A Survey on Liver Lesion Detection Using CT Images- Current Techniques and Future Perspectives
Keywords:
Computer aided diagnosis (CAD) system, Computed tomography, Contour-Based Segmentation , Deep learning,, Liver segmentation, , Machine Learning, Region Based Segmentation.Abstract
Liver disease represents a significant global public health issue, with early detection playing a crucial role in the effective treatment and management of the condition. The utilization of Computed Tomography (CT) imaging has become increasingly important in liver detection, providing high-resolution images that facilitate precise diagnosis. Clinicians commonly seek information regarding the liver's shape for treatment planning in order to minimize harm to surrounding healthy tissues and hepatic vessels, underscoring the importance of developing a geometric model of the liver. Various methods for liver image segmentation have been developed over time. This article presents a thorough examination of Traditional segmentation techniques such as thresholding, region-based growing algorithm, Contour-based algorithm, as well as Machine Learning (ML) and Deep Learning (DL) methods for Segmentation utilizing CT images, shedding light on the different techniques, algorithms, and challenges associated with this domain. We explore diverse approaches to liver segmentation, assess the efficacy of different methods using established metrics, and conclude by outlining potential avenues for future research.
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