reaching up to 98.48% and weighted F1 scores up to 98.54%. This study highlights the potential of laser speckle imaging combined with AI for the early identification of breast abnormalities.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657716","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}
引用次数: 0
ARCNet: Adaptive Reconstruction-Driven Cascaded Network for Deformable Registration of Images With Pathologies ARCNet:自适应重建驱动的级联网络,用于具有病理的图像的可变形配准
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-19 DOI: 10.1002/ima.70165
Li Lian, Jianing Du, Jiajia Liu, Wanman Li, Qing Chang
{"title":"ARCNet: Adaptive Reconstruction-Driven Cascaded Network for Deformable Registration of Images With Pathologies","authors":"Li Lian,&nbsp;Jianing Du,&nbsp;Jiajia Liu,&nbsp;Wanman Li,&nbsp;Qing Chang","doi":"10.1002/ima.70165","DOIUrl":"https://doi.org/10.1002/ima.70165","url":null,"abstract":"<div>\u0000 \u0000 <p>Deformable registration is a critical task in medical image analysis. However, registration of images with tumors is challenging due to absent correspondences induced by the tumor. Furthermore, disease progression or normal aging may cause more intricate deformations in the brain. Therefore, this paper proposes a new adaptive reconstruction-driven cascaded network (ARCNet). Specifically, the symmetric-constrained feature reasoning (SFR) module is designed to reconstruct tumor regions without valid correspondence as normal tissue, allowing the establishment of dense correspondences during the registration process. The dilated multi-receptive feature fusion (DMFF) module is further introduced, which collects long-range features from different dimensions and helps generate well-structured content in the tumor region reconstruction, especially for large tumor cases. Then an adaptive importance-aware guidance module (AIG) is proposed, which adjusts the local importance of a region according to the deformation complexity, directing the network to focus on difficult-to-align regions with complex deformations, thus improving the registration accuracy. We conducted experiments on the BraTS 2021 dataset to validate the effectiveness of the SFR, DMFF, and AIG modules. Using quantitative metrics such as Dice Similarity Coefficient (Dice), the Local Normalized Cross-Correlation (LNCC), the negative Jacobian determinant percentage (%|J| ≤ 0), the 95% Hausdorff Distance (HD95), and Average Surface Distance (ASD), experimental results show that the proposed method effectively handles the problem of pathological image registration, which can maintain the smooth deformation of the tumor region while maximizing the image similarity of normal regions.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657720","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}
引用次数: 0
DIER-Net: Debiased Learning With Medical Image Noisy Label by Intrinsic and Extrinsic Regularization DIER-Net:基于内、外正则化的医学图像噪声标签去偏学习
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-17 DOI: 10.1002/ima.70160
Cheng Xue
{"title":"DIER-Net: Debiased Learning With Medical Image Noisy Label by Intrinsic and Extrinsic Regularization","authors":"Cheng Xue","doi":"10.1002/ima.70160","DOIUrl":"https://doi.org/10.1002/ima.70160","url":null,"abstract":"<div>\u0000 \u0000 <p>In medical image analysis, the presence of noisy labels and imbalanced data poses significant challenges to the performance of deep learning models, particularly in critical diagnostic tasks. To address this issue, we propose DIER-Net, a learning with noisy label framework designed to handle noisy labels in imbalanced medical datasets. Our approach introduces a debiased sample selection technique that effectively filters out noisy labels while preserving important minority class samples. Additionally, we employ intrinsic and extrinsic regularization strategies to enhance the model's robustness by leveraging both clean and noisy data. Our method is evaluated on two widely used medical image datasets: the ISIC melanoma classification and Kaggle histopathologic lymph node classification. The experimental results demonstrate that DIER-Net consistently outperforms existing state-of-the-art methods, particularly in settings with high levels of label noise, offering a robust solution for real-world clinical applications where noisy and imbalanced data are common. DIER-Net provides an effective approach to enhance the reliability of AI systems in medical imaging, contributing to more accurate and trustworthy diagnostic outcomes.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144647123","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}
引用次数: 0
Supervised Constructive Learning-Based Model for Identifying Colorectal Cancer Tissue Types From Histopathological Images 从组织病理图像中识别结直肠癌组织类型的基于监督的建设性学习模型
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-16 DOI: 10.1002/ima.70161
Kazim Firildak, Gaffari Celik, Muhammed Fatih Talu
