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Journals Computerized Medical Imaging a...

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society

https://read.qxmd.com/read/38759471/deep-neural-network-for-the-prediction-of-kras-nras-and-braf-genotypes-in-left-sided-colorectal-cancer-based-on-histopathologic-images
#1
JOURNAL ARTICLE
Xuejie Li, Xianda Chi, Pinjie Huang, Qiong Liang, Jianpei Liu
BACKGROUND: The KRAS, NRAS, and BRAF genotypes are critical for selecting targeted therapies for patients with metastatic colorectal cancer (mCRC). Here, we aimed to develop a deep learning model that utilizes pathologic whole-slide images (WSIs) to accurately predict the status of KRAS, NRAS, and BRAFV600E . METHODS: 129 patients with left-sided colon cancer and rectal cancer from the Third Affiliated Hospital of Sun Yat-sen University were assigned to the training and testing cohorts...
May 12, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38744197/gnn-based-structural-information-to-improve-dnn-based-basal-ganglia-segmentation-in-children-following-early-brain-lesion
#2
JOURNAL ARTICLE
Patty Coupeau, Jean-Baptiste Fasquel, Lucie Hertz-Pannier, Mickaël Dinomais
Analyzing the basal ganglia following an early brain lesion is crucial due to their noteworthy role in sensory-motor functions. However, the segmentation of these subcortical structures on MRI is challenging in children and is further complicated by the presence of a lesion. Although current deep neural networks (DNN) perform well in segmenting subcortical brain structures in healthy brains, they lack robustness when faced with lesion variability, leading to structural inconsistencies. Given the established spatial organization of the basal ganglia, we propose enhancing the DNN-based segmentation through post-processing with a graph neural network (GNN)...
May 7, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38735104/unsupervised-lung-ct-image-registration-via-stochastic-decomposition-of-deformation-fields
#3
JOURNAL ARTICLE
Jing Zou, Youyi Song, Lihao Liu, Angelica I Aviles-Rivero, Jing Qin
We address the problem of lung CT image registration, which underpins various diagnoses and treatments for lung diseases. The main crux of the problem is the large deformation that the lungs undergo during respiration. This physiological process imposes several challenges from a learning point of view. In this paper, we propose a novel training scheme, called stochastic decomposition, which enables deep networks to effectively learn such a difficult deformation field during lung CT image registration. The key idea is to stochastically decompose the deformation field, and supervise the registration by synthetic data that have the corresponding appearance discrepancy...
May 7, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38729092/weakly-supervised-preclinical-tumor-localization-associated-with-survival-prediction-from-lung-cancer-screening-chest-x-ray-images
#4
JOURNAL ARTICLE
Renato Hermoza, Jacinto C Nascimento, Gustavo Carneiro
In this paper, we hypothesize that it is possible to localize image regions of preclinical tumors in a Chest X-ray (CXR) image by a weakly-supervised training of a survival prediction model using a dataset containing CXR images of healthy patients and their time-to-death label. These visual explanations can empower clinicians in early lung cancer detection and increase patient awareness of their susceptibility to the disease. To test this hypothesis, we train a censor-aware multi-class survival prediction deep learning classifier that is robust to imbalanced training, where classes represent quantized number of days for time-to-death prediction...
May 7, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38718561/towards-a-unified-approach-for-unsupervised-brain-mri-motion-artefact-detection-with-few-shot-anomaly-detection
#5
JOURNAL ARTICLE
Niamh Belton, Misgina Tsighe Hagos, Aonghus Lawlor, Kathleen M Curran
Automated Motion Artefact Detection (MAD) in Magnetic Resonance Imaging (MRI) is a field of study that aims to automatically flag motion artefacts in order to prevent the requirement for a repeat scan. In this paper, we identify and tackle the three current challenges in the field of automated MAD; (1) reliance on fully-supervised training, meaning they require specific examples of Motion Artefacts (MA), (2) inconsistent use of benchmark datasets across different works and use of private datasets for testing and training of newly proposed MAD techniques and (3) a lack of sufficiently large datasets for MRI MAD...
