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A Novel Approach for Lung Nodules Segmentation in Chest CT Using Level Sets
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11566489_93openalex-snapshot
Efficient Kernel Density Estimation of Shape and Intensity Priors for Level Set Segmentation
inferredcites →https://doi.org/10.1007/11566489_96openalex-snapshot
Robust Pulmonary Nodule Segmentation in CT: Improving Performance for Juxtapleural Cases
inferredcites →https://doi.org/10.1007/11866763_112openalex-snapshot
Supervised Probabilistic Segmentation of Pulmonary Nodules in CT Scans
inferredcites →https://doi.org/10.1007/978-0-387-21637-9openalex-snapshot
Numerical Geometry of Images
inferredcites →https://doi.org/10.1007/978-3-540-30135-6_26openalex-snapshot
Implicit Active Shape Models for 3D Segmentation in MR Imaging
inferredcites →https://doi.org/10.1007/978-3-642-15711-0_78openalex-snapshot
Toward Precise Pulmonary Nodule Descriptors for Nodule Type Classification
inferredcites →https://doi.org/10.1016/j.media.2004.01.003openalex-snapshot
Mutual information in coupled multi-shape model for medical image segmentation
inferredcites →https://doi.org/10.1016/j.media.2004.06.008openalex-snapshot
3D image segmentation of deformable objects with joint shape-intensity prior models using level sets
inferredcites →https://doi.org/10.1016/j.media.2010.08.005openalex-snapshot
Segmentation of pulmonary nodules of various densities with morphological approaches and convexity models
inferredcites →https://doi.org/10.1023/a:1008102926703openalex-snapshot
Shock Graphs and Shape Matching
inferredcites →https://doi.org/10.1023/a:1020874308076openalex-snapshot
A Multiphase Level Set Framework for Image Segmentation Using the Mumford and Shah Model
inferredcites →https://doi.org/10.1049/iet-cvi.2010.0125openalex-snapshot
Multimodal imaging: modelling and segmentation with biomedical applications
inferredcites →https://doi.org/10.1109/42.932744openalex-snapshot
Automated detection of pulmonary nodules in helical CT images based on an improved template-matching technique
inferredcites →https://doi.org/10.1109/cvpr.2005.213openalex-snapshot
Level Set Evolution without Re-Initialization: A New Variational Formulation
inferredcites →https://doi.org/10.1109/cvpr.2010.5540008openalex-snapshot
Stratified learning of local anatomical context for lung nodules in CT images
inferredcites →https://doi.org/10.1109/iccvw.2011.6130411openalex-snapshot
Probabilistic shape-based segmentation using level sets
inferredcites →https://doi.org/10.1109/icip.2010.5651233openalex-snapshot
Robust segmentation of lung tissue in chest CT scanning
inferredcites →https://doi.org/10.1109/isbi.2012.6235736openalex-snapshot
An AAM-based detection approach of lung nodules from LDCT scans
inferredcites →https://doi.org/10.1109/isbi.2013.6556417openalex-snapshot
Variational approach for small-size lung nodule segmentation
inferredcites →https://doi.org/10.1109/tbme.2009.2017027openalex-snapshot
Shape-Based Computer-Aided Detection of Lung Nodules in Thoracic CT Images
inferredcites →https://doi.org/10.1109/tbme.2011.2167621openalex-snapshot
Automated Segmentation Refinement of Small Lung Nodules in CT Scans by Local Shape Analysis
inferredcites →https://doi.org/10.1109/tip.2005.863949openalex-snapshot
Precise segmentation of multimodal images
inferredcites →https://doi.org/10.1109/titb.2007.899504openalex-snapshot
3-D Segmentation Algorithm of Small Lung Nodules in Spiral CT Images
inferredcites →https://doi.org/10.1109/tmi.2002.808355openalex-snapshot
A shape-based approach to the segmentation of medical imagery using level sets
inferredcites →https://doi.org/10.1109/tmi.2003.817785openalex-snapshot
Three-dimensional segmentation and growth-rate estimation of small pulmonary nodules in helical ct images
inferredcites →https://doi.org/10.1109/tmi.2004.843172openalex-snapshot
Robust anisotropic Gaussian fitting for volumetric characterization of Pulmonary nodules in multislice CT
inferredcites →https://doi.org/10.1109/tmi.2005.844167openalex-snapshot