{"title":"Supervised Constructive Learning-Based Model for Identifying Colorectal Cancer Tissue Types From Histopathological Images","authors":"Kazim Firildak,&nbsp;Gaffari Celik,&nbsp;Muhammed Fatih Talu","doi":"10.1002/ima.70161","DOIUrl":"https://doi.org/10.1002/ima.70161","url":null,"abstract":"<div>\u0000 \u0000 <p>Colorectal cancer is the disease with the second highest mortality rate among cancer types. The survival rate is increased with early diagnosis and treatment of this disease. In this study, a supervised constructive learning based model is proposed for the detection of colorectal cancer using datasets containing hematoxylin and eosin stained colon histopathological images. The datasets used include multi-class datasets (Kather-5K, CRC-7K, NCT-100K) and binary class datasets (Kather MSI and MHIST). The proposed model consists of an encoder (ReFeatureBlock (RFB), depthwise convolution (DWC), and global average pooling (GAP)), a projection head, and fully connected classification networks. With these networks, it is possible to obtain important features, reduce the computational cost, minimize noise sensitivity, and prevent poor margin possibilities. Additionally, the Grad-CAM method was used to ensure transparency and explainability of the model's decision-making processes. In multiple classification experiments, in applications performed by combining Kather-5K, CRC-7K, and NCT-100K datasets, the proposed model achieved the highest performance with 99.21% accuracy, 99.19% precision, 99.19% recall, 99.19% F1-score, 99.92% specificity, and 99.56% AUC values, respectively. In addition, in tests performed on individual datasets, high performances such as 99.10% accuracy for Kather-5K, 99.76% accuracy for CRC-7K, and 99.19% accuracy for NCT-100K were achieved. In binary classification experiments with the MHIST dataset, the proposed model showed the highest success with 99.52% accuracy, 99.30% precision, 99.49% recall, 99.40% F1-score, 99.49% specificity, and 99.49% AUC, respectively. Moreover, the proposed model is compared with state-of-the-art techniques in the literature in the classification of colorectal cancer tissues, and the results are discussed. The findings show that the proposed model provides higher classification success in statistical metrics. The codes of the proposed model are publicly available at https://github.com/KAZIMFIRILDAK23/CRC-SCL.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635568","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}
引用次数: 0
Computer Aided Grading System of Digital Microscopic Blastocyst Images 数字显微囊胚图像的计算机辅助分级系统
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-16 DOI: 10.1002/ima.70162
Shimaa M.Khder, Eman Anwar Hassan Mohamed, Ahmed Elbialy, Inas A.Yassine
{"title":"Computer Aided Grading System of Digital Microscopic Blastocyst Images","authors":"Shimaa M.Khder,&nbsp;Eman Anwar Hassan Mohamed,&nbsp;Ahmed Elbialy,&nbsp;Inas A.Yassine","doi":"10.1002/ima.70162","DOIUrl":"https://doi.org/10.1002/ima.70162","url":null,"abstract":"<div>\u0000 \u0000 <p>Blastocyst grading is among the critical factors that influence the success of in vitro fertilization (IVF) treatment cycles. Blastocyst morphology grading is traditionally performed through manual microscope examinations. Manual microscopic blastocyst morphological grading is a time-consuming task that suffers from intraobserver and interobserver variation. Therefore, automation of blastocyst grading is essential for IVF success. In this paper, we propose a computer-aided grading system for blastocyst images based on Gardner's grading system. Gardner's grading system consists of three components that correspond to specific regions of the blastocyst. Each component has its own classes. The first component, Expansion, is graded into six grades ranging from 1 to 6. The second component is inner cell mass (ICM) grading into three grades (A-B-C). The third component is trophectoderm (TE) grading into three grades (A-B-C). The proposed system is comprised of three basic stages: dataset acquisition, data preparation, and classification. The dataset was acquired from the “Boy and Girl” clinic center, Cairo, Egypt. The dataset comprises 1015 blastocyst images, extracted from 651 images captured by inverted microscope “Nikon eclipse Ti-U” with a resolution of 640 × 480 pixels. The data preparation stage comprises of blastocysts extraction and localization followed by blastocyst labeling by an experienced embryologist. Data augmentation was, later, performed to enhance the robustness and generalize the capability of the trained models on limited datasets. Subsequently, this work contributes by employing many convolutional neural networks (CNNs) including: VGG16, RESNET50, MobileNetv2, EfficientNetB0, and YOLOv8 to choose the best classification framework for blastocysts. The novelty of our work is based on full automation of standard Gardner's grading system using single static microscopic image. The results showed that the fine YOLOv8 framework achieved the highest accuracy of 97%, 82%, and 89% for expansion, TE, and ICM, respectively.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144635247","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}
引用次数: 0
Causality-Inspired Neural Network for the Identification of Schizophrenia 因果启发神经网络识别精神分裂症
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-15 DOI: 10.1002/ima.70156
Shayel Parvez Shams, Saqib Mamoon, Zhengwang Xia, Jianfeng Lu
