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Automatic liver segmentation on computed tomography using random walkers for treatment planning
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/11866763_6openalex-snapshot
Active Shape Models for a Fully Automated 3D Segmentation of the Liver – An Evaluation on Clinical Data
inferredcites →https://doi.org/10.1007/978-3-540-73273-0_1openalex-snapshot
A Shape-Guided Deformable Model with Evolutionary Algorithm Initialization for 3D Soft Tissue Segmentation
inferredcites →https://doi.org/10.1007/978-3-642-04271-3_121openalex-snapshot
Atlas-Based Automated Segmentation of Spleen and Liver Using Adaptive Enhancement Estimation
inferredcites →https://doi.org/10.1007/978-3-642-15711-0_2openalex-snapshot
Fast Random Walker with Priors Using Precomputation for Interactive Medical Image Segmentation
inferredcites →https://doi.org/10.1016/j.acra.2008.07.008openalex-snapshot
Automated Segmentation of the Liver from 3D CT Images Using Probabilistic Atlas and Multilevel Statistical Shape Model
inferredcites →https://doi.org/10.1016/j.ejrad.2010.07.003openalex-snapshot
Improving low-dose abdominal CT images by Weighted Intensity Averaging over Large-scale Neighborhoods
inferredcites →https://doi.org/10.1016/j.media.2013.10.003openalex-snapshot
A conditional statistical shape model with integrated error estimation of the conditions; Application to liver segmentation in non-contrast CT images
inferredcites →https://doi.org/10.1016/j.media.2014.10.003openalex-snapshot
A homotopy-based sparse representation for fast and accurate shape prior modeling in liver surgical planning
inferredcites →https://doi.org/10.1016/j.media.2015.05.009openalex-snapshot
Efficient multi-atlas abdominal segmentation on clinically acquired CT with SIMPLE context learning
inferredcites →https://doi.org/10.1016/s0003-3944(03)00066-xopenalex-snapshot
Mesure semi-automatique des différents lobes hépatiques. Application à la recherche d’une corrélation entre volumes des lobes du foie. Une étude sur 50 angioscanners de foies sains
inferredcites →https://doi.org/10.1088/0031-9155/60/19/7461openalex-snapshot
Automatic liver contouring for radiotherapy treatment planning
inferredcites →https://doi.org/10.1109/iembs.2011.6092092openalex-snapshot
A graph-theoretic approach for segmentation of PET images
inferredcites →https://doi.org/10.1109/tmi.2003.809139openalex-snapshot
Construction of an abdominal probabilistic atlas and its application in segmentation
inferredcites →https://doi.org/10.1109/tmi.2009.2013851openalex-snapshot
Comparison and Evaluation of Methods for Liver Segmentation From CT Datasets
inferredcites →https://doi.org/10.1109/tpami.2006.233openalex-snapshot
Random Walks for Image Segmentation
inferredcites →https://doi.org/10.1155/2014/198015openalex-snapshot
A Low-Interaction Automatic 3D Liver Segmentation Method Using Computed Tomography for Selective Internal Radiation Therapy
inferredcites →https://doi.org/10.1160/me9043openalex-snapshot
Automatic Generation of 3D Statistical Shape Models with Optimal Landmark Distributions
inferredcites →https://doi.org/10.1371/journal.pone.0118064openalex-snapshot
Adaptive Mesh Expansion Model (AMEM) for Liver Segmentation from CT Image
inferredcites →https://doi.org/10.17179/excli2016-402openalex-snapshot
Automatic liver tumor segmentation on computed tomography for patient treatment planning and monitoring
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781119819165.ch7openalex-snapshot
DMSAN: Deep Multi‐Scale Attention Network for Automatic Liver Segmentation From Abdomen CT Images
inferred← citeshttps://doi.org/10.1002/mp.14922openalex-snapshot
PA‐ResSeg: A phase attention residual network for liver tumor segmentation from multiphase CT images
inferred← citeshttps://doi.org/10.1002/mp.15303openalex-snapshot
Automated segmentation of lung, liver, and liver tumors from Tc‐99m MAA SPECT/CT images for Y‐90 radioembolization using convolutional neural networks
inferred← citeshttps://doi.org/10.1007/978-3-030-59719-1_50openalex-snapshot
E$$^2$$Net: An Edge Enhanced Network for Accurate Liver and Tumor Segmentation on CT Scans
inferred← citeshttps://doi.org/10.1007/978-3-031-22061-6_13openalex-snapshot
GradXcepUNet: Explainable AI Based Medical Image Segmentation
inferred← citeshttps://doi.org/10.1007/s10462-017-9550-xopenalex-snapshot
Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography
inferred← citeshttps://doi.org/10.1007/s11548-019-01991-5openalex-snapshot
Computer-aided diagnosis of cirrhosis and hepatocellular carcinoma using multi-phase abdomen CT
inferred← citeshttps://doi.org/10.1007/s11548-020-02225-9openalex-snapshot
Atlas-based liver segmentation for nonhuman primate research
inferred← citeshttps://doi.org/10.1016/j.bspc.2022.104437openalex-snapshot
CANet: Context aware network with dual-stream pyramid for medical image segmentation
inferred← citeshttps://doi.org/10.1016/j.cmpb.2017.08.020openalex-snapshot
