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An unsupervised automatic segmentation algorithm for breast tissue classification of dedicated breast computed tomography images

https://doi.org/10.1002/mp.12920
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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A locally adaptive region growing algorithm for vascular segmentation
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An Integral Solution to Surface Evolution PDEs Via Geo-cuts
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Snakes: Active contour models
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A review of 3D vessel lumen segmentation techniques: Models, features and extraction schemes
inferredcites →https://doi.org/10.1016/j.neuroimage.2009.03.068openalex-snapshot
Performance measure characterization for evaluating neuroimage segmentation algorithms
inferredcites →https://doi.org/10.1016/j.patrec.2009.09.011openalex-snapshot
Data clustering: 50 years beyond K-means
inferredcites →https://doi.org/10.1016/j.ymeth.2007.03.009openalex-snapshot
Computer-assisted measurement of vessel shape from 3T magnetic resonance angiography of mouse brain
inferredcites →https://doi.org/10.1016/s1076-6332(03)80459-2openalex-snapshot
Computerized Assessment of Tissue Composition on Digitized Mammograms
inferredcites →https://doi.org/10.1016/s1470-2045(05)70390-9openalex-snapshot
Mammographic breast density as an intermediate phenotype for breast cancer
inferredcites →https://doi.org/10.1023/a:1007979827043openalex-snapshot
Geodesic Active Contours
inferredcites →https://doi.org/10.1054/bjoc.1999.1010openalex-snapshot
Relationship of vascular maturation in breast cancer blood vessels to vascular density and metastasis, assessed by expression of a novel basement membrane component, LH39
inferredcites →https://doi.org/10.1109/42.993126openalex-snapshot
Initialization, noise, singularities, and scale in height ridge traversal for tubular object centerline extraction
inferredcites →https://doi.org/10.1109/83.902291openalex-snapshot
Active contours without edges
inferredcites →https://doi.org/10.1109/cvpr.2003.1211344openalex-snapshot
Bayesian tangent shape model: estimating shape and pose parameters via Bayesian inference
inferredcites →https://doi.org/10.1109/iccv.2003.1238310openalex-snapshot
Computing geodesics and minimal surfaces via graph cuts
inferredcites →https://doi.org/10.1109/icpr.1994.576361openalex-snapshot
A modified Hausdorff distance for object matching
inferredcites →https://doi.org/10.1109/infoseee.2014.6946295openalex-snapshot
Interactive 3D liver vessels centerline extraction based on moving sphere model
inferredcites →https://doi.org/10.1109/isbi.2006.1624878openalex-snapshot
Shape Constrained Vessel Centerline Extraction by Integrating Surface Evolution and Topology Analysis
inferredcites →https://doi.org/10.1109/isbi.2007.356987openalex-snapshot
SEMI-AUTOMATIC CORONARY ARTERY CENTERLINE EXTRACTION IN COMPUTED TOMOGRAPHY ANGIOGRAPHY DATA
inferredcites →https://doi.org/10.1109/isbi.2014.6867990openalex-snapshot
Centerline constrained minimal path propagation for vessel extraction
inferredcites →https://doi.org/10.1109/tip.2008.2004611openalex-snapshot
Localizing Region-Based Active Contours
inferredcites →https://doi.org/10.1109/tmi.2003.816964openalex-snapshot
Measuring tortuosity of the intracerebral vasculature from MRA images
inferredcites →https://doi.org/10.1117/12.877881openalex-snapshot
Automatic tissue classification for high-resolution breast CT images based on bilateral filtering
inferredcites →https://doi.org/10.1118/1.2839439openalex-snapshot
Classification of breast computed tomography data
inferredcites →https://doi.org/10.1118/1.2841938openalex-snapshot
The effect of skin thickness determined using breast CT on mammographic dosimetry
inferredcites →https://doi.org/10.1118/1.3250863openalex-snapshot
The myth of the 50‐50 breast
inferredcites →https://doi.org/10.1118/1.3457331openalex-snapshot
Dosimetric characterization of a dedicated breast computed tomography clinical prototype
inferredcites →https://doi.org/10.1118/1.4765050openalex-snapshot
Dedicated breast CT: Fibroglandular volume measurements in a diagnostic population
inferredcites →https://doi.org/10.1118/1.4915079openalex-snapshot
Evolution of spatial resolution in breast CT at UC Davis
inferredcites →https://doi.org/10.1118/1.4919441openalex-snapshot
Dedicated breast computed tomography: Basic aspects
inferredcites →https://doi.org/10.1118/1.4931958openalex-snapshot
A minimum spanning forest based classification method for dedicated breast CT images
inferredcites →https://doi.org/10.1145/331499.331504openalex-snapshot
Data clustering
inferredcites →https://doi.org/10.1148/radiol.2213010334openalex-snapshot
Dedicated Breast CT: Radiation Dose and Image Quality Evaluation
inferredcites →https://doi.org/10.1148/radiol.2463070410openalex-snapshot
Dedicated Breast CT: Initial Clinical Experience
inferredcites →https://doi.org/10.1148/radiology.187.1.8451441openalex-snapshot
Artificial neural networks in mammography: application to decision making in the diagnosis of breast cancer.
