An Efficient Framework for Accurate Liver Segmentation in Abdominal CT Images with Low Knowledge Requirement

• Published in: Electronics (Basel) Vol. 11; no. 24; p. 4182
• Main Authors: Yu, Shao-Qian, Zhou, Tao, Wen, Yan-Hua, Li, Chuang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.12.2022
• Subjects: Abdomen; Accuracy; Algorithms; Computed tomography; CT imaging; Datasets; Diagnosis; Diagnostic imaging; Image processing; Image segmentation; Liver; Liver cancer; Medical examination; Medical imaging; Medical research; Methods; Optimization; Performance evaluation; Reconstruction; Tumors; China
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11244182

Liver segmentation from abdominal computed tomography (CT) images is a primary step in the diagnosis and treatment of liver cancer, but previous liver segmentation methods have the problems of excessive demand for prior knowledge, under- and oversegmentation, and boundary leakage. To solve these problems, this paper proposes a new method of liver segmentation to assist doctors in medical judgment. Firstly, a liver reconstruction algorithm is proposed to obtain the approximate initial region of the liver, which reduces the requirement of prior knowledge and can reconstruct the liver region closer to the liver boundary. Then, we refine the edge of the liver region based on the reaction diffusion level set (RD level set). This edge refinement method can effectively deal with the weak boundary problem, prevent under- and oversegmentation, and obtain a more accurate liver region. Our method is verified on the clinical and public datasets, respectively. The segmentation results in terms of mean VOE, RVD, ASD, RMSD, and MSD on dataset Sliver07 are 5.1%, −0.1%, 1.0 mm, 2.0 mm, and 18.2 mm, and on dataset 3Dircadb are 8.1%, −0.2%, 1.5 mm, 2.4 mm, and 20.8 mm, respectively. Compared with the previous algorithms, the experiment results show that this method has a great improvement in accuracy with less prior knowledge. The liver reconstruction algorithm proposed in this paper can obtain a more accurate initial liver region, reduce the requirement for prior knowledge, and reduce time costs compared with the level set algorithm.