Vessel tree reconstruction in thoracic CT scans with application to nodule detection
inferredcites →https://doi.org/10.1109/tmi.2006.871547openalex-snapshot
Morphological segmentation and partial volume analysis for volumetry of solid pulmonary lesions in thoracic CT scans
inferredcites →https://doi.org/10.1109/tmi.2006.871548openalex-snapshot
On measuring the change in size of pulmonary nodules
inferredcites →https://doi.org/10.1109/tmi.2007.907555openalex-snapshot
Segmentation of Pulmonary Nodules in Thoracic CT Scans: A Region Growing Approach
inferredcites →https://doi.org/10.1109/tmi.2009.2032542openalex-snapshot
The ${LoG}$ Characteristic Scale: A Consistent Measurement of Lung Nodule Size in CT Imaging
inferredcites →https://doi.org/10.1109/tpami.1987.4767871openalex-snapshot
Modeling and Segmentation of Noisy and Textured Images Using Gibbs Random Fields
inferredcites →https://doi.org/10.1109/tpami.2006.171openalex-snapshot
Shape registration in implicit spaces using information theory and free form deformations
inferredcites →https://doi.org/10.1109/tpami.2007.1100openalex-snapshot
Curve/Surface Representation and Evolution Using Vector Level Sets with Application to the Shape-Based Segmentation Problem
inferredcites →https://doi.org/10.1109/tpami.2012.245openalex-snapshot
Shape Representation and Registration in Vector Implicit Spaces: Adopting a Closed-Form Solution in the Optimization Process
inferredcites →https://doi.org/10.1117/12.808288openalex-snapshot
Level-set segmentation of pulmonary nodules in radiographs using a CT prior
inferredcites →https://doi.org/10.1118/1.1656593openalex-snapshot
Segmentation of nodules on chest computed tomography for growth assessment
inferredcites →https://doi.org/10.1118/1.598605openalex-snapshot
Two‐dimensional multi‐criterion segmentation of pulmonary nodules on helical CT images
inferredcites →https://doi.org/10.1120/jacmp.v4i3.2522openalex-snapshot
Automatic detection of small lung nodules on CT utilizing a local density maximum algorithm
inferredcites →https://doi.org/10.1137/1.9781611970920openalex-snapshot
Iterative Methods for Optimization
inferredcites →https://doi.org/10.1148/79.2.221openalex-snapshot
Differentiation of Benign and Malignant Pulmonary Nodules by Growth Rate
inferredcites →https://doi.org/10.1148/radiographics.19.5.g99se181303openalex-snapshot
Computerized Detection of Pulmonary Nodules on CT Scans
inferredcites →https://doi.org/10.1148/radiol.2283020059openalex-snapshot
Small Pulmonary Nodules: Volume Measurement at Chest CT—Phantom Study
inferredcites →https://doi.org/10.1148/radiol.2312030553openalex-snapshot
Small Pulmonary Nodules: Reproducibility of Three-dimensional Volumetric Measurement and Estimation of Time to Follow-up CT
inferredcites →https://doi.org/10.1148/radiol.2323032035openalex-snapshot
Lung Image Database Consortium: Developing a Resource for the Medical Imaging Research Community
inferredcites →https://doi.org/10.1148/radiol.2372041887openalex-snapshot
Guidelines for Management of Small Pulmonary Nodules Detected on CT Scans: A Statement from the Fleischner Society
inferredcites →https://doi.org/10.1155/2011/632195openalex-snapshot
Segmentation of Juxtapleural Pulmonary Nodules Using a Robust Surface Estimate
inferredcites →https://doi.org/10.1378/chest.125.4.1522openalex-snapshot
Managing the Small Pulmonary Nodule Discovered by CT
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.2139/ssrn.3990566openalex-extracted
Boundary Refinement Using Adversarial Learning: Application to Lung Tumor Segmentation in Thoracic MRI
inferred← citeshttps://doi.org/10.1002/aisy.202401093openalex-snapshot
Small but Strong: Lightweight Architecture Improves Lung Nodule Detection and Segmentation
inferred← citeshttps://doi.org/10.1002/ima.22336openalex-snapshot
Automated delineation of non‐small cell lung cancer: A step toward quantitative reasoning in medical decision science
inferred← citeshttps://doi.org/10.1002/ima.22964openalex-snapshot
RETRACTED: SAtUNet: Series atrous convolution enhanced U‐Net for lung nodule segmentation
