Niño, S.B.; Bernardino, J.; Domingues, I. Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective. Sensors2024, 24, 1752.
Niño, S.B.; Bernardino, J.; Domingues, I. Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective. Sensors 2024, 24, 1752.
Niño, S.B.; Bernardino, J.; Domingues, I. Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective. Sensors2024, 24, 1752.
Niño, S.B.; Bernardino, J.; Domingues, I. Algorithms for Liver Segmentation in Computed Tomography Scans: A Historical Perspective. Sensors 2024, 24, 1752.
Abstract
Oncology has emerged as a crucial field of study and treatment 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 on computed tomography scans, and evaluates their accuracy and efficiency. It is noteworthy that although studies have been conducted on liver segmentation in computed tomography scans, they often lack an intuitive and visual component that allows healthcare professionals to manipulate and observe the results obtained, thereby limiting interaction with the outcomes. From the literature review, challenges such as under-segmentation, over-segmentation, and poor boundary detection, as well as the selection of methods to improve the accuracy and efficiency of liver segmentation in computed tomography scanners, are highlighted as needs to be addressed. The importance of future research in understanding the essential features for the study, generating more datasets, improving segmentation efficiency, and developing lightweight artificial intelligence frameworks for liver segmentation is outlined.
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
