ArticleBioMed research international2017
3D Liver Tumor Segmentation in CT Images Using Improved Fuzzy
Article in BioMed research international, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Chemotherapeutic nanomaterials in tumor boundary delineation: Prospects for effective tumor treatment.Acta pharmaceutica Sinica. B · 2022Review
- Compound W-Net with Fully Accumulative Residual Connections for Liver Segmentation Using CT Images.Computational and mathematical methods in medicine · 2022Article
- LLRHNet: Multiple Lesions Segmentation Using Local-Long Range Features.Frontiers in neuroinformatics · 2022Article
- Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters.Journal of digital imaging · 2020Review
- Channel-Unet: A Spatial Channel-Wise Convolutional Neural Network for Liver and Tumors Segmentation.Frontiers in genetics · 2019Article
- Dynamic Regulation of Level Set Parameters Using 3D Convolutional Neural Network for Liver Tumor Segmentation.Journal of healthcare engineering · 2019Article
- Robust extraction for low-contrast liver tumors using modified adaptive likelihood estimation.International journal of computer assisted radiology and surgery · 2018Article
- An automated liver tumour segmentation from abdominal CT scans for hepatic surgical planning.International journal of computer assisted radiology and surgery · 2018Article
- Thresholding for Medical Image Segmentation for Cancer using Fuzzy Entropy with Level Set Algorithm.Open medicine (Warsaw, Poland) · 2018Article
- A Unified Level Set Framework Combining Hybrid Algorithms for Liver and Liver Tumor Segmentation in CT Images.BioMed research international · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Three-dimensional (3D) liver tumor segmentation from Computed Tomography (CT) images is a prerequisite for computer-aided diagnosis, treatment planning, and monitoring of liver cancer. Despite many years of research, 3D liver tumor segmentation remains a challenging task. In this paper, an efficient semiautomatic method was proposed for liver tumor segmentation in CT volumes based on improved fuzzy
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Registered trials
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