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Effects of Enhancement on Deep Learning Based Hepatic Vessel Segmentation

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What this work says

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

inferredcites →https://doi.org/10.1002/cyto.a.20022openalex-snapshot
Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images
inferredcites →https://doi.org/10.1002/mp.12560openalex-snapshot
Comparison of vessel enhancement algorithms applied to time‐of‐flight MRA images for cerebrovascular segmentation
inferredcites →https://doi.org/10.1007/978-3-030-31321-0_23openalex-snapshot
Impact of Enhancement for Coronary Artery Segmentation Based on Deep Learning Neural Network
inferredcites →https://doi.org/10.1007/978-3-319-16811-1_40openalex-snapshot
Automatic Wrinkle Detection Using Hybrid Hessian Filter
inferredcites →https://doi.org/10.1007/978-3-319-46723-8_49openalex-snapshot
3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation
inferredcites →https://doi.org/10.1007/978-3-642-33612-6_18openalex-snapshot
Evaluation of Diffusion Filters for 3D CTA Liver Vessel Enhancement
inferredcites →https://doi.org/10.1007/978-981-10-5122-7openalex-snapshot
EMBEC & NBC 2017
inferredcites →https://doi.org/10.1007/978-981-10-5122-7_206openalex-snapshot
Portal and Hepatic Vein Segmentation with Leak Restriction: A Pilot Study
inferredcites →https://doi.org/10.1007/bfb0056181openalex-snapshot
Medical Image Computing and Computer-Assisted Intervention — MICCAI’98
inferredcites →https://doi.org/10.1007/s10585-015-9719-0openalex-snapshot
Nationwide trends in incidence, treatment and survival of colorectal cancer patients with synchronous metastases
inferredcites →https://doi.org/10.1016/b978-0-12-382040-2.10012-7openalex-snapshot
Radiology
inferredcites →https://doi.org/10.1016/j.bspc.2019.101740openalex-snapshot
Enhancing detection of retinal blood vessels by combined approach of DWT, Tyler Coye and Gamma correction
inferredcites →https://doi.org/10.1016/j.cmpb.2018.10.010openalex-snapshot
Accurate liver vessel segmentation via active contour model with dense vessel candidates
inferredcites →https://doi.org/10.1016/j.compmedimag.2020.101783openalex-snapshot
Vessel segmentation using multiscale vessel enhancement and a region based level set model
inferredcites →https://doi.org/10.1016/j.ejmp.2016.04.003openalex-snapshot
Liver vessel segmentation based on extreme learning machine
inferredcites →https://doi.org/10.1016/j.media.2017.07.005openalex-snapshot
A survey on deep learning in medical image analysis
inferredcites →https://doi.org/10.1016/j.media.2022.102680openalex-snapshot
The Liver Tumor Segmentation Benchmark (LiTS)
inferredcites →https://doi.org/10.1016/j.mri.2012.05.001openalex-snapshot
3D Slicer as an image computing platform for the Quantitative Imaging Network
inferredcites →https://doi.org/10.1016/s1361-8415(98)80009-1openalex-snapshot
Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images
inferredcites →https://doi.org/10.1049/iet-ipr.2018.0162openalex-snapshot
Retracted: Robust retinal vessel segmentation using vessels location map and Frangi enhancement filter
inferredcites →https://doi.org/10.1109/access.2019.2950228openalex-snapshot
Impact of Image Enhancement Technique on CNN Model for Retinal Blood Vessels Segmentation
inferredcites →https://doi.org/10.1109/embc.2016.7591443openalex-snapshot
ITK-SNAP: An interactive tool for semi-automatic segmentation of multi-modality biomedical images
inferredcites →https://doi.org/10.1109/icpr48806.2021.9412362openalex-snapshot
Vesselness Filters: A Survey with Benchmarks Applied to Liver Imaging
inferredcites →https://doi.org/10.1109/sceecs.2016.7509287openalex-snapshot
Brightness preserving contrast enhancement of medical images using adaptive gamma correction and homomorphic filtering
