Journal of X-Ray Science and Technology - Volume 32, issue 4

Authors: Hussain, Dildar | Al-masni, Mohammed A. | Aslam, Muhammad | Sadeghi-Niaraki, Abolghasem | Hussain, Jamil | Gu, Yeong Hyeon | Naqvi, Rizwan Ali

Article Type: Review Article

Abstract: BACKGROUND: The emergence of deep learning (DL) techniques has revolutionized tumor detection and classification in medical imaging, with multimodal medical imaging (MMI) gaining recognition for its precision in diagnosis, treatment, and progression tracking. OBJECTIVE: This review comprehensively examines DL methods in transforming tumor detection and classification across MMI modalities, aiming to provide insights into advancements, limitations, and key challenges for further progress. METHODS: Systematic literature analysis identifies DL studies for tumor detection and classification, outlining methodologies including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and their variants. Integration of multimodality imaging enhances …accuracy and robustness. RESULTS: Recent advancements in DL-based MMI evaluation methods are surveyed, focusing on tumor detection and classification tasks. Various DL approaches, including CNNs, YOLO, Siamese Networks, Fusion-Based Models, Attention-Based Models, and Generative Adversarial Networks, are discussed with emphasis on PET-MRI, PET-CT, and SPECT-CT. FUTURE DIRECTIONS: The review outlines emerging trends and future directions in DL-based tumor analysis, aiming to guide researchers and clinicians toward more effective diagnosis and prognosis. Continued innovation and collaboration are stressed in this rapidly evolving domain. CONCLUSION: Conclusions drawn from literature analysis underscore the efficacy of DL approaches in tumor detection and classification, highlighting their potential to address challenges in MMI analysis and their implications for clinical practice. Show more

Keywords: Multimodal medical image, deep learning, MRI, CT, PET, fusion, segmentation, image analysis, deep learning, GANs

DOI: 10.3233/XST-230429

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 857-911, 2024

Authors: Vaikunta Pai, T. | Maithili, K. | Arun Kumar, Ravula | Nagaraju, D. | Anuradha, D. | Kumar, Shailendra | Ravuri, Ananda | Sunilkumar Reddy, T. | Sivaram, M. | Vidhya, R.G.

Article Type: Research Article

Abstract: BACKGROUND: An efficient deep convolutional neural network (DeepCNN) is proposed in this article for the classification of Covid-19 disease. OBJECTIVE: A novel structure known as the Pointwise-Temporal-pointwise convolution unit is developed incorporated with the varying kernel-based depth wise temporal convolution before and after the pointwise convolution operations. METHODS: The outcome is optimized by the Slap Swarm algorithm (SSA). The proposed Deep CNN is composed of depth wise temporal convolution and end-to-end automatic detection of disease. First, the datasets SARS-COV-2 Ct-Scan Dataset and CT scan COVID Prediction dataset are preprocessed using the min-max approach and the features …are extracted for further processing. RESULTS: The experimental analysis is conducted between the proposed and some state-of-art works and stated that the proposed work effectively classifies the disease than the other approaches. CONCLUSION: The proposed structural unit is used to design the deep CNN with the increasing kernel sizes. The classification process is improved by the inclusion of depth wise temporal convolutions along with the kernel variation. The computational complexity is reduced by the introduction of stride convolutions are used in the residual linkage among the adjacent structural units. Show more

Keywords: Deep CNN, temporal, structural unit, depth wise temporal, pointwise

DOI: 10.3233/XST-230424

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 913-930, 2024

Authors: Jiang, Zhongchuan | Wu, Yun | Huang, Lei | Gu, Maohua

Article Type: Research Article

Abstract: BACKGROUND: The rapid development of deep learning techniques has greatly improved the performance of medical image segmentation, and medical image segmentation networks based on convolutional neural networks and Transformer have been widely used in this field. However, due to the limitation of the restricted receptive field of convolutional operation and the lack of local fine information extraction ability of the self-attention mechanism in Transformer, the current neural networks with pure convolutional or Transformer structure as the backbone still perform poorly in medical image segmentation. METHODS: In this paper, we propose FDB-Net (Fusion Double Branch Network, FDB-Net), a double …branch medical image segmentation network combining CNN and Transformer, by using a CNN containing g n Conv blocks and a Transformer containing Varied-Size Window Attention (VWA) blocks as the feature extraction backbone network, the dual-path encoder ensures that the network has a global receptive field as well as access to the target local detail features. We also propose a new feature fusion module (Deep Feature Fusion, DFF), which helps the image to simultaneously fuse features from two different structural encoders during the encoding process, ensuring the effective fusion of global and local information of the image. CONCLUSION: Our model achieves advanced results in all three typical tasks of medical image segmentation, which fully validates the effectiveness of FDB-Net. Show more

