Fully automatic 3D segmentation measurements of human liver vessels from contrast-enhanced CT

Abstract

Aim of the present work was to evaluate the performance of a novel fully automatic algorithm for 3D segmentation and volumetric reconstruction of liver vessel network from contrast-enhanced computed tomography (CECT) datasets acquired during routine clinical activity. Three anonymized CECT datasets were randomly collected and were automatically analyzed by the new vessel segmentation algorithm, whose parameter configuration had been previously optimized on a phantom model. The same datasets were also manually segmented by an experienced operator that was blind with respect to algorithm outcome. Automatic segmentation accuracy was quantitatively assessed for both single 2D slices and 3D reconstruction of the vessel network, accounting manual segmentation results as the reference “ground truth”. Adopted evaluation framework included the following two groups of calculations: 1) for 3D vessel network, sensitivity in vessel detection was quantified as a function of both vessel diameter and vessel order; 2) for vessel images on 2D slices, dice similarity coefficient (DSC), false positive ratio (FPR), false negative ratio (FNR), Bland-Altman plots and Pearson correlation coefficients were used to judge the correctness of single pixel classifications. Automatic segmentation resulted in a 3D vessel detection sensitivity of 100% for vessels larger than 1 mm in diameter, 64.6% for vessels in the range 0.5-1.0 mm and 27.8% for smaller vessels. An average area overlap of 99.1% was obtained between automatically and manually segmented vessel sections, with an average difference of 0.53 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> . The corresponding average values of FPR and FNR were 1.8% and 1.6%, respectively. Therefore, the tested method showed significant robustness and accuracy in automatic extraction of the liver vessel tree from CECT datasets. Although further verification studies on larger patient populations are required, the described algorithm has an exciting potential for supporting liver surgery planning and intraoperative resection guidance.