inferredcites →https://doi.org/10.1109/tip.2012.2185938openalex-snapshot
Contrast-Independent Curvilinear Structure Detection in Biomedical Images
inferredcites →https://doi.org/10.1109/tmi.2002.801166openalex-snapshot
Analysis of vasculature for liver surgical planning
inferredcites →https://doi.org/10.1117/12.2006779openalex-snapshot
3D multiscale vessel enhancement based centerline extraction of blood vessels
inferredcites →https://doi.org/10.1117/12.2006811openalex-snapshot
Hepatic vein segmentation using wavefront propagation and multiscale vessel enhancement
inferredcites →https://doi.org/10.1117/12.2081147openalex-snapshot
Beyond Frangi: an improved multiscale vesselness filter
inferredcites →https://doi.org/10.1166/jmihi.2017.1998openalex-snapshot
Diabetic Retinopathy Retinal Image Enhancement Based on Gamma Correction
inferredcites →https://doi.org/10.1533/9780857099242.270openalex-snapshot
Using instruments to quantify colour
inferredcites →https://doi.org/10.3322/caac.21332openalex-snapshot
Cancer statistics, 2016
inferredcites →https://doi.org/10.3322/caac.21660openalex-snapshot
Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries
inferredcites →https://doi.org/10.7717/peerj.453openalex-snapshot
scikit-image: image processing in Python

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.4784957openalex-extracted
Segmentation of Cerebral Vessels in Mouse Tof-Mra Via an Attention-Based Multi-Kernel Convolutional Network with Multiscale Vessel Enhancement
inferred← citeshttps://doi.org/10.1007/978-3-032-17611-0_3openalex-snapshot
Do Segmentation Models Understand Vascular Structure? A Blob-Based Saliency Framework
inferred← citeshttps://doi.org/10.1007/s00464-023-10166-yopenalex-snapshot
Process model analysis of parenchyma sparing laparoscopic liver surgery to recognize surgical steps and predict impact of new technologies
inferred← citeshttps://doi.org/10.1007/s11760-025-04586-7openalex-snapshot
Automatic extraction of liver vessel centerlines and topology guided by multi-scale vascular features
inferred← citeshttps://doi.org/10.1016/j.artmed.2022.102331openalex-snapshot
Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions
inferred← citeshttps://doi.org/10.1016/j.bspc.2025.107822openalex-snapshot
Challenges and artificial intelligence solutions for clinically optimal hepatic venous vessel segmentation
inferred← citeshttps://doi.org/10.1016/j.compbiomed.2025.110530openalex-snapshot
D2-RD-UNet: A dual-stage dual-class framework with connectivity correction for hepatic vessels segmentation
inferred← citeshttps://doi.org/10.1016/j.engappai.2022.105532openalex-snapshot
Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations
inferred← citeshttps://doi.org/10.1016/j.knosys.2025.114686openalex-snapshot
Continuous and complete liver vessel segmentation with graph-attention guided diffusion
inferred← citeshttps://doi.org/10.1038/s41598-023-27671-8openalex-snapshot
Region-adaptive magnetic resonance image enhancement for improving CNN-based segmentation of the prostate and prostatic zones
inferred← citeshttps://doi.org/10.1109/access.2021.3107473openalex-snapshot
Cross-Modality Guided Contrast Enhancement for Improved Liver Tumor Image Segmentation
inferred← citeshttps://doi.org/10.1109/bibm58861.2023.10385519openalex-snapshot
CPSS-Net: A Cross-pseudo Semi-supervised Network for Liver Vessel Segmentation from CTA Images
inferred← citeshttps://doi.org/10.1109/gseact68539.2026.11619984openalex-snapshot
Exploring Cross Modal Learning Techniques in Medical Image Analysis for Enhanced Diagnostic Accuracy
inferred← citeshttps://doi.org/10.1109/ictacs62700.2024.10841192openalex-snapshot
Extensive Analysis of Deep Learning Algorithms for Automatic Detection of Liver Anomalies Using MRI and CT Scanned Images
inferred← citeshttps://doi.org/10.1109/isbi56570.2024.10635696openalex-snapshot
