【Objective】 To investigate the application of interactive segmentation software in CT images of focal liver lesions. 【Methods】 This software is based on machine learning interactive segmentation. It achieves region segmentation by learning discriminant model for each pixel in the lesion or non-lesion discrimination. Segmentation efficiency was tested on the ideal single or sequential (Religion of Interest) ROI model established by Photoshop CS4

and segmentation in CT images of focal liver lesions was performed. 【Results】 In ideal single or sequential ROI model

this software would accurately segment all ROI area despite the gray values and diameter of the ROI. In single and/or sequential CT images

once the ablation lesion was selected

the software would accurately segment the region of the lesion and surrounding normal tissue. Compared with Photoshop manual segmentation

there is no statistically significant difference between the gold standard and the single or sequential segmentation (P > 0.05). 【Conclusion】 This interactive segmentation software based on discriminant model learning has been preliminary successfully applied to CT images of focal liver lesions.