Segmenting Hepatic Blood Vessels and Liver Tumors in CT Images: An Improved Evolutionary Algorithm Approach

Abstract

Among the many forms of cancer, liver tumours are among the most dangerous. Liver neoplasms can be effectively predicted, identified, and managed with the help of computer-aided technology and liver interventional surgery. Accurately understanding the morphological nature of the liver and its blood arteries is a crucial task. An essential part of medical analytic planning is the segmentation of liver tumours in CT scans. The enormous difficulty, however, lies in correctly identifying and segmenting the hepatic blood veins in CT scans. Finding and segmenting hepatic vessels manually in CT scans is an inconvenient and time-consuming process. In order to segment liver tumours, this study employs a variety of techniques to clean up the input images before feeding them into the STDCSL, an algorithm for short-term dense concat segmentation. Three parts make up the STDC-CT network: detail guidance, multi-scale contextual information, and small object attention extractor. Small affected area attention guides the merging of detailed and contextual information branches. This study proposes an improvement to the Barnacle Mating Optimizer (BMO), an evolutionary algorithm that takes its cues from nature, in order to fine-tune the STD-CSL parameters. Levy flight is used to enforce and replace the sperm cast equation, which improves the exploration phase of the original BMO. Next, the enhanced BMO (IBMO) is teamed up with the suggested STDCSL. The technology proves to be reliable and applicable to automatic analysis of liver tumours in everyday clinical practice, proving its generalizability. The method's great accuracy in stroke detection further supports its potential use as a clinical tool for preoperative clinical planning.