Uterine fibroid segmentation and volume measurement on MRI

Abstract

Uterine leiomyomas are the most common pelvic tumors in females. The efficacy of medical treatment is gauged by shrinkage of the size of these tumors. In this paper, we present a method to robustly segment the fibroids on MRI and accurately measure the 3D volume. Our method is based on a combination of fast marching level set and Laplacian level set. With a seed point placed inside the fibroid region, a fast marching level set is first employed to obtain a rough segmentation, followed by a Laplacian level set to refine the segmentation. We devised a scheme to automatically determine the parameters for the level set function and the sigmoid function based on pixel statistics around the seed point. The segmentation is conducted on three concurrent views (axial, coronal and sagittal), and a combined volume measurement is computed to obtain a more reliable measurement. We carried out extensive tests on 13 patients, 25 MRI studies and 133 fibroids. The segmentation result was validated against manual segmentation defined by experts. The average segmentation sensitivity (true positive fraction) among all fibroids was 84.6%, and the average segmentation specificity (1-false positive fraction) was 84.3%.