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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/38537536/annotation-free-prediction-of-treatment-specific-tissue-outcome-from-4d-ct-perfusion-imaging-in-acute-ischemic-stroke
#1
JOURNAL ARTICLE
Alejandro Gutierrez, Kimberly Amador, Anthony Winder, Matthias Wilms, Jens Fiehler, Nils D Forkert
Acute ischemic stroke is a critical health condition that requires timely intervention. Following admission, clinicians typically use perfusion imaging to facilitate treatment decision-making. While deep learning models leveraging perfusion data have demonstrated the ability to predict post-treatment tissue infarction for individual patients, predictions are often represented as binary or probabilistic masks that are not straightforward to interpret or easy to obtain. Moreover, these models typically rely on large amounts of subjectively segmented data and non-standard perfusion analysis techniques...
March 23, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38522222/a-novel-center-based-deep-contrastive-metric-learning-method-for-the-detection-of-polymicrogyria-in-pediatric-brain-mri
#2
JOURNAL ARTICLE
Lingfeng Zhang, Nishard Abdeen, Jochen Lang
Polymicrogyria (PMG) is a disorder of cortical organization mainly seen in children, which can be associated with seizures, developmental delay and motor weakness. PMG is typically diagnosed on magnetic resonance imaging (MRI) but some cases can be challenging to detect even for experienced radiologists. In this study, we create an open pediatric MRI dataset (PPMR) containing both PMG and control cases from the Children's Hospital of Eastern Ontario (CHEO), Ottawa, Canada. The differences between PMG and control MRIs are subtle and the true distribution of the features of the disease is unknown...
March 21, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38518412/multi-modality-fusion-transformer-with-spatio-temporal-feature-aggregation-module-for-psychiatric-disorder-diagnosis
#3
JOURNAL ARTICLE
Guoxin Wang, Fengmei Fan, Sheng Shi, Shan An, Xuyang Cao, Wenshu Ge, Feng Yu, Qi Wang, Xiaole Han, Shuping Tan, Yunlong Tan, Zhiren Wang
Bipolar disorder (BD) is characterized by recurrent episodes of depression and mild mania. In this paper, to address the common issue of insufficient accuracy in existing methods and meet the requirements of clinical diagnosis, we propose a framework called Spatio-temporal Feature Fusion Transformer (STF2Former). It improves on our previous work - MFFormer by introducing a Spatio-temporal Feature Aggregation Module (STFAM) to learn the temporal and spatial features of rs-fMRI data. It promotes intra-modality attention and information fusion across different modalities...
March 19, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38522221/sstu-swin-spectral-transformer-u-net-for-hyperspectral-whole-slide-image-reconstruction
#4
JOURNAL ARTICLE
Yukun Wang, Yanfeng Gu, Abiyasi Nanding
Whole Slide Imaging and Hyperspectral Microscopic Imaging provide great quality data with high spatial and spectral resolution for histopathology. Existing Hyperspectral Whole Slide Imaging systems combine the advantages of the techniques above, thus providing rich information for pathological diagnosis. However, it cannot avoid the problems of slow acquisition speed and mass data storage demand. Inspired by the spectral reconstruction task in computer vision and remote sensing, the Swin-Spectral Transformer U-Net (SSTU) has been developed to reconstruct Hyperspectral Whole Slide images (HWSis) from multiple Hyperspectral Microscopic images (HMis) of small Field of View and Whole Slide images (WSis)...
March 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38518411/improving-vessel-segmentation-with-multi-task-learning-and-auxiliary-data-available-only-during-model-training
#5
JOURNAL ARTICLE
Daniel Sobotka, Alexander Herold, Matthias Perkonigg, Lucian Beer, Nina Bastati, Alina Sablatnig, Ahmed Ba-Ssalamah, Georg Langs
Liver vessel segmentation in magnetic resonance imaging data is important for the computational analysis of vascular remodeling, associated with a wide spectrum of diffuse liver diseases. Existing approaches rely on contrast enhanced imaging data, but the necessary dedicated imaging sequences are not uniformly acquired. Images without contrast enhancement are acquired more frequently, but vessel segmentation is challenging, and requires large-scale annotated data. We propose a multi-task learning framework to segment vessels in liver MRI without contrast...