inferred← citeshttps://doi.org/10.1002/ima.23209openalex-snapshot
DAG ‐Net: Dual‐Branch Attention‐Guided Network for Multi‐Scale Information Fusion in Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1002/med.21846openalex-snapshot
A review in radiomics: Making personalized medicine a reality via routine imaging
inferred← citeshttps://doi.org/10.1002/mp.12690openalex-snapshot
Segmentation and tracking of lung nodules via graph‐cuts incorporating shape prior and motion from 4D CT
inferred← citeshttps://doi.org/10.1002/mp.14068openalex-snapshot
Feature‐shared adaptive‐boost deep learning for invasiveness classification of pulmonary subsolid nodules in CT images
inferred← citeshttps://doi.org/10.1002/mp.15074openalex-snapshot
An effective deep network for automatic segmentation of complex lung tumors in CT images
inferred← citeshttps://doi.org/10.1002/mp.15810openalex-snapshot
Hybrid U‐Net‐based deep learning model for volume segmentation of lung nodules in CT images
inferred← citeshttps://doi.org/10.1002/mp.16900openalex-snapshot
Segmentation of lung nodules based on a refined segmentation network
inferred← citeshttps://doi.org/10.1007/978-3-030-32040-9_30openalex-snapshot
Lung Nodule Segmentation Based on Convolutional Neural Networks Using Multi-orientation and Patchwise Mechanisms
inferred← citeshttps://doi.org/10.1007/978-3-319-68843-5_11openalex-snapshot
Radiomics in Medical Imaging—Detection, Extraction and Segmentation
inferred← citeshttps://doi.org/10.1007/978-3-319-71767-8_9openalex-snapshot
Early Detection of Lung Cancer Using Wavelet Feature Descriptor and Feed Forward Back Propagation Neural Networks Classifier
inferred← citeshttps://doi.org/10.1007/978-981-15-4015-8_23openalex-snapshot
Optical Flow Based Background Subtraction Method for Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1007/978-981-19-0898-9_54openalex-snapshot
Accurate Segmentation of Lung Nodule using Adaptive Weights as Feature for Recurrent Neural Network
inferred← citeshttps://doi.org/10.1007/978-981-99-1648-1_2openalex-snapshot
Automating Patient-Level Lung Cancer Diagnosis in Different Data Regimes
inferred← citeshttps://doi.org/10.1007/s00521-021-06182-5openalex-snapshot
Lung cancer detection and classification with DGMM-RBCNN technique
inferred← citeshttps://doi.org/10.1007/s00779-021-01637-xopenalex-snapshot
An IOMT assisted lung nodule segmentation using enhanced receptive field-based modified UNet
inferred← citeshttps://doi.org/10.1007/s10278-019-00301-4openalex-snapshot
Deep Deconvolutional Residual Network Based Automatic Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1007/s10278-020-00346-wopenalex-snapshot
Optical Flow Methods for Lung Nodule Segmentation on LIDC-IDRI Images
inferred← citeshttps://doi.org/10.1007/s10489-026-07176-3openalex-snapshot
MSCFNet: mixed-scale context fusion network for medical image segmentation
inferred← citeshttps://doi.org/10.1007/s10916-018-1139-7openalex-snapshot
An IoT Based Predictive Modelling for Predicting Lung Cancer Using Fuzzy Cluster Based Segmentation and Classification
inferred← citeshttps://doi.org/10.1007/s11042-024-18485-5openalex-snapshot
V-3DResNets: a 3D convolutional neural network based on residual network variants and slice grouping for pulmonary nodule detection
inferred← citeshttps://doi.org/10.1007/s11220-023-00451-4openalex-snapshot
KFS-Net: Key Features Sampling Network for Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1007/s11227-021-03845-xopenalex-snapshot
An efficient U-Net framework for lung nodule detection using densely connected dilated convolutions
inferred← citeshttps://doi.org/10.1007/s11548-021-02504-zopenalex-snapshot
Preliminary study of generalized semiautomatic segmentation for 3D voxel labeling of lesions based on deep learning
inferred← citeshttps://doi.org/10.1007/s11554-017-0710-7openalex-snapshot
A GPU-based elastic shape registration approach in implicit spaces
inferred← citeshttps://doi.org/10.1007/s11604-022-01359-xopenalex-snapshot