Combined endeavor of Neutrosophic Set and Chan-Vese model to extract accurate liver image from CT scan
inferred← citeshttps://doi.org/10.1016/j.cmpb.2021.106501openalex-snapshot
HFRU-Net: High-Level Feature Fusion and Recalibration UNet for Automatic Liver and Tumor Segmentation in CT Images
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2018.02.012openalex-snapshot
Fully automatic liver segmentation in CT images using modified graph cuts and feature detection
inferred← citeshttps://doi.org/10.1016/j.compmedimag.2019.05.003openalex-snapshot
A robust multi-variability model based liver segmentation algorithm for CT-scan and MRI modalities
inferred← citeshttps://doi.org/10.1016/j.engappai.2025.111374openalex-snapshot
Feature refinement attention U-net for enhanced liver segmentation
inferred← citeshttps://doi.org/10.1016/j.ibmed.2025.100210openalex-snapshot
V-NET-VGG16: Hybrid deep learning architecture for optimal segmentation and classification of multi-differentiated liver tumors
inferred← citeshttps://doi.org/10.1016/j.liver.2024.100251openalex-snapshot
Deep learning technique for automatic liver and liver tumor segmentation in CT images
inferred← citeshttps://doi.org/10.1016/j.media.2021.102005openalex-snapshot
Weakly-Supervised teacher-Student network for liver tumor segmentation from non-enhanced images
inferred← citeshttps://doi.org/10.1016/j.media.2022.102680openalex-snapshot
The Liver Tumor Segmentation Benchmark (LiTS)
inferred← citeshttps://doi.org/10.1038/s41598-022-20108-8openalex-snapshot
A pipeline for automated deep learning liver segmentation (PADLLS) from contrast enhanced CT exams
inferred← citeshttps://doi.org/10.1088/1361-6501/ab95dbopenalex-snapshot
A novel approach for automatic and robust segmentation of the 3D liver in computed tomography images
inferred← citeshttps://doi.org/10.1109/access.2019.2896961openalex-snapshot
Deep Belief Network Modeling for Automatic Liver Segmentation
inferred← citeshttps://doi.org/10.1109/eit.2018.8500205openalex-snapshot
Automatic Tumor Segmentation Using Machine Learning Classifiers
inferred← citeshttps://doi.org/10.1109/jbhi.2023.3278079openalex-snapshot
AC-E Network: Attentive Context-Enhanced Network for Liver Segmentation
inferred← citeshttps://doi.org/10.1109/tmi.2018.2845918openalex-snapshot
H-DenseUNet: Hybrid Densely Connected UNet for Liver and Tumor Segmentation From CT Volumes
inferred← citeshttps://doi.org/10.1155/2022/2665283openalex-snapshot
Efficient Liver Segmentation from Computed Tomography Images Using Deep Learning
inferred← citeshttps://doi.org/10.1155/2022/7954333openalex-snapshot
A Lightweight Convolutional Neural Network Model for Liver Segmentation in Medical Diagnosis
inferred← citeshttps://doi.org/10.1186/s13550-019-0485-xopenalex-snapshot
Multi-modal image analysis for semi-automatic segmentation of the total liver and liver arterial perfusion territories for radioembolization
inferred← citeshttps://doi.org/10.12688/f1000research.12892.1openalex-snapshot
Development of a deep neural network derived from contours defined by consensus-based guidelines for automatic target segmentation in hepatocellular carcinoma radiotherapy: A study protocol
inferred← citeshttps://doi.org/10.1515/jisys-2017-0144openalex-snapshot
A Novel Approach to Extract Exact Liver Image Boundary from Abdominal CT Scan using Neutrosophic Set and Fast Marching Method
inferred← citeshttps://doi.org/10.1515/jisys-2017-0362openalex-snapshot
Universal Liver Extraction Algorithm: An Improved Chan–Vese Model
inferred← citeshttps://doi.org/10.17179/excli2017-1038openalex-snapshot
Highlight report: The pseudolobule in liver fibrosis
inferred← citeshttps://doi.org/10.17946/jrst.2024.47.1.39openalex-snapshot
Optimization of Non-Local Means Algorithm in Low-Dose Computed Tomographic Image Based on Noise Level and Similarity Evaluations
inferred← citeshttps://doi.org/10.21203/rs.3.rs-1746728/v1openalex-snapshot
Hepatic tumors diagnosis system based on fuzzy c-means using computed tomography images
inferred← citeshttps://doi.org/10.24963/ijcai.2019/593openalex-snapshot
Light-Weight Hybrid Convolutional Network for Liver Tumor Segmentation
inferred← citeshttps://doi.org/10.3389/fgene.2019.01110openalex-snapshot
Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation
inferred← citeshttps://doi.org/10.36647/ijereee/09.01.a001openalex-snapshot
Automatic Computer Propped Diagnosis Framework of Liver Cancer Detection with Simulation using CNN LSTM
inferred← citeshttps://doi.org/10.48084/etasr.9157openalex-snapshot
Efficient Liver Segmentation using Advanced 3D-DCNN Algorithm on CT Images
inferred← citeshttps://doi.org/10.48550/arxiv.2101.09987openalex-snapshot
Automatic Liver Segmentation from CT Images Using Deep Learning Algorithms: A Comparative Study
inferred← citeshttps://doi.org/10.5120/ijca2017915917openalex-snapshot
An Adaptive Method for Fully Automatic Liver Segmentation in Medical MRI-Images
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