mlsanityData to 7 Oct 2026

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

arXiv 2103.06384 ↗First posted 10 Mar 2021eess.IV cs.CV cs.LGcited by 3

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Abstract (arXiv)

Machine learning and computer vision techniques have grown rapidly in recent years due to their automation, suitability, and ability to generate astounding results. Hence, in this paper, we survey the key studies that are published between 2014 and 2022, 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 they are further partitioned if the amount of work that falls 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 used excessively in literature are mentioned in our review, stressing their relevance 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 sooner than later.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 46 of its 195 references that are in the map. First posted on arXiv 10 Mar 2021.

Builds on

46 of 195 references.

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34 more
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Citing papers per year: 2023: 1; 2024: 2.

3 papers.

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