Empirical greedy machine‐based automatic liver segmentation in CT images
Gajendra Kumar Mourya, Dinesh Bhatia, Akash Handique · IET Image Processing · 2020
Segmentation of the liver from 3D computed tomography volumes plays a significant role in trajectory development for computer‐assisted interventional surgery for the liver disease. Despite a lot of studies, liver segmentation remains a challenging task due to the lack of clear edges on most liver boundaries coupled with high variability of both anatomical and intensity patterns. In addition, there is a problem with the segmentation of the left portal vein, in which the size of this vein prominently estimates the liver tumour area. The empirical greedy machine is proposed to make the precise, automated segmentation of the liver as well as the left portal vein. In which the empirical robust nature trains the features of the liver proficiently thereby segmenting the liver from other organs without the omission of adjacent organs and liver lobe region. Hence this proposed method can achieve one of the highest accuracies compared to other segmentation methods and the performance is calculated using several parameters such as volumetric overlap error, relative absolute volume difference (RVD), average symmetric absolute surface distance (ASD), root mean square surface distance, maximum symmetric ASD.