May 3, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38714019/3dfrinet-a-framework-for-the-detection-and-diagnosis-of-fracture-related-infection-in-low-extremities-based-on-18-f-fdg-pet-ct-3d-images
#6
JOURNAL ARTICLE
Chengfan Li, Liangbing Nie, Zhenkui Sun, Xuehai Ding, Quanyong Luo, Chentian Shen
Fracture related infection (FRI) is one of the most devastating complications after fracture surgery in the lower extremities, which can lead to extremely high morbidity and medical costs. Therefore, early comprehensive evaluation and accurate diagnosis of patients are critical for appropriate treatment, prevention of complications, and good prognosis. 18 Fluoro-deoxyglucose positron emission tomography/computed tomography (18 F-FDG PET/CT) is one of the most commonly used medical imaging modalities for diagnosing FRI...
May 3, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38714018/leveraging-a-realistic-synthetic-database-to-learn-shape-from-shading-for-estimating-the-colon-depth-in-colonoscopy-images
#7
JOURNAL ARTICLE
Josué Ruano, Martín Gómez, Eduardo Romero, Antoine Manzanera
Colonoscopy is the choice procedure to diagnose, screening, and treat the colon and rectum cancer, from early detection of small precancerous lesions (polyps), to confirmation of malign masses. However, the high variability of the organ appearance and the complex shape of both the colon wall and structures of interest make this exploration difficult. Learned visuospatial and perceptual abilities mitigate technical limitations in clinical practice by proper estimation of the intestinal depth. This work introduces a novel methodology to estimate colon depth maps in single frames from monocular colonoscopy videos...
May 3, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38714020/cave-cerebral-artery-vein-segmentation-in-digital-subtraction-angiography
#8
JOURNAL ARTICLE
Ruisheng Su, P Matthijs van der Sluijs, Yuan Chen, Sandra Cornelissen, Ruben van den Broek, Wim H van Zwam, Aad van der Lugt, Wiro J Niessen, Danny Ruijters, Theo van Walsum
Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery-vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts...
May 1, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38704993/cross-modality-cerebrovascular-segmentation-based-on-pseudo-label-generation-via-paired-data
#9
JOURNAL ARTICLE
Zhanqiang Guo, Jianjiang Feng, Wangsheng Lu, Yin Yin, Guangming Yang, Jie Zhou
Accurate segmentation of cerebrovascular structures from Computed Tomography Angiography (CTA), Magnetic Resonance Angiography (MRA), and Digital Subtraction Angiography (DSA) is crucial for clinical diagnosis of cranial vascular diseases. Recent advancements in deep Convolution Neural Network (CNN) have significantly improved the segmentation process. However, training segmentation networks for all modalities requires extensive data labeling for each modality, which is often expensive and time-consuming. To circumvent this limitation, we introduce an approach to train cross-modality cerebrovascular segmentation network based on paired data from source and target domains...
May 1, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38692199/motion-correction-and-super-resolution-for-multi-slice-cardiac-magnetic-resonance-imaging-via-an-end-to-end-deep-learning-approach
#10
JOURNAL ARTICLE
Zhennong Chen, Hui Ren, Quanzheng Li, Xiang Li
Accurate reconstruction of a high-resolution 3D volume of the heart is critical for comprehensive cardiac assessments. However, cardiac magnetic resonance (CMR) data is usually acquired as a stack of 2D short-axis (SAX) slices, which suffers from the inter-slice misalignment due to cardiac motion and data sparsity from large gaps between SAX slices. Therefore, we aim to propose an end-to-end deep learning (DL) model to address these two challenges simultaneously, employing specific model components for each challenge...