March 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38513397/a-semi-supervised-multiview-mri-network-for-the-detection-of-knee-osteoarthritis
#6
JOURNAL ARTICLE
Mohamed Berrimi, Didier Hans, Rachid Jennane
Knee OsteoArthritis (OA) is a prevalent chronic condition, affecting a significant proportion of the global population. Detecting knee OA is crucial as the degeneration of the knee joint is irreversible. In this paper, we introduce a semi-supervised multi-view framework and a 3D CNN model for detecting knee OA using 3D Magnetic Resonance Imaging (MRI) scans. We introduce a semi-supervised learning approach combining labeled and unlabeled data to improve the performance and generalizability of the proposed model...
March 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38513396/mef-unet-an-end-to-end-ultrasound-image-segmentation-algorithm-based-on-multi-scale-feature-extraction-and-fusion
#7
JOURNAL ARTICLE
Mengqi Xu, Qianting Ma, Huajie Zhang, Dexing Kong, Tieyong Zeng
Ultrasound image segmentation is a challenging task due to the complexity of lesion types, fuzzy boundaries, and low-contrast images along with the presence of noises and artifacts. To address these issues, we propose an end-to-end multi-scale feature extraction and fusion network (MEF-UNet) for the automatic segmentation of ultrasound images. Specifically, we first design a selective feature extraction encoder, including detail extraction stage and structure extraction stage, to precisely capture the edge details and overall shape features of the lesions...
March 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38471330/deformable-registration-of-preoperative-mr-and-intraoperative-long-length-tomosynthesis-images-for-guidance-of-spine-surgery-via-image-synthesis
#8
JOURNAL ARTICLE
Yixuan Huang, Xiaoxuan Zhang, Yicheng Hu, Ashley R Johnston, Craig K Jones, Wojciech B Zbijewski, Jeffrey H Siewerdsen, Patrick A Helm, Timothy F Witham, Ali Uneri
PURPOSE: Improved integration and use of preoperative imaging during surgery hold significant potential for enhancing treatment planning and instrument guidance through surgical navigation. Despite its prevalent use in diagnostic settings, MR imaging is rarely used for navigation in spine surgery. This study aims to leverage MR imaging for intraoperative visualization of spine anatomy, particularly in cases where CT imaging is unavailable or when minimizing radiation exposure is essential, such as in pediatric surgery...
March 6, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38471329/patchcl-ae-anomaly-detection-for-medical-images-using-patch-wise-contrastive-learning-based-auto-encoder
#9
JOURNAL ARTICLE
Shuai Lu, Weihang Zhang, Jia Guo, Hanruo Liu, Huiqi Li, Ningli Wang
Anomaly detection is an important yet challenging task in medical image analysis. Most anomaly detection methods are based on reconstruction, but the performance of reconstruction-based methods is limited due to over-reliance on pixel-level losses. To address the limitation, we propose a patch-wise contrastive learning-based auto-encoder for medical anomaly detection. The key contribution is the patch-wise contrastive learning loss that provides supervision on local semantics to enforce semantic consistency between corresponding input-output patches...
March 6, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38447381/detection-of-abdominopelvic-lymph-nodes-in-multi-parametric-mri
#10
JOURNAL ARTICLE
Tejas Sudharshan Mathai, Thomas C Shen, Daniel C Elton, Sungwon Lee, Zhiyong Lu, Ronald M Summers
Reliable localization of lymph nodes (LNs) in multi-parametric MRI (mpMRI) studies plays a major role in the assessment of lymphadenopathy and staging of metastatic disease. Radiologists routinely measure the nodal size in order to distinguish benign from malignant nodes, which require subsequent cancer staging. However, identification of lymph nodes is a cumbersome task due to their myriad appearances in mpMRI studies. Multiple sequences are acquired in mpMRI studies, including T2 fat suppressed (T2FS) and diffusion weighted imaging (DWI) sequences among others; consequently, the sizing of LNs is rendered challenging due to the variety of signal intensities in these sequences...
