Artículo

Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis: Neural Network-based Methods for Liver Semantic Segmentation

Resumen

The use of artificial intelligence (AI) in the segmentation of liver structures in medical images has become a popular research focus in the past half-decade. The performance of AI tools in screening for this task may vary widely and has been tested in the literature in various datasets. However, no scientometric report has provided a systematic overview of this scientific area. This article presents a systematic and bibliometric review of recent advances in neuronal network modeling approaches, mainly of deep learning, to outline the multiple research directions of the field in terms of algorithmic features. Therefore, a detailed systematic review of the most relevant publications addressing fully automatic semantic segmenting liver structures in Computed Tomography (CT) images in terms of algorithm modeling objective, performance benchmark, and model complexity is provided. The review suggests that fully automatic hybrid 2D and 3D networks are the top performers in the semantic segmentation of the liver. In the case of liver tumor and vasculature segmentation, fully automatic generative approaches perform best. However, the reported performance benchmark indicates that there is still much to be improved in segmenting such small structures in high-resolution abdominal CT scans.
Delmoral, Jessica C. (57200918331); R.S. Tavares, João Manuel (57225852141)
Semantic Segmentation of CT Liver Structures: A Systematic Review of Recent Trends and Bibliometric Analysis: Neural Network-based Methods for Liver Semantic Segmentation
2024
10.1007/s10916-024-02115-6
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85206276786&doi=10.1007%2fs10916-024-02115-6&partnerID=40&md5=e1a4f5e58e18f37fe43ec8c80725857a
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, Porto, 4200-465, Portugal; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, Porto, 4200-465, Portugal
All Open Access; Hybrid Gold Open Access
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