Keywords: Medical image processing, dual-branch network, convolutional neural networks, transformer, feature fusion

DOI: 10.3233/XST-230413

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 931-951, 2024

Authors: Ma, Yuqi | He, Jingliu | Tan, Duo | Han, Xu | Feng, Ruiqi | Xiong, Hailing | Peng, Xihua | Pu, Xun | Zhang, Lin | Li, Yongmei | Chen, Shanxiong

Article Type: Research Article

Abstract: BACKGROUND: The Chinese population ranks among the highest globally in terms of stroke prevalence. In the clinical diagnostic process, radiologists utilize computed tomography angiography (CTA) images for diagnosis, enabling a precise assessment of collateral circulation in the brains of stroke patients. Recent studies frequently combine imaging and machine learning methods to develop computer-aided diagnostic algorithms. However, in studies concerning collateral circulation assessment, the extracted imaging features are primarily composed of manually designed statistical features, which exhibit significant limitations in their representational capacity. Accurately assessing collateral circulation using image features in brain CTA images still presents challenges. METHODS: To …tackle this issue, considering the scarcity of publicly accessible medical datasets, we combined clinical data with imaging data to establish a dataset named RadiomicsClinicCTA. Moreover, we devised two collateral circulation assessment models to exploit the synergistic potential of patients’ clinical information and imaging data for a more accurate assessment of collateral circulation: data-level fusion and feature-level fusion. To remove redundant features from the dataset, we employed Levene’s test and T -test methods for feature pre-screening. Subsequently, we performed feature dimensionality reduction using the LASSO and random forest algorithms and trained classification models with various machine learning algorithms on the data-level fusion dataset after feature engineering. RESULTS: Experimental results on the RadiomicsClinicCTA dataset demonstrate that the optimized data-level fusion model achieves an accuracy and AUC value exceeding 86%. Subsequently, we trained and assessed the performance of the feature-level fusion classification model. The results indicate the feature-level fusion classification model outperforms the optimized data-level fusion model. Comparative experiments show that the fused dataset better differentiates between good and bad side branch features relative to the pure radiomics dataset. CONCLUSIONS: Our study underscores the efficacy of integrating clinical and imaging data through fusion models, significantly enhancing the accuracy of collateral circulation assessment in stroke patients. Show more

Keywords: Radiomics, computer-aided diagnosis, fusion model, collateral circulation assessment, machine learning

DOI: 10.3233/XST-240083

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 953-971, 2024

Authors: Xu, Bu | Yang, Jinzhong | Hong, Peng | Fan, Xiaoxue | Sun, Yu | Zhang, Libo | Yang, Benqiang | Xu, Lisheng | Avolio, Alberto

Article Type: Research Article

Abstract: BACKGROUND: Coronary artery segmentation is a prerequisite in computer-aided diagnosis of Coronary Artery Disease (CAD). However, segmentation of coronary arteries in Coronary Computed Tomography Angiography (CCTA) images faces several challenges. The current segmentation approaches are unable to effectively address these challenges and existing problems such as the need for manual interaction or low segmentation accuracy. OBJECTIVE: A Multi-scale Feature Learning and Rectification (MFLR) network is proposed to tackle the challenges and achieve automatic and accurate segmentation of coronary arteries. METHODS: The MFLR network introduces a multi-scale feature extraction module in the encoder to effectively capture contextual …information under different receptive fields. In the decoder, a feature correction and fusion module is proposed, which employs high-level features containing multi-scale information to correct and guide low-level features, achieving fusion between the two-level features to further improve segmentation performance. RESULTS: The MFLR network achieved the best performance on the dice similarity coefficient, Jaccard index, Recall, F1-score, and 95% Hausdorff distance, for both in-house and public datasets. CONCLUSION: Experimental results demonstrate the superiority and good generalization ability of the MFLR approach. This study contributes to the accurate diagnosis and treatment of CAD, and it also informs other segmentation applications in medicine. Show more