March 1, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38432060/multi-task-global-optimization-based-method-for-vascular-landmark-detection
#11
JOURNAL ARTICLE
Zimeng Tan, Jianjiang Feng, Wangsheng Lu, Yin Yin, Guangming Yang, Jie Zhou
Vascular landmark detection plays an important role in medical analysis and clinical treatment. However, due to the complex topology and similar local appearance around landmarks, the popular heatmap regression based methods always suffer from the landmark confusion problem. Vascular landmarks are connected by vascular segments and have special spatial correlations, which can be utilized for performance improvement. In this paper, we propose a multi-task global optimization-based framework for accurate and automatic vascular landmark detection...
March 1, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38377630/automatic-artery-vein-classification-methods-for-retinal-blood-vessel-a-review
#12
REVIEW
Qihan Chen, Jianqing Peng, Shen Zhao, Wanquan Liu
Automatic retinal arteriovenous classification can assist ophthalmologists in disease early diagnosis. Deep learning-based methods and topological graph-based methods have become the main solutions for retinal arteriovenous classification in recent years. This paper reviews the automatic retinal arteriovenous classification methods from 2003 to 2022. Firstly, we compare different methods and provide comparison tables of the summary results. Secondly, we complete the classification of the public arteriovenous classification datasets and provide the annotation development tables of different datasets...
February 16, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38364600/scaned-siamese-collateral-assessment-network-for-evaluation-of-collaterals-from-ischemic-damage
#13
JOURNAL ARTICLE
Mumu Aktar, Yiming Xiao, Ali K Z Tehrani, Donatella Tampieri, Hassan Rivaz, Marta Kersten-Oertel
This study conducts collateral evaluation from ischemic damage using a deep learning-based Siamese network, addressing the challenges associated with a small and imbalanced dataset. The collateral network provides an alternative oxygen and nutrient supply pathway in ischemic stroke cases, influencing treatment decisions. Research in this area focuses on automated collateral assessment using deep learning (DL) methods to expedite decision-making processes and enhance accuracy. Our study employed a 3D ResNet-based Siamese network, referred to as SCANED, to classify collaterals as good/intermediate or poor...
February 15, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38387114/sc-gan-structure-completion-generative-adversarial-network-for-synthetic-ct-generation-from-mr-images-with-truncated-anatomy
#14
JOURNAL ARTICLE
Xinru Chen, Yao Zhao, Laurence E Court, He Wang, Tinsu Pan, Jack Phan, Xin Wang, Yao Ding, Jinzhong Yang
Creating synthetic CT (sCT) from magnetic resonance (MR) images enables MR-based treatment planning in radiation therapy. However, the MR images used for MR-guided adaptive planning are often truncated in the boundary regions due to the limited field of view and the need for sequence optimization. Consequently, the sCT generated from these truncated MR images lacks complete anatomic information, leading to dose calculation error for MR-based adaptive planning. We propose a novel structure-completion generative adversarial network (SC-GAN) to generate sCT with full anatomic details from the truncated MR images...
February 10, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38341945/a-transformer-based-pyramid-network-for-coronary-calcified-plaque-segmentation-in-intravascular-optical-coherence-tomography-images
#15
JOURNAL ARTICLE
Yiqing Liu, Farhad R Nezami, Elazer R Edelman
Characterizing coronary calcified plaque (CCP) provides essential insight into diagnosis and treatment of atherosclerosis. Intravascular optical coherence tomography (OCT) offers significant advantages for detecting CCP and even automated segmentation with recent advances in deep learning techniques. Most of current methods have achieved promising results by adopting existing convolution neural networks (CNNs) in computer vision domain. However, their performance can be detrimentally affected by unseen plaque patterns and artifacts due to inherent limitation of CNNs in contextual reasoning...
