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A Machine Learning Approach for Locating Boundaries of Liver Tumors in CT Images
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
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Experimental Results
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What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/rcs.70068openalex-snapshot
MF‐ResUnet: A 3D Liver Image Segmentation Method Based on Multi‐Scale Feature Fusion
inferred← citeshttps://doi.org/10.1007/978-3-319-28194-0_7openalex-snapshot
Automatic Liver Tumor Segmentation in Follow-Up CT Scans: Preliminary Method and Results
inferred← citeshttps://doi.org/10.1007/978-3-642-28557-8_7openalex-snapshot
Segmentation of Liver Tumor Using Efficient Global Optimal Tree Metrics Graph Cuts
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/s11517-018-1803-6openalex-snapshot
Patient-specific and global convolutional neural networks for robust automatic liver tumor delineation in follow-up CT studies
inferred← citeshttps://doi.org/10.1007/s11548-010-0497-5openalex-snapshot
Liver tumors segmentation from CTA images using voxels classification and affinity constraint propagation
inferred← citeshttps://doi.org/10.1016/j.media.2011.06.006openalex-snapshot
Semi-automatic liver tumor segmentation with hidden Markov measure field model and non-parametric distribution estimation
inferred← citeshttps://doi.org/10.1016/j.media.2022.102680openalex-snapshot
The Liver Tumor Segmentation Benchmark (LiTS)
inferred← citeshttps://doi.org/10.1080/02648725.2023.2193057openalex-snapshot
Diagnosis of liver diseases based on artificial intelligence
inferred← citeshttps://doi.org/10.1088/0031-9155/60/16/6459openalex-snapshot
Metastatic liver tumour segmentation from discriminant Grassmannian manifolds
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Augmenting atlas-based liver segmentation for radiotherapy treatment planning by incorporating image features proximal to the atlas contours
inferred← citeshttps://doi.org/10.1109/crc.2018.00010openalex-snapshot
Medical Images Sequence Normalization and Augmentation: Improve Liver Tumor Segmentation from Small Dataset
inferred← citeshttps://doi.org/10.1109/icbbe.2009.5163371openalex-snapshot
Segmentation of Complicated Liver Lesion Based on Local Multiphase Level Set
inferred← citeshttps://doi.org/10.1109/icerect56837.2022.10060373openalex-snapshot
Retracted: A Calculation on CT scanner Settings Predictions with Artificial Intelligence
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Automatic Detection and Segmentation of Focal Liver Lesions in Contrast Enhanced CT Images
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inferred← citeshttps://doi.org/10.1109/ihmsc.2017.53openalex-snapshot
Hybrid Detection Model Combining Grey Scale Weight Based Quoit Filter with Support Vector Machine
inferred← citeshttps://doi.org/10.1109/isbi.2008.4541116openalex-snapshot
3D general lesion segmentation in CT
inferred← citeshttps://doi.org/10.1109/isbi.2011.5872793openalex-snapshot
Tumor segmentation using the learned distance metric
inferred← citeshttps://doi.org/10.1109/jstsp.2008.2011107openalex-snapshot
Advanced Segmentation Techniques for Lung Nodules, Liver Metastases, and Enlarged Lymph Nodes in CT Scans
inferred← citeshttps://doi.org/10.1109/mace.2011.5988824openalex-snapshot
Segmentation of liver tumor with local C-V level set
inferred← citeshttps://doi.org/10.1109/tase.2020.2978881openalex-snapshot
Depth Estimation of Hard Inclusions in Soft Tissue by Autonomous Robotic Palpation Using Deep Recurrent Neural Network
inferred← citeshttps://doi.org/10.1109/tim.2022.3198765openalex-snapshot
Methods to Recognize Depth of Hard Inclusions in Soft Tissue Using Ordinal Classification for Robotic Palpation
inferred← citeshttps://doi.org/10.1109/tro.2015.2402531openalex-snapshot
Methods to Segment Hard Inclusions in Soft Tissue During Autonomous Robotic Palpation
inferred← citeshttps://doi.org/10.1109/trpms.2021.3059780openalex-snapshot
DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation
inferred← citeshttps://doi.org/10.1117/12.770516openalex-snapshot
Simultaneous detection of multiple elastic surfaces with application to tumor segmentation in CT images
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A cost constrained boosting algorithm for fast lesion detection and segmentation
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Liver Tumor Segmentation from MR Images Using 3D Fast Marching Algorithm and Single Hidden Layer Feedforward Neural Network
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Locating Liver Lesion with Local C-V Level Set and Image Registration
inferred← citeshttps://doi.org/10.1186/s41747-020-00148-3openalex-snapshot
Multiphase CT-based prediction of Child-Pugh classification: a machine learning approach
inferred← citeshttps://doi.org/10.1515/bmt-2024-0620openalex-snapshot
Machine learning prediction of effective radiation doses in various computed tomography applications: a virtual human phantom study
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Practical Utility of Liver Segmentation Techniques in Clinical Surgeries and Interventions
inferred← citeshttps://doi.org/10.22215/etd/2018-12657openalex-snapshot
A CNN Based Method for Brain Tumor Detection
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A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning
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Research on Identifying Liver Diseases Based on Mathematical Models
inferred← citeshttps://doi.org/10.3389/fonc.2021.697178openalex-snapshot
Effects of Multiple Filters on Liver Tumor Segmentation From CT Images
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A Comprehensive Review of Machine Learning Used to Combat COVID-19
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Machine Learning Techniques Applied to Dose Prediction in Computed Tomography Tests
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A Fully-Automated Pipeline for Detection and Segmentation of Liver Lesions and Pathological Lymph Nodes
inferred← citeshttps://doi.org/10.54294/1h2wu4openalex-snapshot
Efficient Automated Detection and Segmentation of Medium and Large Liver Tumors: CAD Approach
inferred← citeshttps://doi.org/10.54294/5clvrbopenalex-snapshot
Interactive Liver Tumor Segmentation Using Graph-cuts and Watershed
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Segmentation of Liver Metastases in CT Scans by Adaptive Thresholding and Morphological Processing
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