Artificial intelligence in lung cancer: current applications and perspectives
inferred← citeshttps://doi.org/10.1016/j.asoc.2019.105934openalex-snapshot
Dual-branch residual network for lung nodule segmentation
inferred← citeshttps://doi.org/10.1016/j.bspc.2016.12.019openalex-snapshot
Segmentation of differential structures on computed tomography images for diagnosis lung-related diseases
inferred← citeshttps://doi.org/10.1016/j.bspc.2017.11.017openalex-snapshot
An appraisal of nodules detection techniques for lung cancer in CT images
inferred← citeshttps://doi.org/10.1016/j.bspc.2020.102296openalex-snapshot
LungSeg-Net: Lung field segmentation using generative adversarial network
inferred← citeshttps://doi.org/10.1016/j.bspc.2021.102890openalex-snapshot
Multi-granularity scale-aware networks for hard pixels segmentation of pulmonary nodules
inferred← citeshttps://doi.org/10.1016/j.bspc.2024.106804openalex-snapshot
MAST-UNet: More adaptive semantic texture for segmenting pulmonary nodules
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105781openalex-snapshot
CSE-GAN: A 3D conditional generative adversarial network with concurrent squeeze-and-excitation blocks for lung nodule segmentation
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2023.107437openalex-snapshot
A survey and taxonomy of 2.5D approaches for lung segmentation and nodule detection in CT images
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108674openalex-snapshot
SaraNet: Semantic aggregation reverse attention network for pulmonary nodule segmentation
inferred← citeshttps://doi.org/10.1016/j.ejmp.2019.06.003openalex-snapshot
A cascaded dual-pathway residual network for lung nodule segmentation in CT images
inferred← citeshttps://doi.org/10.1016/j.eswa.2016.05.024openalex-snapshot
Hessian based approaches for 3D lung nodule segmentation
inferred← citeshttps://doi.org/10.1016/j.eswa.2018.11.010openalex-snapshot
An unsupervised metaheuristic search approach for segmentation and volume measurement of pulmonary nodules in lung CT scans
inferred← citeshttps://doi.org/10.1016/j.ibmed.2024.100166openalex-snapshot
Improving the quality of pulmonary nodules segmentation using the new proposed U-Net neural network
inferred← citeshttps://doi.org/10.1016/j.jksuci.2018.07.005openalex-snapshot
Automatic lung segmentation for the inclusion of juxtapleural nodules and pulmonary vessels using curvature based border correction
inferred← citeshttps://doi.org/10.1016/j.media.2017.06.014openalex-snapshot
Central focused convolutional neural networks: Developing a data-driven model for lung nodule segmentation
inferred← citeshttps://doi.org/10.1016/j.media.2022.102491openalex-snapshot
Prior-aware autoencoders for lung pathology segmentation
inferred← citeshttps://doi.org/10.1016/j.neucom.2015.05.044openalex-snapshot
A novel approach of lung segmentation on chest CT images using graph cuts
inferred← citeshttps://doi.org/10.1016/j.patrec.2019.03.004openalex-snapshot
A deep learning-shape driven level set synergism for pulmonary nodule segmentation
inferred← citeshttps://doi.org/10.1016/j.ymeth.2024.04.008openalex-snapshot
MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation
inferred← citeshttps://doi.org/10.1038/s41598-018-28100-xopenalex-snapshot
Unsupervised CT Lung Image Segmentation of a Mycobacterium Tuberculosis Infection Model
inferred← citeshttps://doi.org/10.1038/s41598-022-24900-4openalex-snapshot
Lung_PAYNet: a pyramidal attention based deep learning network for lung nodule segmentation
inferred← citeshttps://doi.org/10.1038/s41598-024-51452-6openalex-snapshot
GPU-accelerated lung CT segmentation based on level sets and texture analysis
inferred← citeshttps://doi.org/10.1038/s41598-025-85199-5openalex-snapshot
Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet
inferred← citeshttps://doi.org/10.1038/s41746-025-02221-wopenalex-snapshot
CoreFormer high fidelity pulmonary nodule segmentation with structural core priors and geodesic implicit fields
inferred← citeshttps://doi.org/10.1049/iet-ipr.2016.1014openalex-snapshot
