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Segmentation of Liver Metastases in CT Scans by Adaptive Thresholding and Morphological Processing

https://doi.org/10.54294/msg94u
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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/11428831_116openalex-snapshot
Automatic Hepatic Tumor Segmentation Using Statistical Optimal Threshold
inferredcites →https://doi.org/10.1007/978-3-540-73055-2_8openalex-snapshot
Automatic Segmentation of Single and Multiple Neoplastic Hepatic Lesions in CT Images
inferredcites →https://doi.org/10.1007/s11548-006-0059-zopenalex-snapshot
OncoTREAT: a software assistant for cancer therapy monitoring
inferredcites →https://doi.org/10.1109/icpr.2006.93openalex-snapshot
A Machine Learning Approach for Locating Boundaries of Liver Tumors in CT Images
inferredcites →https://doi.org/10.1109/iembs.2005.1617181openalex-snapshot
Liver Tumor Volume Estimation By Semi-Automatic Segmentation Method
inferredcites →https://doi.org/10.1109/isbi.2008.4541116openalex-snapshot
3D general lesion segmentation in CT
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/10.1117/12.770516openalex-snapshot
Simultaneous detection of multiple elastic surfaces with application to tumor segmentation in CT images

What points at this work

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

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Automatic segmentation system for liver tumors based on the multilevel thresholding and electromagnetism optimization algorithm
inferred← citeshttps://doi.org/10.1002/mp.13584openalex-snapshot
Accurate colorectal tumor segmentation for CT scans based on the label assignment generative adversarial network
inferred← citeshttps://doi.org/10.1007/978-1-4614-7245-2_5openalex-snapshot
Computational Anatomy in the Abdomen: Automated Multi-Organ and Tumor Analysis from Computed Tomography
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Segmenting Hepatocellular Carcinoma in Multi-phase CT
inferred← citeshttps://doi.org/10.1007/978-3-642-36620-8_20openalex-snapshot
A Cascade Learning Method for Liver Lesion Detection in CT Images
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Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks
inferred← citeshttps://doi.org/10.1007/s10462-017-9550-xopenalex-snapshot
Review of liver segmentation and computer assisted detection/diagnosis methods in computed tomography
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A computer-aided diagnostic system for liver tumor detection using modified U-Net architecture
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An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning
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Robust extraction for low-contrast liver tumors using modified adaptive likelihood estimation
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Automatic segmentation of liver tumors from multiphase contrast-enhanced CT images based on FCNs
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ZCP-UNet: A hybrid attention–convolutional network for liver tumor segmentation
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SAR-U-Net: Squeeze-and-excitation block and atrous spatial pyramid pooling based residual U-Net for automatic liver segmentation in Computed Tomography
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2022.105693openalex-snapshot
FCRB U-Net: A novel fully connected residual block U-Net for fetal cerebellum ultrasound image segmentation
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2024.108625openalex-snapshot
ResTransUnet: An effective network combined with Transformer and U-Net for liver segmentation in CT scans
inferred← citeshttps://doi.org/10.1016/j.ibmed.2026.100376openalex-snapshot
LiT-HiSegFormer-Net: A hybrid SegFormer network with Bi-level context fusion for liver and tumor segmentation in CT images
inferred← citeshttps://doi.org/10.1016/j.jrras.2024.101182openalex-snapshot
Dynamic RU-Next: Advancing liver and tumor segmentation with enhanced deep learning architecture
inferred← citeshttps://doi.org/10.1016/j.media.2022.102680openalex-snapshot
The Liver Tumor Segmentation Benchmark (LiTS)
inferred← citeshttps://doi.org/10.1016/j.neucom.2024.128014openalex-snapshot
CoProLITE: Constrained Proxy Learning for lIver and hepaTic lesion sEgmentation
inferred← citeshttps://doi.org/10.1038/s41598-018-33860-7openalex-snapshot
Automatic liver tumor segmentation in CT with fully convolutional neural networks and object-based postprocessing
inferred← citeshttps://doi.org/10.1038/s41598-025-94195-8openalex-snapshot
A paired multi-scale attention network for liver tumor segmentation in 99mTc-MAA SPECT/CT imaging
inferred← citeshttps://doi.org/10.1088/1742-6596/2004/1/012007openalex-snapshot
Improved 3D fully convolutional network based on squeeze-excitation method for liver segmentation
inferred← citeshttps://doi.org/10.1109/access.2019.2959611openalex-snapshot