April 29, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38692200/a-deep-learning-based-pipeline-for-developing-multi-rib-shape-generative-model-with-populational-percentiles-or-anthropometrics-as-predictors
#11
JOURNAL ARTICLE
Yuan Huang, Sven A Holcombe, Stewart C Wang, Jisi Tang
Rib cross-sectional shapes (characterized by the outer contour and cortical bone thickness) affect the rib mechanical response under impact loading, thereby influence the rib injury pattern and risk. A statistical description of the rib shapes or their correlations to anthropometrics is a prerequisite to the development of numerical human body models representing target demographics. Variational autoencoders (VAE) as anatomical shape generators remain to be explored in terms of utilizing the latent vectors to control or interpret the representativeness of the generated results...
April 25, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38703602/w-drag-a-joint-framework-of-wgan-with-data-random-augmentation-optimized-for-generative-networks-for-bone-marrow-edema-detection-in-dual-energy-ct
#12
JOURNAL ARTICLE
Chunsu Park, Jeong-Woon Kang, Doen-Eon Lee, Wookon Son, Sang-Min Lee, Chankue Park, MinWoo Kim
Dual-energy computed tomography (CT) is an excellent substitute for identifying bone marrow edema in magnetic resonance imaging. However, it is rarely used in practice owing to its low contrast. To overcome this problem, we constructed a framework based on deep learning techniques to screen for diseases using axial bone images and to identify the local positions of bone lesions. To address the limited availability of labeled samples, we developed a new generative adversarial network (GAN) that extends expressions beyond conventional augmentation (CA) methods based on geometric transformations...
April 24, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38718562/advancing-post-traumatic-seizure-classification-and-biomarker-identification-information-decomposition-based-multimodal-fusion-and-explainable-machine-learning-with-missing-neuroimaging-data
#13
JOURNAL ARTICLE
Md Navid Akbar, Sebastian F Ruf, Ashutosh Singh, Razieh Faghihpirayesh, Rachael Garner, Alexis Bennett, Celina Alba, Marianna La Rocca, Tales Imbiriba, Deniz Erdoğmuş, Dominique Duncan
A late post-traumatic seizure (LPTS), a consequence of traumatic brain injury (TBI), can potentially evolve into a lifelong condition known as post-traumatic epilepsy (PTE). Presently, the mechanism that triggers epileptogenesis in TBI patients remains elusive, inspiring the epilepsy community to devise ways to predict which TBI patients will develop PTE and to identify potential biomarkers. In response to this need, our study collected comprehensive, longitudinal multimodal data from 48 TBI patients across multiple participating institutions...
April 19, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38663077/a-novel-approach-for-estimating-lung-tumor-motion-based-on-dynamic-features-in-4d-ct
#14
JOURNAL ARTICLE
Ye-Jun Gong, Yue-Ke Li, Rongrong Zhou, Zhan Liang, Yingying Zhang, Tingting Cheng, Zi-Jian Zhang
Due to the high expenses involved, 4D-CT data for certain patients may only include five respiratory phases (0%, 20%, 40%, 60%, and 80%). This limitation can affect the subsequent planning of radiotherapy due to the absence of lung tumor information for the remaining five respiratory phases (10%, 30%, 50%, 70%, and 90%). This study aims to develop an interpolation method that can automatically derive tumor boundary contours for the five omitted phases using the available 5-phase 4D-CT data. The dynamic mode decomposition (DMD) method is a data-driven and model-free technique that can extract dynamic information from high-dimensional data...
April 18, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38643551/hybrid-dual-mean-teacher-network-with-double-uncertainty-guidance-for-semi-supervised-segmentation-of-magnetic-resonance-images
#15
JOURNAL ARTICLE
Jiayi Zhu, Bart Bolsterlee, Brian V Y Chow, Yang Song, Erik Meijering
Semi-supervised learning has made significant progress in medical image segmentation. However, existing methods primarily utilize information from a single dimensionality, resulting in sub-optimal performance on challenging magnetic resonance imaging (MRI) data with multiple segmentation objects and anisotropic resolution. To address this issue, we present a Hybrid Dual Mean-Teacher (HD-Teacher) model with hybrid, semi-supervised, and multi-task learning to achieve effective semi-supervised segmentation. HD-Teacher employs a 2D and a 3D mean-teacher network to produce segmentation labels and signed distance fields from the hybrid information captured in both dimensionalities...
