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Review
. 2024 Mar 8;24(6):1752.
doi: 10.3390/s24061752.

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective

Stephanie Batista Niño  1 , Jorge Bernardino  1   2 , Inês Domingues  1   3
Affiliations
Review

Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective

Stephanie Batista Niño et al. Sensors (Basel). .

Abstract

Oncology has emerged as a crucial field of study in the domain of medicine. Computed tomography has gained widespread adoption as a radiological modality for the identification and characterisation of pathologies, particularly in oncology, enabling precise identification of affected organs and tissues. However, achieving accurate liver segmentation in computed tomography scans remains a challenge due to the presence of artefacts and the varying densities of soft tissues and adjacent organs. This paper compares artificial intelligence algorithms and traditional medical image processing techniques to assist radiologists in liver segmentation in computed tomography scans and evaluates their accuracy and efficiency. Despite notable progress in the field, the limited availability of public datasets remains a significant barrier to broad participation in research studies and replication of methodologies. Future directions should focus on increasing the accessibility of public datasets, establishing standardised evaluation metrics, and advancing the development of three-dimensional segmentation techniques. In addition, maintaining a collaborative relationship between technological advances and medical expertise is essential to ensure that these innovations not only achieve technical accuracy, but also remain aligned with clinical needs and realities. This synergy ensures their applicability and effectiveness in real-world healthcare environments.

Keywords: artificial intelligence; computed tomography; hepatic pathologies; liver segmentation.

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Conflict of interest statement

The authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
Distribution of the retrieved papers over the years.
Figure 2
Figure 2
Characterisation of the selected papers.
Figure 3
Figure 3
Selected paper distribution according to the categories of image segmentation techniques.
Figure 4
Figure 4
Summary of the quantitative results presented in the reviewed documents.
Figure 5
Figure 5
Percentage of studies according to their level of automation (semi-automatic, fully automatic, or both).
Figure 6
Figure 6
Percentage of studies according to their image dimensionality (2D vs. 3D).
Figure 7
Figure 7
Percentage of database types.
Figure 8
Figure 8
Number of studies using each public dataset.
See this image and copyright information in PMC

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