Keywords: Coronary artery segmentation, CCTA, multi-scale feature, feature fusion, feature correction

DOI: 10.3233/XST-240093

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 973-991, 2024

Authors: Zhao, Chen | Song, Jianping | Yuan, Yifan | Chu, Ying-Hua | Hsu, Yi-Cheng | Huang, Qiu

Article Type: Research Article

Abstract: BACKGROUND: Accurate volumetric segmentation of primary central nervous system lymphoma (PCNSL) is essential for assessing and monitoring the tumor before radiotherapy and the treatment planning. The tedious manual segmentation leads to interindividual and intraindividual differences, while existing automatic segmentation methods cause under-segmentation of PCNSL due to the complex and multifaceted nature of the tumor. OBJECTIVE: To address the challenges of small size, diffused distribution, poor inter-layer continuity on the same axis, and tendency for over-segmentation in brain MRI PCNSL segmentation, we propose an improved attention module based on nnUNet for automated segmentation. METHODS: …We collected 114 T1 MRI images of patients in the Huashan Hospital, Shanghai. Then randomly split the total of 114 cases into 5 distinct training and test sets for a 5-fold cross-validation. To efficiently and accurately delineate the PCNSL, we proposed an improved attention module based on nnU-Net with 3D convolutions, batch normalization, and residual attention (res-attention) to learn the tumor region information. Additionally, multi-scale dilated convolution kernels with different dilation rates were integrated to broaden the receptive field. We further used attentional feature fusion with 3D convolutions (AFF3D) to fuse the feature maps generated by multi-scale dilated convolution kernels to reduce under-segmentation. RESULTS: Compared to existing methods, our attention module improves the ability to distinguish diffuse and edge enhanced types of tumors; and the broadened receptive field captures tumor features of various scales and shapes more effectively, achieving a 0.9349 Dice Similarity Coefficient (DSC). CONCLUSIONS: Quantitative results demonstrate the effectiveness of the proposed method in segmenting the PCNSL. To our knowledge, this is the first study to introduce attention modules into deep learning for segmenting PCNSL based on brain magnetic resonance imaging (MRI), promoting the localization of PCNSL before radiotherapy. Show more

Keywords: Primary central nervous system lymphoma (PCNSL), convolutional neural network (CNN), attention, magnetic resonance imaging (MRI), segmentation

DOI: 10.3233/XST-240016

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 993-1009, 2024

Authors: Vishnu Priyan, S. | Vinod Kumar, R. | Moorthy, C. | Nishok, V.S.

Article Type: Research Article

Abstract: Retinal disorders pose a serious threat to world healthcare because they frequently result in visual loss or impairment. For retinal disorders to be diagnosed precisely, treated individually, and detected early, deep learning is a necessary subset of artificial intelligence. This paper provides a complete approach to improve the accuracy and reliability of retinal disease identification using images from OCT (Retinal Optical Coherence Tomography). The Hybrid Model GIGT, which combines Generative Adversarial Networks (GANs), Inception, and Game Theory, is a novel method for diagnosing retinal diseases using OCT pictures. This technique, which is carried out in Python, includes preprocessing images, feature …extraction, GAN classification, and a game-theoretic examination. Resizing, grayscale conversion, noise reduction using Gaussian filters, contrast enhancement using Contrast Limiting Adaptive Histogram Equalization (CLAHE), and edge recognition via the Canny technique are all part of the picture preparation step. These procedures set up the OCT pictures for efficient analysis. The Inception model is used for feature extraction, which enables the extraction of discriminative characteristics from the previously processed pictures. GANs are used for classification, which improves accuracy and resilience by adding a strategic and dynamic aspect to the diagnostic process. Additionally, a game-theoretic analysis is utilized to evaluate the security and dependability of the model in the face of hostile attacks. Strategic analysis and deep learning work together to provide a potent diagnostic tool. This suggested model’s remarkable 98.2% accuracy rate shows how this method has the potential to improve the detection of retinal diseases, improve patient outcomes, and address the worldwide issue of visual impairment. Show more

Keywords: Retinal disease detection, optical coherence tomography, hybrid GIGT model, Generative Adversarial Networks (GANs), inception, game theory, contrast limited adaptive histogram equalization, canny edge detection

DOI: 10.3233/XST-240027

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1011-1039, 2024

Authors: Udayakumar, P. | Subhashini, R.

