Liver Lesion Segmentation Using Deep Learning Models
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2022, Vol 1, Issue 1
Abstract
An estimated 9.6 million deaths, or one in every six deaths, were attributed to cancer in 2018, making it the second highest cause of death worldwide. Men are more likely to develop lung, prostate, colorectal, stomach, and liver cancer than women, who are more likely to develop breast, colorectal, lung, cervical, and thyroid cancer. The primary goals of medical image segmentation include studying anatomical structure, identifying regions of interest (RoI), and measuring tissue volume to track tumor growth. It is crucial to diagnose and treat liver lesions quickly in order to stop the tumor from spreading further. Deep learning model-based liver segmentation has become very popular in the field of medical image analysis. This study explores various deep learning-based liver lesion segmentation algorithms and methodologies. Based on the developed models, the performance, and their limitations of these methodologies are contrasted. In the end, it was concluded that small size lesion segmentation, in particular, is still an open research subject for computer-aided systems of liver lesion segmentation, for there are still a number of technical issues that need to be resolved.
Authors and Affiliations
Aasia Rehman,Muheet Ahmed Butt,Majid Zaman
Keywords
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- EP ID EP731873
- DOI https://doi.org/10.56578/ataiml010108
- Views 72
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