Research on Tumors Segmentation based on Image Enhancement Method

TL;DR

This paper introduces a novel image enhancement technique combined with deep learning and multi-scale analysis to improve the accuracy and efficiency of liver tumor segmentation in medical images, aiding surgical planning.

Contribution

The study presents a new image enhancement algorithm integrated with a deep learning segmentation network and multi-scale analysis, enhancing tumor detection accuracy over traditional methods.

Findings

Significant improvement in tumor segmentation accuracy.

Enhanced recall rate for tumor detection.

Effective application of image enhancement in medical imaging.

Abstract

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.