{"title":"Causality-Inspired Neural Network for the Identification of Schizophrenia","authors":"Shayel Parvez Shams,&nbsp;Saqib Mamoon,&nbsp;Zhengwang Xia,&nbsp;Jianfeng Lu","doi":"10.1002/ima.70156","DOIUrl":"https://doi.org/10.1002/ima.70156","url":null,"abstract":"<div>\u0000 \u0000 <p>Functional connectivity (FC) analysis has emerged as a pivotal tool for identifying neural biomarkers in schizophrenia. However, existing methods often lack interpretability and fail to capture temporally dynamic causal connectivity. To address this limitation, we propose a novel Granger causality (GC)-inspired Convolutional Long Short-Term Memory (cLSTM) model for diagnosing schizophrenia. Our framework integrates a dynamically learned sparsity-inducing mask within the cLSTM architecture to prioritize causal connectivity patterns while filtering out non-informative connections, thereby enhancing computational efficiency and model interpretability. We evaluated the model on the COBRE dataset across seven parcellation atlases, achieving superior performance with a mean accuracy exceeding 90% and F1-scores of up to 92%, thereby outperforming state-of-the-art methods. The GC-inspired mask reduces redundant parameters by 40%–60%, facilitating the identification of clinically relevant biomarkers, including dysregulated prefrontal-hippocampal and default mode network (DMN) interactions. By integrating temporal dependency modeling with causal inference, our approach not only enhances diagnostic accuracy but also provides neurobiologically interpretable insights into functional disruptions associated with schizophrenia. This study bridges the gap between complex deep learning (DL) models and clinically actionable tools, demonstrating significant potential for psychological healthcare applications.</p>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624494","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}
引用次数: 0
Improving Image Quality of Thin-Slice and Low-keV Images in Dual-Energy CT Angiography for Children With Neuroblastoma Using Deep Learning Image Reconstruction 应用深度学习图像重建提高儿童神经母细胞瘤双能CT血管造影薄层低分辨率图像质量
IF 3 4区 计算机科学
International Journal of Imaging Systems and Technology Pub Date : 2025-07-13 DOI: 10.1002/ima.70143
Jihang Sun, Haoyan Li, Shen Yang, Ruifang Sun, Fanning Wang, Zhenpeng Chen, Yun Peng
{"title":"Improving Image Quality of Thin-Slice and Low-keV Images in Dual-Energy CT Angiography for Children With Neuroblastoma Using Deep Learning Image Reconstruction","authors":"Jihang Sun,&nbsp;Haoyan Li,&nbsp;Shen Yang,&nbsp;Ruifang Sun,&nbsp;Fanning Wang,&nbsp;Zhenpeng Chen,&nbsp;Yun Peng","doi":"10.1002/ima.70143","DOIUrl":"https://doi.org/10.1002/ima.70143","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Neuroblastoma (NB) is a common malignant tumor in children, and the evaluation of vascular involvement image-defined risk factors (IDRFs) using computed tomography angiography (CTA) is crucial for prognostic assessment. To evaluate whether deep learning image reconstruction (DLIR) can improve the image quality of thin-slice, low-keV images in dual-energy CTA (DECTA) and provide a more accurate assessment of IDRFs in children with NB. Forty-three NB patients (median age: 2 years., 6 months to 7 years), who underwent chest or abdominal DECTA, were included. The 0.625 mm slice thickness images at 40 keV were reconstructed using high-strength DLIR (40 keV-DL-0.6 mm) in the study group. The 0.625 mm images at 40 keV and 5 mm images at 68 keV, reconstructed using the adaptive statistical iterative reconstruction-V (ASIR-V) with a strength of 50% (40 keV-AV-0.6 mm,68 keV-AV-5 mm, respectively), served as the control group. Objective measurements included the contrast-to-noise ratio (CNR) and edge-rise slope (ERS) of the aorta, and magnitude of noise power spectrum (NPS) of the liver. Subjective image quality was assessed using a 5-point scale to evaluate overall image noise, image contrast, and the visualization of large and small arteries. The IDRFs were also evaluated across all images. In general, the 0.625-mm images had higher spatial resolution and more confident IDRF assessment compared to the 5-mm images. The 40 keV-DL-0.6-mm images demonstrated the highest CNR and ERS of large vessels, and the best visualization of small arteries among the three image groups (all <i>p</i> &lt; 0.05). Subjective assessments revealed that only the 40 keV-DL-0.6 mm images met diagnostic requirements for overall noise, image contrast, large artery, and small artery visualization simultaneously. DLIR-H significantly improves the image quality of the thin-slice and low-keV images in DECTA for pediatric NB patients, enabling improved visualization of small arteries and more accurate assessment of vascular involvement IDRFs in NB.</p>\u0000 </section>\u0000 </div>","PeriodicalId":14027,"journal":{"name":"International Journal of Imaging Systems and Technology","volume":"35 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144612001","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}
引用次数: 0
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
相关产品
×
本文献相关产品
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信