April 17, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38640621/global-contextual-representation-via-graph-transformer-fusion-for-hepatocellular-carcinoma-prognosis-in-whole-slide-images
#16
JOURNAL ARTICLE
Luyu Tang, Songhui Diao, Chao Li, Miaoxia He, Kun Ru, Wenjian Qin
Current methods of digital pathological images typically employ small image patches to learn local representative features to overcome the issues of computationally heavy and memory limitations. However, the global contextual features are not fully considered in whole-slide images (WSIs). Here, we designed a hybrid model that utilizes Graph Neural Network (GNN) module and Transformer module for the representation of global contextual features, called TransGNN. GNN module built a WSI-Graph for the foreground area of a WSI for explicitly capturing structural features, and the Transformer module through the self-attention mechanism implicitly learned the global context information...
April 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38640619/cardsegnet-an-adaptive-hybrid-cnn-vision-transformer-model-for-heart-region-segmentation-in-cardiac-mri
#17
JOURNAL ARTICLE
Hamed Aghapanah, Reza Rasti, Saeed Kermani, Faezeh Tabesh, Hossein Yousefi Banaem, Hamidreza Pour Aliakbar, Hamid Sanei, William Paul Segars
Cardiovascular MRI (CMRI) is a non-invasive imaging technique adopted for assessing the blood circulatory system's structure and function. Precise image segmentation is required to measure cardiac parameters and diagnose abnormalities through CMRI data. Because of anatomical heterogeneity and image variations, cardiac image segmentation is a challenging task. Quantification of cardiac parameters requires high-performance segmentation of the left ventricle (LV), right ventricle (RV), and left ventricle myocardium from the background...
April 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38640620/distraction-aware-hierarchical-learning-for-vascular-structure-segmentation-in-intravascular-ultrasound-images
#18
JOURNAL ARTICLE
Wenhao Zhong, Heye Zhang, Zhifan Gao, William Kongto Hau, Guang Yang, Xiujian Liu, Lin Xu
Vascular structure segmentation in intravascular ultrasound (IVUS) images plays an important role in pre-procedural evaluation of percutaneous coronary intervention (PCI). However, vascular structure segmentation in IVUS images has the challenge of structure-dependent distractions. Structure-dependent distractions are categorized into two cases, structural intrinsic distractions and inter-structural distractions. Traditional machine learning methods often rely solely on low-level features, overlooking high-level features...
April 12, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38626631/sub-features-orthogonal-decoupling-detecting-bone-wall-absence-via-a-small-number-of-abnormal-examples-for-temporal-ct-images
#19
JOURNAL ARTICLE
Xiaoguang Li, Yichao Zhou, Hongxia Yin, Pengfei Zhao, Ruowei Tang, Han Lv, Yating Qin, Li Zhuo, Zhenchang Wang
The absence of bone wall located in the jugular bulb and sigmoid sinus of the temporal bone is one of the important reasons for pulsatile tinnitus. Automatic and accurate detection of these abnormal singes in CT slices has important theoretical significance and clinical value. Due to the shortage of abnormal samples, imbalanced samples, small inter-class differences, and low interpretability, existing deep-learning methods are greatly challenged. In this paper, we proposed a sub-features orthogonal decoupling model, which can effectively disentangle the representation features into class-specific sub-features and class-independent sub-features in a latent space...
April 12, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38608333/semantically-redundant-training-data-removal-and-deep-model-classification-performance-a-study-with-chest-x-rays
#20
JOURNAL ARTICLE
Sivaramakrishnan Rajaraman, Ghada Zamzmi, Feng Yang, Zhaohui Liang, Zhiyun Xue, Sameer Antani
Deep learning (DL) has demonstrated its innate capacity to independently learn hierarchical features from complex and multi-dimensional data. A common understanding is that its performance scales up with the amount of training data. However, the data must also exhibit variety to enable improved learning. In medical imaging data, semantic redundancy, which is the presence of similar or repetitive information, can occur due to the presence of multiple images that have highly similar presentations for the disease of interest...
April 9, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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