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Segmentation of Liver Metastases Using a Level Set Method with Spiral-Scanning Technique and Supervised Fuzzy Pixel Classification
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/978-3-540-49197-2_9openalex-snapshot
Computer-Aided Interactive Object Delineation Using an Intelligent Paintbrush Technique
inferredcites →https://doi.org/10.1007/b97541openalex-snapshot
Geometric Level Set Methods in Imaging, Vision, and Graphics
inferredcites →https://doi.org/10.1007/b98879openalex-snapshot
Level Set Methods and Dynamic Implicit Surfaces
inferredcites →https://doi.org/10.1007/bfb0034935openalex-snapshot
Computer-aided interactive object delineation using an intelligent paintbrush technique
inferredcites →https://doi.org/10.1007/s003300050180openalex-snapshot
Evaluation of manual vs semi-automated delineation of liver lesions on CT images
inferredcites →https://doi.org/10.1016/j.acra.2004.09.004openalex-snapshot
Comparison of treatment response classifications between unidimensional, bidimensional, and volumetric measurements of metastatic lung lesions on chest computed tomography1
inferredcites →https://doi.org/10.1017/cbo9780511596803openalex-snapshot
Fundamentals of Medical Imaging
inferredcites →https://doi.org/10.1093/jnci/djj403openalex-snapshot
Sometimes Size Doesn't Matter: Reevaluating RECIST and Tumor Response Rate Endpoints
inferredcites →https://doi.org/10.1109/42.938237openalex-snapshot
Automated segmentation of multiple sclerosis lesions by model outlier detection
inferredcites →https://doi.org/10.1109/cbms.2003.1212810openalex-snapshot
Volumetry of hepatic metastases in computed tomography using the watershed and active contour algorithms
inferredcites →https://doi.org/10.1109/iembs.2005.1615771openalex-snapshot
Hybrid Feature Vector Extraction in Unsupervised Learning Neural Classifier
inferredcites →https://doi.org/10.1109/ispa.2001.938658openalex-snapshot
Computing textural features based on co-occurrence matrix for infrared images
inferredcites →https://doi.org/10.1109/tsmc.1973.4309314openalex-snapshot
Textural Features for Image Classification
inferredcites →https://doi.org/10.1118/1.2799885openalex-snapshot
Segmentation of pulmonary nodules in three‐dimensional CT images by use of a spiral‐scanning technique
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/10.1148/radiol.2252011604openalex-snapshot
CT Tumor Measurement for Therapeutic Response Assessment: Comparison of Unidimensional, Bidimensional, and Volumetric Techniques—Initial Observations
inferredcites →https://doi.org/10.1148/radiology.202.3.9051014openalex-snapshot
Size quantification of liver metastases in patients undergoing cancer treatment: reproducibility of one-, two-, and three-dimensional measurements determined with spiral CT.
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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
inferred← citeshttps://doi.org/10.1007/978-3-642-28557-8_13openalex-snapshot
Liver Tumor Segmentation Using Kernel-Based FGCM and PGCM
inferred← citeshttps://doi.org/10.1007/s10462-011-9289-8openalex-snapshot
Performance divergence with data discrepancy: a review
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/s11554-016-0578-yopenalex-snapshot
Accelerated liver tumor segmentation in four-phase computed tomography images
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.2009.09.002openalex-snapshot
Semi-automatic level set segmentation of liver tumors combining a spiral-scanning technique with supervised fuzzy pixel classification
inferred← citeshttps://doi.org/10.1016/j.media.2014.04.001openalex-snapshot
Tumor sensitive matching flow: A variational method to detecting and segmenting perihepatic and perisplenic ovarian cancer metastases on contrast-enhanced abdominal CT
inferred← citeshttps://doi.org/10.1016/j.media.2022.102680openalex-snapshot
The Liver Tumor Segmentation Benchmark (LiTS)
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/10.1049/ipr2.12073openalex-snapshot
Analysis on segmentation and biomarker‐based approaches for liver cancer detection: A survey
inferred← citeshttps://doi.org/10.1088/0031-9155/60/16/6459openalex-snapshot
Metastatic liver tumour segmentation from discriminant Grassmannian manifolds
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/iembs.2011.6091280openalex-snapshot
Profiling the features of pre-segmented healthy liver CT scans: Towards fast detection of liver lesions in emergency scenario
inferred← citeshttps://doi.org/10.1109/ipta.2015.7367088openalex-snapshot
Smoothed shock filtered defuzzification with Zernike moments for liver tumor extraction in MR images
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/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.2022.3175461openalex-snapshot
Automatic Liver Tumor Segmentation on Dynamic Contrast Enhanced MRI Using 4D Information: Deep Learning Model Based on 3D Convolution and Convolutional LSTM
inferred← citeshttps://doi.org/10.2478/v10048-012-0023-8openalex-snapshot
Soft-tissues Image Processing: Comparison of Traditional Segmentation Methods with 2D active Contour Methods
inferred← citeshttps://doi.org/10.25148/etd.fi12050223openalex-snapshot
A Novel 3-D Segmentation Algorithm for Anatomic Liver and Tumor Volume Calculations for Liver Cancer Treatment Planning
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/cancers14246123openalex-snapshot
The Role of Radiomics and AI Technologies in the Segmentation, Detection, and Management of Hepatocellular Carcinoma
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