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.
Autores
Quiñonez, EEE; Sánchez, YLN; Cifuentes-Gonzalez, AL
Título
Panorama de la competitividad y politicas publi- cas en el sector agropecuario frente a la crisis del COVID-19 en Colombia Overview of Competitiveness and Public Policies in the Agricultural Sector in the Face of the COVID-19 Crisis in Colombia
Afiliaciones
Año
2023
DOI
10.25100/iyc.v25i3.12902
Tipo de acceso abierto
hybrid
Referencia
WOS:001030677200003
Artículo obtenido de:
WOS
Fuente
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