inferredcites →https://doi.org/10.1148/rg.241035073openalex-snapshot
New Vessel Analysis Tool for Morphometric Quantification and Visualization of Vessels in CT and MR Imaging Data Sets
inferredcites →https://doi.org/10.2214/ajr.08.1017openalex-snapshot
Cone-Beam CT for Breast Imaging: Radiation Dose, Breast Coverage, and Image Quality
inferredcites →https://doi.org/10.2214/ajr.138.3.553openalex-snapshot
Computed tomographic mammography using a conventional body scanner

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/acm2.13726openalex-snapshot
Fully automated breast segmentation on spiral breast computed tomography images
inferred← citeshttps://doi.org/10.1002/mp.13156openalex-snapshot
Patient‐based 4D digital breast phantom for perfusion contrast‐enhanced breast CT imaging
inferred← citeshttps://doi.org/10.1002/mp.14159openalex-snapshot
Effects of kV, filtration, dose, and object size on soft tissue and iodine contrast in dedicated breast CT
inferred← citeshttps://doi.org/10.1002/mp.14716openalex-snapshot
Fibroglandular tissue distribution in the breast during mammography and tomosynthesis based on breast CT data: A patient‐based characterization of the breast parenchyma
inferred← citeshttps://doi.org/10.1002/mp.14826openalex-snapshot
Dataset of patient‐derived digital breast phantoms for in silico studies in breast computed tomography, digital breast tomosynthesis, and digital mammography
inferred← citeshttps://doi.org/10.1002/mp.15785openalex-snapshot
Patient‐derived heterogeneous breast phantoms for advanced dosimetry in mammography and tomosynthesis
inferred← citeshttps://doi.org/10.1002/mp.16185openalex-snapshot
Deep learning for x‐ray scatter correction in dedicated breast CT
inferred← citeshttps://doi.org/10.1002/mp.17980openalex-snapshot
Optimization and validation of a mechanical compression model for digital breast phantoms in mammography and tomosynthesis simulations
inferred← citeshttps://doi.org/10.1007/978-3-031-43827-1_17openalex-snapshot
Deep Learning in Image Processing: Part 1—Types of Neural Networks, Image Segmentation
inferred← citeshttps://doi.org/10.1007/s00261-024-04427-0openalex-snapshot
Radiomic analysis based on magnetic resonance imaging for the prediction of VEGF expression in hepatocellular carcinoma patients
inferred← citeshttps://doi.org/10.1007/s11831-020-09497-zopenalex-snapshot
A Systematic Review of the Techniques for the Automatic Segmentation of Organs-at-Risk in Thoracic Computed Tomography Images
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.111121openalex-snapshot
Image-based reconstruction of anthropomorphic breast phantoms for synthetic mammogram generation
inferred← citeshttps://doi.org/10.1016/j.ejmp.2021.03.007openalex-snapshot
Breast glandularity and mean glandular dose assessment using a deep learning framework: Virtual patients study
inferred← citeshttps://doi.org/10.1016/j.jvir.2019.11.032openalex-snapshot
Deep Learning Based on MR Imaging for Predicting Outcome of Uterine Fibroid Embolization
inferred← citeshttps://doi.org/10.1016/j.media.2021.102061openalex-snapshot
Deep learning reconstruction of digital breast tomosynthesis images for accurate breast density and patient-specific radiation dose estimation
inferred← citeshttps://doi.org/10.1016/j.radphyschem.2022.110715openalex-snapshot
Physical and digital phantoms for 2D and 3D x-ray breast imaging: Review on the state-of-the-art and future prospects