Automatic benign and malignant classification of pulmonary nodules in thoracic computed tomography based on RF algorithm
inferred← citeshttps://doi.org/10.1049/iet-ipr.2018.5748openalex-snapshot
Kernel‐based Bayesian clustering of computed tomography images for lung nodule segmentation
inferred← citeshttps://doi.org/10.1049/iet-ipr.2019.1054openalex-snapshot
Segmentation of the pulmonary nodule and the attached vessels in the CT scan of the chest using morphological features and topological skeleton of the nodule
inferred← citeshttps://doi.org/10.1051/matecconf/201823202001openalex-snapshot
A Review of Image Segmentation Methods for Lung Nodule Detection Based on Computed Tomography Images
inferred← citeshttps://doi.org/10.1051/matecconf/201823202056openalex-snapshot
An Edge Detection Method for Suspicious Local Regions in CT images with Jaxtapleural Nodules
inferred← citeshttps://doi.org/10.1063/5.0111863openalex-snapshot
Fast recurrent neural network approach for automatic detection of lung nodules
inferred← citeshttps://doi.org/10.1080/03772063.2019.1565955openalex-snapshot
Novel Technology for Lung Tumor Detection Using Nanoimage
inferred← citeshttps://doi.org/10.1080/23311916.2024.2406377openalex-snapshot
An efficient pulmonary carcinoma nodule detection model
inferred← citeshttps://doi.org/10.1088/0031-9155/60/3/1307openalex-snapshot
Robust semi-automatic segmentation of pulmonary subsolid nodules in chest computed tomography scans
inferred← citeshttps://doi.org/10.1088/1742-6596/1236/1/012028openalex-snapshot
Automatic lung segmentation method in computed tomography scans
inferred← citeshttps://doi.org/10.1101/304949openalex-snapshot
Automated delineation of non-small cell lung cancer
inferred← citeshttps://doi.org/10.1109/access.2020.2968936openalex-snapshot
Segmentation of Pulmonary Nodules Using a GMM Fuzzy C-Means Algorithm
inferred← citeshttps://doi.org/10.1109/access.2024.3510856openalex-snapshot
LA-ResUNet: An Efficient Linear Attention Mechanism in ResUNet for the Semantic Segmentation of Pulmonary Nodules
inferred← citeshttps://doi.org/10.1109/bibm52615.2021.9669883openalex-snapshot
HAUNet-3D: a Novel Hierarchical Attention 3D UNet for Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1109/biocas.2018.8584661openalex-snapshot
Lung Nodule Segmentation Using Pleural Wall Shape
inferred← citeshttps://doi.org/10.1109/cac51589.2020.9326834openalex-snapshot
Automatic Segmentation of Lung Noudles using improved U-Net NetWork
inferred← citeshttps://doi.org/10.1109/ccdc.2016.7531986openalex-snapshot
Abnormal lung regions segmentation method based on improved ASM
inferred← citeshttps://doi.org/10.1109/cict56698.2022.9997926openalex-snapshot
An optical flow visualization based pipeline for lung nodule segmentation
inferred← citeshttps://doi.org/10.1109/citisia53721.2021.9719963openalex-snapshot
DCV: A Taxonomy on Deep Learning Based Lung Cancer Classification
inferred← citeshttps://doi.org/10.1109/cvmi66673.2025.11337927openalex-snapshot
GPU-Enhanced Lung CT Segmentation via Chan-Vese Model and Multi-Scale Texture Analysis
inferred← citeshttps://doi.org/10.1109/eexpolytech50912.2020.9243982openalex-snapshot
Automatic Lung Segmentation in Computed Tomography Images Using Active Shape Model
inferred← citeshttps://doi.org/10.1109/embc.2015.7319037openalex-snapshot
Minimum mutual information based level set clustering algorithm for fast MRI tissue segmentation
inferred← citeshttps://doi.org/10.1109/embc.2017.8037182openalex-snapshot
A multi-view deep convolutional neural networks for lung nodule segmentation
inferred← citeshttps://doi.org/10.1109/embc58623.2025.11254801openalex-snapshot
Exploring Attention-Driven Deep Learning for Accurate Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1109/icac353642.2021.9697158openalex-snapshot
Lung Cancer Detection Using CT Images and CNN Algorithm
inferred← citeshttps://doi.org/10.1109/icacci.2017.8125983openalex-snapshot
Extraction of lung nodules in HRCT images using partial differential equation based model
inferred← citeshttps://doi.org/10.1109/icaiihi57871.2023.10489238openalex-snapshot