Article Type: Research Article

Abstract: BACKGROUND: Connectome is understanding the complex organization of the human brain’s structural and functional connectivity is essential for gaining insights into cognitive processes and disorders. OBJECTIVE: To improve the prediction accuracy of brain disorder issues, the current study investigates dysconnected subnetworks and graph structures associated with schizophrenia. METHOD: By using the proposed structural connectivity-deep graph neural network (sc-DGNN) model and compared with machine learning (ML) and deep learning (DL) models.This work attempts to focus on eighty-eight subjects of diffusion magnetic resonance imaging (dMRI), three classical ML, and five DL models. RESULT: The structural connectivity-deep …graph neural network (sc-DGNN) model is proposed to effectively predict dysconnectedness associated with schizophrenia and exhibits superior performance compared to traditional ML and DL (GNNs) methods in terms of accuracy, sensitivity, specificity, precision, F1-score, and Area under receiver operating characteristic (AUC). CONCLUSION: The classification task on schizophrenia using structural connectivity matrices and experimental results showed that linear discriminant analysis (LDA) performed 72% accuracy rate in ML models and sc-DGNN performed at a 93% accuracy rate in DL models to distinguish between schizophrenia and healthy patients. Show more

Keywords: Connectome, brain disorder, neural network, graph measure, connectivity matrices, neuroimaging, tau protein

DOI: 10.3233/XST-230426

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1041-1059, 2024

Authors: Zhao, Wei | Liu, Yi | Linghu, Xinyao | Zhang, Pengcheng | Yan, Hongxu | Ding, Xiaxu | Wang, Xiang | Gui, Zhiguo | Chen, Yan

Article Type: Research Article

Abstract: BACKGROUND: Recently, X-rays have been widely used to detect complex structural workpieces. Due to the uneven thickness of the workpiece and the high dynamic range of the X-ray image itself, the detailed internal structure of the workpiece cannot be clearly displayed. OBJECTIVE: To solve this problem, we propose an image enhancement algorithm based on a multi-scale local edge-preserving filter. METHODS: Firstly, the global brightness of the image is enhanced through logarithmic transformation. Then, to enhance the local contrast, we propose utilizing the gradient decay function based on fuzzy entropy to process the gradient and then incorporate …the gradient into the energy function of the local edge-preserving filter (LEP) as a constraint term. Finally, multiple base layers and detail layers are obtained through filtering multi-scale decomposition. All detail layers are enhanced and fused using S-curve mapping to improve contrast further. RESULTS: This method is competitive in both quantitative indices and visual perception quality. CONCLUSIONS: The experimental results demonstrate that the proposed method significantly enhances various complex workpieces and is highly efficient. Show more

Keywords: X-ray images, local edge-preserving filter, local fuzzy entropy, gradient domain compression, S-curve mapping

DOI: 10.3233/XST-240045

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1061-1077, 2024

Authors: Shi, Liu | Wei, Cunfeng | Jia, Tong | Zhao, Yunsong | Liu, Baodong

Article Type: Research Article

Abstract: BACKGROUND: The rapid development of industrialization in printed circuit board (PCB) warrants more complexity and integrity, which entails an essential procedure of PCB inspection. X-ray computed laminography (CL) enables inspection of arbitrary regions for large-sized flat objects with high resolution. PCB inspection based on CL imaging is worthy of exploration. OBJECTIVE: This work aims to extract PCB circuit layer information based on CL imaging through image segmentation technique. METHODS: In this work, an effective and applicable segmentation model for PCB CL images is established for the first time. The model comprises two components, …with one integrating edge diffusion and l 0 smoothing to filter CL images with aliasing artifacts, and the other being the fuzzy energy-based active contour model driven by local pre-fitting energy to segment the filtered images. RESULT: The proposed model is able to suppress aliasing artifacts in the PCB CL images and has good performance on images of different circuit layers. CONCLUSIONS: Results of the simulation experiment reveal that the method is capable of accurate segmentation under ideal scanning condition. Testing of different PCBs and comparison of different segmentation methods authenticate the applicability and superiority of the model. Show more

Keywords: PCB, CL, image segmentation, active contour model

DOI: 10.3233/XST-240006

Citation: Journal of X-Ray Science and Technology, vol. 32, no. 4, pp. 1079-1098, 2024