  One of the most effective ways to treat liver cancer is to perform precise
liver resection surgery, the key step of which includes precise digital image
segmentation of the liver and its tumor. However, traditional liver parenchymal
segmentation techniques often face several challenges in performing liver
segmentation: lack of precision, slow processing speed, and computational
burden. These shortcomings limit the efficiency of surgical planning and
execution. In this work, the model initially describes in detail a new image
enhancement algorithm that enhances the key features of an image by adaptively
adjusting the contrast and brightness of the image. Then, a deep learning-based
segmentation network was introduced, which was specially trained on the
enhanced images to optimize the detection accuracy of tumor regions. In
addition, multi-scale analysis techniques have been incorporated into the
study, allowing the model to analyze images at different resolutions to capture
more nuanced tumor features. In the presentation of the experimental results,
the study used the 3Dircadb dataset to test the effectiveness of the proposed
method. The experimental results show that compared with the traditional image
segmentation method, the new method using image enhancement technology has
significantly improved the accuracy and recall rate of tumor identification.
