Review on the Methods of Automatic Liver Segmentation from Abdominal Images

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Journal of Computer and Communications

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"Review on the Methods of Automatic Liver Segmentation from Abdominal Images"
written by Suhuai Luo, Xuechen Li, Jiaming Li,
published by Journal of Computer and Communications, Vol.2 No.2, 2014

has been cited by the following article(s):

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[1]	LiM-Net: Lightweight multi-level multiscale network with deep residual learning for automatic liver segmentation in CT images Biomedical Signal Processing and Control, 2023 DOI:10.1016/j.bspc.2022.104305

[2]	LiM-Net: Lightweight multi-level multiscale network with deep residual learning for automatic liver segmentation in CT images Biomedical Signal Processing and Control, 2023 DOI:10.1016/j.bspc.2022.104305

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[5]	Quantification of liver-Lung shunt fraction on 3D SPECT/CT images for selective internal radiation therapy of liver cancer using CNN-based segmentations and non-rigid registration Computer Methods and Programs in Biomedicine, 2023 DOI:10.1016/j.cmpb.2023.107453

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[17]	Research Anthology on Improving Medical Imaging Techniques for Analysis and Intervention 2022 DOI:10.4018/978-1-6684-7544-7.ch041

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[19]	Deep Learning for Automated Normal Liver Volume Estimation Radiology, 2022 DOI:10.1148/radiol.2021212010

[20]	RMS-UNet: Residual multi-scale UNet for liver and lesion segmentation Artificial Intelligence in Medicine, 2022 DOI:10.1016/j.artmed.2021.102231

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[28]	MS-UNet: A multi-scale UNet with feature recalibration approach for automatic liver and tumor segmentation in CT images Computerized Medical Imaging and Graphics, 2021 DOI:10.1016/j.compmedimag.2021.101885

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[38]	Survey on Liver Tumour Resection Planning System: Steps, Techniques, and Parameters Journal of Digital Imaging, 2019 DOI:10.1007/s10278-019-00262-8

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[42]	Spleen segmentation in MRI sequence images using template matching and active contours Procedia Computer Science, 2018 DOI:10.1016/j.procs.2018.04.180

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[60]	Automated Liver Tumor Detection Using Markov Random Field Segmentation Procedia Technology, 2016 DOI:10.1016/j.protcy.2016.05.126

[61]	Smoothed shock filtered defuzzification with Zernike moments for liver tumor extraction in MR images 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), 2015 DOI:10.1109/IPTA.2015.7367088

[62]	Automatic volumetric localization of the liver in abdominal CT scans using low level processing and shape priors 2015 IEEE International Conference on Signal and Image Processing Applications (ICSIPA), 2015 DOI:10.1109/ICSIPA.2015.7412230

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[65]	Accurate object segmentation using novel active shape and appearance models based on support vector machine learning 2014 International Conference on Audio, Language and Image Processing, 2014 DOI:10.1109/ICALIP.2014.7009813

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