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dc.contributor.authorSurvarachakan, Shanmugapriya
dc.contributor.authorPrasad, Pravda Jith Ray
dc.contributor.authorNaseem, Rabia
dc.contributor.authorPerez de Frutos, Javier
dc.contributor.authorKumar, Rahul Prasanna
dc.contributor.authorLangø, Thomas
dc.contributor.authorAlaya Cheikh, Faouzi
dc.contributor.authorElle, Ole Jakob
dc.contributor.authorLindseth, Frank
dc.identifier.citationArtificial Intelligence in Medicine. 2022, 130, 102331.en_US
dc.description.abstractDeep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.en_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.titleDeep learning for image-based liver analysis — A comprehensive review focusing on malignant lesionsen_US
dc.title.alternativeDeep learning for image-based liver analysis — A comprehensive review focusing on malignant lesionsen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.rights.holder© 2022 The Authorsen_US
dc.source.journalArtificial Intelligence in Medicineen_US

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