Comparing Three Different Layers of CNN using Lungs Cancer Dataset
inferred← citeshttps://doi.org/10.1109/iccke.2016.7802139openalex-snapshot
Accurate automatic localisation of lung nodules using Graph Cut and snakes algorithms
inferred← citeshttps://doi.org/10.1109/iccmc.2019.8819677openalex-snapshot
An Fully Automated CAD System for Juxta-Vacular Nodules Segmentation in CT Scan Images
inferred← citeshttps://doi.org/10.1109/iccpct.2014.7054783openalex-snapshot
An efficient lung segmentation approach for interstitial lung disease
inferred← citeshttps://doi.org/10.1109/icett.2016.7873780openalex-snapshot
Automatic extraction of Lung lesion by using optimized toboggan based approach with feature normalization and transfer learning methods
inferred← citeshttps://doi.org/10.1109/icicml57342.2022.10009788openalex-snapshot
A deep learn-based algorithm for the identification of suspicious local blocks in Lung CT slices : Deep learning method to identify suspicious lung edge
inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897686openalex-snapshot
A Coarse-to-Fine Morphological Approach with Knowledge-Based Rules and Self-Adapting Correction for Lung Nodules Segmentation
inferred← citeshttps://doi.org/10.1109/icosp.2014.7015166openalex-snapshot
Distance regularized curve evolution: A formulation using creaseness features for dermoscopic image segmentation
inferred← citeshttps://doi.org/10.1109/icpr48806.2021.9413064openalex-snapshot
MTGAN: Mask and Texture-driven Generative Adversarial Network for Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1109/icscan.2018.8541257openalex-snapshot
Liver Segmentation using Bidirectional Region Growing with Edge Enhancement in NSCT Domain
inferred← citeshttps://doi.org/10.1109/icspc47137.2019.9067987openalex-snapshot
Segmenting Nodules of Lung Tomography Image with Level Set Algorithm and Neural Network
inferred← citeshttps://doi.org/10.1109/incet49848.2020.9154186openalex-snapshot
An Effective and Robust Cancer Detection in the Lungs with BPNN and Watershed Segmentation
inferred← citeshttps://doi.org/10.1109/inmic50486.2020.9318181openalex-snapshot
Lungs Nodule Cancer Detection Using Statistical Techniques
inferred← citeshttps://doi.org/10.1109/isbi.2017.7950733openalex-snapshot
Lung nodule segmentation using deep learned prior based graph cut
inferred← citeshttps://doi.org/10.1109/iscid.2019.10135openalex-snapshot
Random Walks with GMM Statistical Inference Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules
inferred← citeshttps://doi.org/10.1109/iscsic.2017.22openalex-snapshot
Small-Cell Lung Cancer Detection Using a Supervised Machine Learning Algorithm
inferred← citeshttps://doi.org/10.1109/itme.2019.00045openalex-snapshot
Pulmonary Nodule Segmentation in Computed Tomography with an Encoder-Decoder Architecture
inferred← citeshttps://doi.org/10.1109/jbhi.2015.2402173openalex-snapshot
Adaptive Cosegmentation of Pheochromocytomas in CECT Images Using Localized Level Set Models
inferred← citeshttps://doi.org/10.1109/jbhi.2018.2832455openalex-snapshot
Active Contours Based Segmentation and Lesion Periphery Analysis for Characterization of Skin Lesions in Dermoscopy Images
inferred← citeshttps://doi.org/10.1109/jbhi.2022.3220430openalex-snapshot
Knowledge Sharing for Pulmonary Nodule Detection in Medical Cyber-Physical Systems
inferred← citeshttps://doi.org/10.1109/mecbme.2018.8402411openalex-snapshot
A morphological operation-based approach for Sub-pleural lung nodule detection from CT images
inferred← citeshttps://doi.org/10.1109/msp.2019.2900993openalex-snapshot
From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and Opportunities
inferred← citeshttps://doi.org/10.1109/tetci.2021.3051910openalex-snapshot
Multi-Crop Convolutional Neural Networks for Fast Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.1109/tip.2013.2296103openalex-snapshot
2013 Index IEEE Transactions on Image Processing Vol. 22
inferred← citeshttps://doi.org/10.1109/tip.2023.3270801openalex-snapshot
Position-Aware Relation Learning for RGB-Thermal Salient Object Detection