Deep Vessel Segmentation Based on a New Combination of Vesselness Filters
inferred← citeshttps://doi.org/10.1109/metroxraine58569.2023.10405781openalex-snapshot
Automatic Liver Vessels Segmentation Using ResDense UNet And An Appropriate Preprocessing Pipeline
inferred← citeshttps://doi.org/10.1109/nssmicrtsd49126.2023.10337892openalex-snapshot
Self super resolution for hepatic vessel CT segmentation
inferred← citeshttps://doi.org/10.1109/sisy52375.2021.9582533openalex-snapshot
Augmented/Mixed Reality Technologies Supporting Digital Surgery
inferred← citeshttps://doi.org/10.1109/tmi.2022.3192679openalex-snapshot
A Benchmark Framework for Multiregion Analysis of Vesselness Filters
inferred← citeshttps://doi.org/10.1177/20552076241293498openalex-snapshot
Progress and clinical translation in hepatocellular carcinoma of deep learning in hepatic vascular segmentation
inferred← citeshttps://doi.org/10.2139/ssrn.5744654openalex-snapshot
dynamic scattering medium imaging based on multi-stage preprocessing and deep learning
inferred← citeshttps://doi.org/10.21553/rev-jec.315openalex-snapshot
Impact of Image Denoising Techniques on CNN-based Liver Vessel Segmentation using Synthesis Low-dose Contrast Enhanced CT Images
inferred← citeshttps://doi.org/10.2352/ei.2022.34.10.ipas-344openalex-snapshot
Contrast enhancement: Cross-modal learning approach for medical images
inferred← citeshttps://doi.org/10.29137/umagd.1203617openalex-snapshot
Threshold Based Image Enhancement Method for Low Contrast X-Ray Images Using CLAHE
inferred← citeshttps://doi.org/10.32604/cmes.2024.056424openalex-snapshot
Semantic Segmentation of Lumbar Vertebrae Using Meijering U-Net (MU-Net) on Spine Magnetic Resonance Images
inferred← citeshttps://doi.org/10.3390/app13010548openalex-snapshot
Automatic Hepatic Vessels Segmentation Using RORPO Vessel Enhancement Filter and 3D V-Net with Variant Dice Loss Function
inferred← citeshttps://doi.org/10.3390/bioengineering13070728openalex-snapshot
Automated Craniofacial Artery Segmentation with Vessel Enhancement-Guided Deep Learning
inferred← citeshttps://doi.org/10.3390/cancers15225352openalex-snapshot
Towards Realistic 3D Models of Tumor Vascular Networks
inferred← citeshttps://doi.org/10.3390/diagnostics15020118openalex-snapshot
TransRAUNet: A Deep Neural Network with Reverse Attention Module Using HU Windowing Augmentation for Robust Liver Vessel Segmentation in Full Resolution of CT Images
inferred← citeshttps://doi.org/10.3390/electronics10222855openalex-snapshot
Cross-Modal Guidance Assisted Hierarchical Learning Based Siamese Network for MR Image Denoising
inferred← citeshttps://doi.org/10.3390/electronics11030305openalex-snapshot
Tutorial on the Use of Deep Learning in Diffuse Optical Tomography
inferred← citeshttps://doi.org/10.3390/electronics12081815openalex-snapshot
Novel Design of Industrial Real-Time CT System Based on Sparse-View Reconstruction and Deep-Learning Image Enhancement
inferred← citeshttps://doi.org/10.3390/electronics12194159openalex-snapshot
High-Level Hessian-Based Image Processing with the Frangi Neuron
inferred← citeshttps://doi.org/10.3390/jcm15156077openalex-snapshot
Towards Fixing Vessel Segmentation Breakage: A Systematic Review
inferred← citeshttps://doi.org/10.3390/photonics11090887openalex-snapshot
Long-Range Imaging through Scattering Media Using Deep Learning
inferred← citeshttps://doi.org/10.3390/s24061880openalex-snapshot
A Vascular Feature Detection and Matching Method Based on Dual-Branch Fusion and Structure Enhancement
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A Vehicle Density Estimation Traffic Light Control System Using a Two-Dimensional Convolution Neural Network
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Two-Stage Contrast Enhancement Method for Liver Tumor Segmentation
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