Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations
<p dir="ltr">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 d...
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2023
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| _version_ | 1864513540496293888 |
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| author | Ayman Al-Kababji (16870080) |
| author2 | Faycal Bensaali (12427401) Sarada Prasad Dakua (14151789) Yassine Himeur (14158821) |
| author2_role | author author author |
| author_facet | Ayman Al-Kababji (16870080) Faycal Bensaali (12427401) Sarada Prasad Dakua (14151789) Yassine Himeur (14158821) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ayman Al-Kababji (16870080) Faycal Bensaali (12427401) Sarada Prasad Dakua (14151789) Yassine Himeur (14158821) |
| dc.date.none.fl_str_mv | 2023-01-01T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1016/j.engappai.2022.105532 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Automated_liver_tissues_delineation_techniques_A_systematic_survey_on_machine_learning_current_trends_and_future_orientations/24516493 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Biomedical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Liver Hepatic-tumors Hepatic-vessels Machine learning Survey Semantic segmentation |
| dc.title.none.fl_str_mv | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">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 the 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2022.105532" target="_blank">https://dx.doi.org/10.1016/j.engappai.2022.105532</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_f6ff0f4e0650ee4abda29883c3d3acf7 |
| identifier_str_mv | 10.1016/j.engappai.2022.105532 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24516493 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientationsAyman Al-Kababji (16870080)Faycal Bensaali (12427401)Sarada Prasad Dakua (14151789)Yassine Himeur (14158821)EngineeringBiomedical engineeringElectronics, sensors and digital hardwareInformation and computing sciencesArtificial intelligenceMachine learningLiverHepatic-tumorsHepatic-vesselsMachine learningSurveySemantic segmentation<p dir="ltr">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 the 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.</p><h2>Other Information</h2><p dir="ltr">Published in: Engineering Applications of Artificial Intelligence<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1016/j.engappai.2022.105532" target="_blank">https://dx.doi.org/10.1016/j.engappai.2022.105532</a></p>2023-01-01T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1016/j.engappai.2022.105532https://figshare.com/articles/journal_contribution/Automated_liver_tissues_delineation_techniques_A_systematic_survey_on_machine_learning_current_trends_and_future_orientations/24516493CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/245164932023-01-01T03:00:00Z |
| spellingShingle | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations Ayman Al-Kababji (16870080) Engineering Biomedical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Liver Hepatic-tumors Hepatic-vessels Machine learning Survey Semantic segmentation |
| status_str | publishedVersion |
| title | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations |
| title_full | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations |
| title_fullStr | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations |
| title_full_unstemmed | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations |
| title_short | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations |
| title_sort | Automated liver tissues delineation techniques: A systematic survey on machine learning current trends and future orientations |
| topic | Engineering Biomedical engineering Electronics, sensors and digital hardware Information and computing sciences Artificial intelligence Machine learning Liver Hepatic-tumors Hepatic-vessels Machine learning Survey Semantic segmentation |