inferred← citeshttps://doi.org/10.1109/tip.2026.3683290openalex-snapshot
Prototype-Based Multi-Dimension Intensity Mapping Density Sampling Network for Corrosion Segmentation
inferred← citeshttps://doi.org/10.1109/tmi.2015.2474119openalex-snapshot
Lung Lesion Extraction Using a Toboggan Based Growing Automatic Segmentation Approach
inferred← citeshttps://doi.org/10.1109/tmi.2017.2720119openalex-snapshot
3-D Active Contour Segmentation Based on Sparse Linear Combination of Training Shapes (SCoTS)
inferred← citeshttps://doi.org/10.1109/tmi.2018.2876510openalex-snapshot
Knowledge-based Collaborative Deep Learning for Benign-Malignant Lung Nodule Classification on Chest CT
inferred← citeshttps://doi.org/10.1109/tnnls.2023.3303395openalex-snapshot
Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck
inferred← citeshttps://doi.org/10.1109/wacv.2018.00070openalex-snapshot
Segmenting Root Systems in X-Ray Computed Tomography Images Using Level Sets
inferred← citeshttps://doi.org/10.1117/12.2284388openalex-snapshot
Method of the active contour for segmentation of bone systems on bitmap images
inferred← citeshttps://doi.org/10.1118/1.4907961openalex-snapshot
Pulmonary nodule detection in CT images based on shape constraint CV model
inferred← citeshttps://doi.org/10.1145/3338533.3366604openalex-snapshot
An Automated Lung Nodule Segmentation Method Based On Nodule Detection Network and Region Growing
inferred← citeshttps://doi.org/10.1155/2018/2183847openalex-snapshot
Juxta-Vascular Pulmonary Nodule Segmentation in PET-CT Imaging Based on an LBF Active Contour Model with Information Entropy and Joint Vector
inferred← citeshttps://doi.org/10.1155/2022/5112867openalex-snapshot
Lung Nodule Segmentation and Recognition Algorithm Based on Multiposition U-Net
inferred← citeshttps://doi.org/10.1155/2022/6727957openalex-snapshot
An Automatic Random Walker Algorithm for Segmentation of Ground Glass Opacity Pulmonary Nodules
inferred← citeshttps://doi.org/10.11591/ijece.v9i2.pp982-991openalex-snapshot
Framework for progressive segmentation of chest radiograph for efficient diagnosis of inert regions
inferred← citeshttps://doi.org/10.1177/0976500x251336602openalex-snapshot
LungCTSeg-Net: Lung HRCT Image Segmentation Using Learning Approach
inferred← citeshttps://doi.org/10.1186/s12938-015-0003-yopenalex-snapshot
Automated system for lung nodules classification based on wavelet feature descriptor and support vector machine
inferred← citeshttps://doi.org/10.1186/s12938-015-0043-3openalex-snapshot
Anatomy packing with hierarchical segments: an algorithm for segmentation of pulmonary nodules in CT images
inferred← citeshttps://doi.org/10.1186/s12938-020-00793-0openalex-snapshot
Segmentation of small ground glass opacity pulmonary nodules based on Markov random field energy and Bayesian probability difference
inferred← citeshttps://doi.org/10.1201/b22435-3openalex-snapshot
A Modified Global and Elastic ICP Shape Registration for Medical Imaging Applications
inferred← citeshttps://doi.org/10.12677/csa.2022.127177openalex-snapshot
A U-Net Lung Nodule Image Segmentation Method Incorporating a Dual Attention Module
inferred← citeshttps://doi.org/10.1371/journal.pone.0282107openalex-snapshot
SIFT-GVF-based lung edge correction method for correcting the lung region in CT images
inferred← citeshttps://doi.org/10.1615/critrevoncog.2023050439openalex-snapshot
Artificial Intelligence in Lung Cancer Imaging: From Data to Therapy
inferred← citeshttps://doi.org/10.18137/cardiometry.2023.26.302308openalex-snapshot
Automatic recognition of lung tumor using convolutional neural networks
inferred← citeshttps://doi.org/10.18297/etd/2938openalex-snapshot
Segmentation, tracking, and kinematics of lung parenchyma and lung tumors from 4D CT with application to radiation treatment planning.
inferred← citeshttps://doi.org/10.18297/etd/3301openalex-snapshot
Segmentation and classification of lung nodules from Thoracic CT scans : methods based on dictionary learning and deep convolutional neural networks.