Prostate MR Image Segmentation With Self-Attention Adversarial Training Based on Wasserstein Distance
inferred← citeshttps://doi.org/10.1109/asiancon62057.2024.10838029openalex-snapshot
The U-Net Model and it’s Variants: A Revolution in Medical Image Segmentation
inferred← citeshttps://doi.org/10.1109/bibm47256.2019.8983266openalex-snapshot
CU-Net: Cascaded U-Net Model for Automated Liver and Lesion Segmentation and Summarization
inferred← citeshttps://doi.org/10.1109/cvprw.2012.6239244openalex-snapshot
Automatic detection of liver lesion from 3D computed tomography images
inferred← citeshttps://doi.org/10.1109/ecmi68341.2026.11603024openalex-snapshot
Automated Liver Lesions Segmentation for Improved Selection of ROI
inferred← citeshttps://doi.org/10.1109/embc46164.2021.9630913openalex-snapshot
TwinLiverNet: Predicting TACE Treatment Outcome from CT scans for Hepatocellular Carcinoma using Deep Capsule Networks
inferred← citeshttps://doi.org/10.1109/icces.2010.5674853openalex-snapshot
Fully automatic liver tumor segmentation from abdominal CT scans
inferred← citeshttps://doi.org/10.1109/iccs45141.2019.9065850openalex-snapshot
Survey on Automatic Liver Segmentation Techniques from Abdominal CT Images
inferred← citeshttps://doi.org/10.1109/icip.2015.7351504openalex-snapshot
Segmentation of liver tumor via nonlocal active contours
inferred← citeshttps://doi.org/10.1109/iembs.2011.6091484openalex-snapshot
Interactive liver tumor segmentation from ct scans using support vector classification with watershed
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/isbi45749.2020.9098656openalex-snapshot
Hybrid Cascaded Neural Network for Liver Lesion Segmentation
inferred← citeshttps://doi.org/10.1109/isda.2010.5687080openalex-snapshot
A novel fully automatic technique for liver tumor segmentation from CT scans with knowledge-based constraints
inferred← citeshttps://doi.org/10.1109/isvc.2010.5656429openalex-snapshot
Segmentation of liver tumor using HMRF-EM algorithm with Bootstrap resampling
inferred← citeshttps://doi.org/10.1109/jbhi.2021.3091900openalex-snapshot
DeepRecS: From RECIST Diameters to Precise Liver Tumor Segmentation
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/tbme.2019.2894123openalex-snapshot
Liver Extraction Using Residual Convolution Neural Networks From Low-Dose CT Images
inferred← citeshttps://doi.org/10.1109/tcyb.2015.2489719openalex-snapshot
Inference With Collaborative Model for Interactive Tumor Segmentation in Medical Image Sequences
inferred← citeshttps://doi.org/10.1109/tmi.2012.2196285openalex-snapshot
Interactive Lesion Segmentation with Shape Priors From Offline and Online Learning
inferred← citeshttps://doi.org/10.1109/tmi.2012.2211887openalex-snapshot
Tumor Burden Analysis on Computed Tomography by Automated Liver and Tumor 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/10.1109/wcitca.2015.7367076openalex-snapshot
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inferred← citeshttps://doi.org/10.1117/12.2217778openalex-snapshot
A general approach to liver lesion segmentation in CT images
inferred← citeshttps://doi.org/10.1118/1.3654162openalex-snapshot
Automated 3‐dimensional segmentation of pelvic lymph nodes in magnetic resonance images
inferred← citeshttps://doi.org/10.1134/s1054661819020056openalex-snapshot
Detection of Liver Cancer Using Modified Fuzzy Clustering and Decision Tree Classifier in CT Images
inferred← citeshttps://doi.org/10.1145/3502827.3502832openalex-snapshot
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Automated and robust organ segmentation for 3D-based internal dose calculation
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Hepatic tumors diagnosis system based on fuzzy c-means using computed tomography images
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Image Segmentation of Lesions in Wireless Capsule Endoscopy Based on Mask R-CNN
inferred← citeshttps://doi.org/10.3389/fonc.2021.697178openalex-snapshot
Effects of Multiple Filters on Liver Tumor Segmentation From CT Images
inferred← citeshttps://doi.org/10.3390/app10113794openalex-snapshot
3D Liver and Tumor Segmentation with CNNs Based on Region and Distance Metrics
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Fully Automatic Liver and Tumor Segmentation from CT Image Using an AIM-Unet
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RMCNet: A Liver Cancer Segmentation Network Based on 3D Multi-Scale Convolution, Attention, and Residual Path
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The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma
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Energy-Based MRI Semantic Augmented Segmentation for Unpaired CT Images
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A Residual-Learning-Based Multi-Scale Parallel-Convolutions- Assisted Efficient CAD System for Liver Tumor Detection
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A Novel Hybrid U-Net with Custom Triplet Flatten Loss Function for Liver Lesion Detection
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