Cargando…

Development and verification of radiomics framework for computed tomography image segmentation

BACKGROUND: Radiomics has been considered an imaging marker for capturing quantitative image information (QII). The introduction of radiomics to image segmentation is desirable but challenging. PURPOSE: This study aims to develop and validate a radiomics‐based framework for image segmentation (RFIS)...

Descripción completa

Detalles Bibliográficos
Autores principales:	Gu, Jiabing, Li, Baosheng, Shu, Huazhong, Zhu, Jian, Qiu, Qingtao, Bai, Tong
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	John Wiley and Sons Inc. 2022
Materias:	QUANTITATIVE IMAGING AND IMAGE PROCESSING
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9805121/ https://www.ncbi.nlm.nih.gov/pubmed/35917213 http://dx.doi.org/10.1002/mp.15904

Ejemplares similares

An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images
por: Caballo, Marco, et al.
Publicado: (2018)

Quantifying pulmonary perfusion from noncontrast computed tomography
por: Castillo, Edward, et al.
Publicado: (2021)

Morphology-guided deep learning framework for segmentation of pancreas in computed tomography images
por: Qureshi, Touseef Ahmad, et al.
Publicado: (2022)

Technical Note: Ontology‐guided radiomics analysis workflow (O‐RAW)
por: Shi, Zhenwei, et al.
Publicado: (2019)

Multi‐marker quantitative radiomics for mass characterization in dedicated breast CT imaging
por: Caballo, Marco, et al.
Publicado: (2020)

A novel real‐time computational framework for detecting catheters and rigid guidewires in cardiac catheterization procedures
por: Ma, YingLiang, et al.
Publicado: (2018)

Lung cancer diagnosis using deep attention‐based multiple instance learning and radiomics
por: Chen, Junhua, et al.
Publicado: (2022)

Differentiation between immune checkpoint inhibitor‐related and radiation pneumonitis in lung cancer by CT radiomics and machine learning
por: Cheng, Jun, et al.
Publicado: (2022)

Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain
por: Bologna, Marco, et al.
Publicado: (2019)

Stratification of pseudoprogression and true progression of glioblastoma multiform based on longitudinal diffusion tensor imaging without segmentation
por: Qian, Xiaohua, et al.
Publicado: (2016)

Repeatability of (18)F‐FDG PET radiomic features: A phantom study to explore sensitivity to image reconstruction settings, noise, and delineation method
por: Pfaehler, Elisabeth, et al.
Publicado: (2018)

Design and evaluation of a modular multimodality imaging phantom to simulate heterogeneous uptake and enhancement patterns for radiomic quantification in hybrid imaging: A feasibility study
por: Kalisvaart, Gijsbert M., et al.
Publicado: (2022)

Iodine quantification in limited angle tomography
por: Michielsen, Koen, et al.
Publicado: (2020)

Synergistic impact of motion and acquisition/reconstruction parameters on (18)F‐FDG PET radiomic features in non‐small cell lung cancer: Phantom and clinical studies
por: Hosseini, Seyyed Ali, et al.
Publicado: (2022)

Combining natural and artificial intelligence for robust automatic anatomy segmentation: Application in neck and thorax auto‐contouring
por: Udupa, Jayaram K., et al.
Publicado: (2022)

Transfer learning for data‐efficient abdominal muscle segmentation with convolutional neural networks
por: McSweeney, Dónal M., et al.
Publicado: (2022)

Quantitative transmission ultrasound tomography: Imaging and performance characteristics
por: Malik, Bilal, et al.
Publicado: (2018)

Assessment of fully automatic segmentation of pulmonary artery and aorta on noncontrast CT with optimal surface graph cuts
por: Sedghi Gamechi, Zahra, et al.
Publicado: (2021)

Hybrid U‐Net‐based deep learning model for volume segmentation of lung nodules in CT images
por: Wang, Yifan, et al.
Publicado: (2022)

SAUN: Stack attention U‐Net for left ventricle segmentation from cardiac cine magnetic resonance imaging
por: Sun, Xiaowu, et al.
Publicado: (2021)

[Image: see text] Quantitative assessment of breast density using transmission ultrasound tomography
por: Wiskin, James, et al.
Publicado: (2019)

Image reconstruction for positron emission tomography based on patch‐based regularization and dictionary learning
por: Zhang, Wanhong, et al.
Publicado: (2019)

Semiautomated segmentation of head and neck cancers in 18F-FDG PET scans: A just-enough-interaction approach
por: Beichel, Reinhard R., et al.
Publicado: (2016)

Deep learning‐based carotid media‐adventitia and lumen‐intima boundary segmentation from three‐dimensional ultrasound images
por: Zhou, Ran, et al.
Publicado: (2019)

Automated 3D geometry segmentation of the healthy and diseased carotid artery in free‐hand, probe tracked ultrasound images
por: de Ruijter, Joerik, et al.
Publicado: (2020)

Breast MRI segmentation for density estimation: Do different methods give the same results and how much do differences matter?
por: Doran, Simon J., et al.
Publicado: (2017)

Toward a standard for the evaluation of PET‐Auto‐Segmentation methods following the recommendations of AAPM task group No. 211: Requirements and implementation
por: Berthon, Beatrice, et al.
Publicado: (2017)

Towards ultrasound‐guided adaptive radiotherapy for cervical cancer: Evaluation of Elekta's semiautomated uterine segmentation method on 3D ultrasound images
por: Mason, Sarah A., et al.
Publicado: (2017)

Fully automated segmentation of the left atrium, pulmonary veins, and left atrial appendage from magnetic resonance angiography by joint‐atlas‐optimization
por: Qiao, Menyun, et al.
Publicado: (2019)

Segmentation of dental cone‐beam CT scans affected by metal artifacts using a mixed‐scale dense convolutional neural network
por: Minnema, Jordi, et al.
Publicado: (2019)

Fully automatic segmentation and objective assessment of atrial scars for long‐standing persistent atrial fibrillation patients using late gadolinium‐enhanced MRI
por: Yang, Guang, et al.
Publicado: (2018)

Automated segmentation of lung, liver, and liver tumors from Tc‐99m MAA SPECT/CT images for Y‐90 radioembolization using convolutional neural networks
por: Chaichana, Anucha, et al.
Publicado: (2021)

Automatic segmentation of the choroid in enhanced depth imaging optical coherence tomography images
por: Tian, Jing, et al.
Publicado: (2013)

MRI texture features as biomarkers to predict MGMT methylation status in glioblastomas
por: Korfiatis, Panagiotis, et al.
Publicado: (2016)

Effect of reconstruction methods and x-ray tube current–time product on nodule detection in an anthropomorphic thorax phantom: A crossed-modality JAFROC observer study
por: Thompson, J. D., et al.
Publicado: (2016)

Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features
por: Lo, P., et al.
Publicado: (2016)

Effect of atelectasis changes on tissue mass and dose during lung radiotherapy
por: Guy, Christopher L., et al.
Publicado: (2016)

Technical Note: Contrast free angiography of the pulmonary vasculature in live mice using a laboratory x-ray source
por: Samarage, Chaminda R., et al.
Publicado: (2016)

Impact of PET/CT system, reconstruction protocol, data analysis method, and repositioning on PET/CT precision: An experimental evaluation using an oncology and brain phantom
por: Mansor, Syahir, et al.
Publicado: (2017)

Quantitative evaluation of contrast agent uptake in standard fat‐suppressed dynamic contrast‐enhanced MRI examinations of the breast
por: Kousi, Evanthia, et al.
Publicado: (2017)

Cannot write session to /tmp/vufind_sessions/sess_h7oins564406djfcbfopm1uhq6