Learning Based Random Walks for Automatic Liver Segmentation in CT Image

Zhang, Pan; Yang, Jian; Ai, Danni; Xie, Zhijie; Liu, Yue

doi:10.1007/978-3-662-47791-5_29

Cited by 2 publications

(2 citation statements)

References 10 publications

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“…In an ensemble of weak classifiers fashion, adaptive boosting (Ad-aBoost) is used to segment the liver with the aid of the random walks (RW) algorithm in Zhang et al (2015). A similar work uses the same combination with extra improvements on the RW algorithm in Zheng et al (2017b), and finally, a three-level ASM is guided by the AdaBoost algorithm in He et al (2016).…”

Section: Miscellaneous Supervisedmentioning

confidence: 99%

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Al‐Kababji

Bensaali

Dakua

et al. 2023

Engineering Applications of Artificial Intelligence

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Section: Miscellaneous Supervisedmentioning

confidence: 99%

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Al‐Kababji

Bensaali

Dakua

et al. 2023

Engineering Applications of Artificial Intelligence

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“…In an ensemble of weak classifiers fashion, adaptive boosting (AdaBoost) is used to segment the liver by the aid of random walks (RW) in [69]. A similar work uses the same combination with extra improvements on the RW algorithm in [70], and finally, a three-level ASM is guided by AdaBoost algorithm in [71].…”

Section: Miscellaneous Supervisedmentioning

confidence: 99%

Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

Al‐Kababji¹,

Bensaali²,

Dakua³

et al. 2021

Preprint

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There is no denying how machine learning and computer vision have grown in the recent years. Their highest advantages lie within their automation, suitability, and ability to generate astounding results in a matter of seconds in a reproducible manner. This is aided by the ubiquitous advancements reached in the computing capabilities of current graphical processing units and the highly efficient implementation of such techniques. Hence, in this paper, we survey the key studies that are published between 2014 and 2020, showcasing the different machine learning algorithms researchers have used to segment the liver, hepatic-tumors, and hepatic-vasculature structures. We divide the surveyed studies based on the tissue of interest (hepatic-parenchyma, hepatic-tumors, or hepatic-vessels), highlighting the studies that tackle more than one task simultaneously. Additionally, the machine learning algorithms are classified as either supervised or unsupervised, and further partitioned if the amount of works that fall under a certain scheme is significant. Moreover, different datasets and challenges found in literature and websites, containing masks of the aforementioned tissues, are thoroughly discussed, highlighting the organizers' original contributions, and those of other researchers. Also, the metrics that are used excessively in literature are mentioned in our review stressing their relevancy to the task at hand. Finally, critical challenges and future directions are emphasized for innovative researchers to tackle, exposing gaps that need addressing such as the scarcity of many studies on the vessels' segmentation challenge, and why their absence needs to be dealt with in an accelerated manner.

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Learning Based Random Walks for Automatic Liver Segmentation in CT Image

Cited by 2 publications

References 10 publications

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations

Automated liver tissues delineation based on machine learning techniques: A survey, current trends and future orientations

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