February 9, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38340573/improving-abdominal-image-segmentation-with-overcomplete-shape-priors
#16
JOURNAL ARTICLE
Amine Sadikine, Bogdan Badic, Jean-Pierre Tasu, Vincent Noblet, Pascal Ballet, Dimitris Visvikis, Pierre-Henri Conze
The extraction of abdominal structures using deep learning has recently experienced a widespread interest in medical image analysis. Automatic abdominal organ and vessel segmentation is highly desirable to guide clinicians in computer-assisted diagnosis, therapy, or surgical planning. Despite a good ability to extract large organs, the capacity of U-Net inspired architectures to automatically delineate smaller structures remains a major issue, especially given the increase in receptive field size as we go deeper into the network...
February 9, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38368665/dynamic-recurrent-inference-machines-for-accelerated-mri-guided-radiotherapy-of-the-liver
#17
JOURNAL ARTICLE
Kai Lønning, Matthan W A Caan, Marlies E Nowee, Jan-Jakob Sonke
Recurrent inference machines (RIM), a deep learning model that learns an iterative scheme for reconstructing sparsely sampled MRI, has been shown able to perform well on accelerated 2D and 3D MRI scans, learn from small datasets and generalize well to unseen types of data. Here we propose the dynamic recurrent inference machine (DRIM) for reconstructing sparsely sampled 4D MRI by exploiting correlations between respiratory states. The DRIM was applied to a 4D protocol for MR-guided radiotherapy of liver lesions based on repetitive interleaved coronal 2D multi-slice T2 -weighted acquisitions...
February 8, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38340574/knowledge-distillation-on-individual-vertebrae-segmentation-exploiting-3d-u-net
#18
JOURNAL ARTICLE
Luís Serrador, Francesca Pia Villani, Sara Moccia, Cristina P Santos
Recent advances in medical imaging have highlighted the critical development of algorithms for individual vertebral segmentation on computed tomography (CT) scans. Essential for diagnostic accuracy and treatment planning in orthopaedics, neurosurgery and oncology, these algorithms face challenges in clinical implementation, including integration into healthcare systems. Consequently, our focus lies in exploring the application of knowledge distillation (KD) methods to train shallower networks capable of efficiently segmenting vertebrae in CT scans...
February 8, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38341946/crdet-a-circle-representation-detector-for-lung-granulomas-based-on-multi-scale-attention-features-with-center-point-calibration
#19
JOURNAL ARTICLE
Yu Jin, Juan Liu, Yuanyuan Zhou, Rong Chen, Hua Chen, Wensi Duan, Yuqi Chen, Xiao-Lian Zhang
Lung granuloma is a very common lung disease, and its specific diagnosis is important for determining the exact cause of the disease as well as the prognosis of the patient. And, an effective lung granuloma detection model based on computer-aided diagnostics (CAD) can help pathologists to localize granulomas, thereby improving the efficiency of the specific diagnosis. However, for lung granuloma detection models based on CAD, the significant size differences between granulomas and how to better utilize the morphological features of granulomas are both critical challenges to be addressed...
February 7, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
https://read.qxmd.com/read/38330635/dual-task-kidney-mr-segmentation-with-transformers-in-autosomal-dominant-polycystic-kidney-disease
#20
JOURNAL ARTICLE
Pierre-Henri Conze, Gustavo Andrade-Miranda, Yannick Le Meur, Emilie Cornec-Le Gall, François Rousseau
Autosomal-dominant polycystic kidney disease is a prevalent genetic disorder characterized by the development of renal cysts, leading to kidney enlargement and renal failure. Accurate measurement of total kidney volume through polycystic kidney segmentation is crucial to assess disease severity, predict progression and evaluate treatment effects. Traditional manual segmentation suffers from intra- and inter-expert variability, prompting the exploration of automated approaches. In recent years, convolutional neural networks have been employed for polycystic kidney segmentation from magnetic resonance images...
February 7, 2024: Computerized Medical Imaging and Graphics: the Official Journal of the Computerized Medical Imaging Society
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