inferred← citeshttps://doi.org/10.21037/qims-21-19openalex-snapshot
Two-stage multitask U-Net construction for pulmonary nodule segmentation and malignancy risk prediction
inferred← citeshttps://doi.org/10.2147/jmdh.s456167openalex-snapshot
Classification of Chest CT Lung Nodules Using Collaborative Deep Learning Model
inferred← citeshttps://doi.org/10.2174/0118749445335956241002052628openalex-snapshot
Condition Generative Adversarial Network Deep Learning Model for Accurate Segmentation and Volumetric Measurement to Detect Mucormycosis from Lung CT Images
inferred← citeshttps://doi.org/10.2174/1574893613666181029100249openalex-snapshot
Robust Pulmonary Nodule Segmentation in CT Image for Juxta-pleural and Juxta-vascular Case
inferred← citeshttps://doi.org/10.22161/ijaers.4.6.1openalex-snapshot
Feature Extraction and Classification of Automatically Segmented Lung Lesion Using Improved Toboggan Algorithm
inferred← citeshttps://doi.org/10.23919/ccc52363.2021.9549944openalex-snapshot
Deep Neural Networks for Semantic Segmentation of Lung Nodules
inferred← citeshttps://doi.org/10.23919/chicc.2019.8865247openalex-snapshot
A Method for Detecting Suspicious Border Regions of Extracted CT Lung Region
inferred← citeshttps://doi.org/10.24920/003615openalex-snapshot
The Artificial Intelligence-Enabled Medical Imaging: Today and Its Future
inferred← citeshttps://doi.org/10.26481/dis.20220209fzopenalex-snapshot
Distributed learning for optimal radiomics knowledge
inferred← citeshttps://doi.org/10.26481/dis.20230228avopenalex-snapshot
AI applications in routine clinical imaging
inferred← citeshttps://doi.org/10.3233/faia210154openalex-snapshot
WEU-Net: A Weight Excitation U-Net for Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.3233/faia230471openalex-snapshot
CompLung: Comprehensive Computer-Aided Diagnosis of Lung Cancer
inferred← citeshttps://doi.org/10.3233/thc-160597openalex-snapshot
Automatic computer aided analysis algorithms and system for adrenal tumors on CT images
inferred← citeshttps://doi.org/10.3389/fonc.2026.1802047openalex-snapshot
DPCrossU-Net: a dual-branch parallel CNN–Transformer network for lung nodule segmentation
inferred← citeshttps://doi.org/10.3389/frai.2022.782225openalex-snapshot
Segmentation of Lung Nodules on CT Images Using a Nested Three-Dimensional Fully Connected Convolutional Network
inferred← citeshttps://doi.org/10.3389/frai.2025.1682171openalex-snapshot
Leveraging multi-scale feature integration in UNet and FPN for semantic segmentation of lung nodules
inferred← citeshttps://doi.org/10.3390/app112110132openalex-snapshot
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.3390/diagnostics13162617openalex-snapshot
A Review of Deep Learning Techniques for Lung Cancer Screening and Diagnosis Based on CT Images
inferred← citeshttps://doi.org/10.3390/math9131457openalex-snapshot
An Efficient DA-Net Architecture for Lung Nodule Segmentation
inferred← citeshttps://doi.org/10.3390/s21051908openalex-snapshot
Lung Nodule Segmentation with a Region-Based Fast Marching Method
inferred← citeshttps://doi.org/10.36548/jiip.2020.4.002openalex-snapshot
Early diagnosis of Lung Cancer with Probability of Malignancy Calculation and Automatic Segmentation of Lung CT scan Images
inferred← citeshttps://doi.org/10.3978/j.issn.2223-4292.2016.02.06openalex-snapshot
Lung nodule segmentation in chest computed tomography using a novel background estimation method.
inferred← citeshttps://doi.org/10.4018/ijhisi.20210401.oa5openalex-snapshot
Fully Automatic Detection and Segmentation Approach for Juxta-Pleural Nodules From CT Images
inferred← citeshttps://doi.org/10.48550/arxiv.1910.03191openalex-snapshot
Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation
inferred← citeshttps://doi.org/10.48550/arxiv.2003.09293openalex-snapshot
U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
inferred← citeshttps://doi.org/10.70675/6cabf9efza834z492cz9634z82db6ebe2b3fopenalex-snapshot
Etude des méthodes d'apprentissage profond pour la classification et la segmentation des chromosome et des images pulmonaires
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
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