inferred← citeshttps://doi.org/10.1055/a-0808-7772openalex-snapshot
Artificial Intelligence with Radiology as a Trailblazer for Super-Diagnostics: An Essay
inferred← citeshttps://doi.org/10.1080/2150704x.2019.1692385openalex-snapshot
Hyperspectral remote sensing image feature extraction based on spectral clustering and subclass discriminant analysis
inferred← citeshttps://doi.org/10.1088/1361-6560/aae78dopenalex-snapshot
Development of 3D patient-based super-resolution digital breast phantoms using machine learning
inferred← citeshttps://doi.org/10.1088/1361-6560/aaf453openalex-snapshot
Monte Carlo study on optimal breast voxel resolution for dosimetry estimates in digital breast tomosynthesis
inferred← citeshttps://doi.org/10.1088/1361-6560/ab7a6dopenalex-snapshot
Estimation of glandular dose in mammography based on artificial neural networks
inferred← citeshttps://doi.org/10.1088/1361-6560/ac8966openalex-snapshot
Attenuation coefficient in the energy range 14–36 keV of 3D printing materials for physical breast phantoms
inferred← citeshttps://doi.org/10.1109/access.2020.2989819openalex-snapshot
Automatic Segmentation Algorithm of Ultrasound Heart Image Based on Convolutional Neural Network and Image Saliency
inferred← citeshttps://doi.org/10.1109/tmi.2022.3224223openalex-snapshot
A 3D Mathematical Breast Texture Model With Parameters Automatically Inferred From Clinical Breast CT Images
inferred← citeshttps://doi.org/10.1117/12.2317938openalex-snapshot
Automatic estimation of glandular tissue loss due to limited reconstruction voxel size in tomographic images of the breast
inferred← citeshttps://doi.org/10.1177/02841851211027386openalex-snapshot
Quantitative breast density measurement based on three-dimensional images: a study on cone-beam breast computed tomography
inferred← citeshttps://doi.org/10.21037/qims-20-1141openalex-snapshot
Prior information guided auto-contouring of breast gland for deformable image registration in postoperative breast cancer radiotherapy
inferred← citeshttps://doi.org/10.2174/1872212117666230120142802openalex-snapshot
Breast Tissue Classification Method Based on Machine Learning
inferred← citeshttps://doi.org/10.3390/cancers15020365openalex-snapshot
Radiomic Analysis Based on Magnetic Resonance Imaging for Predicting PD-L2 Expression in Hepatocellular Carcinoma
inferred← citeshttps://doi.org/10.3390/ijerph19084482openalex-snapshot
Application of Personalized Education in the Mobile Medical App for Breast Self-Examination
inferred← citeshttps://doi.org/10.3390/jcm12041582openalex-snapshot
Complete, Fully Automatic Detection and Classification of Benign and Malignant Breast Tumors Based on CT Images Using Artificial Intelligent and Image Processing
inferred← citeshttps://doi.org/10.3390/technologies14090555openalex-snapshot
Design, Fabrication, and Evaluation of a Patient-Derived 3D-Printed Anthropomorphic Breast Phantom for Performance Evaluation of Dual-Energy Subtraction in Contrast-Enhanced Mammography
inferred← citeshttps://doi.org/10.48550/arxiv.2009.01538openalex-snapshot
Deep Learning-based Initialization of Iterative Reconstruction for Breast Tomosynthesis
inferred← citeshttps://doi.org/10.70675/96d4a1e2za7eaz438bza08bzec4325c1ffe0openalex-snapshot
Role of deep learning into three-dimensional reconstruction for breast tomosynthesis
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