Facilitating Manual Segmentation of 3D Datasets Using Contour And Intensity Guided Interpolation

Abstract

Manual segmentation of anatomical structures in 3D imaging datasets is a highly time-consuming process. This process can be sped up using interslice interpolation techniques, which require only a small subset of slices to be manually segmented. In this paper, we propose a two-step interpolation approach that utilizes a “binary weighted averaging” algorithm to interpolate contour information, and the random forest framework to perform intensity-based label classification. We present the results of experiments performed in the context of hippocampal segmentations in ex vivo MRI scans. Compared to the random walker algorithm and morphology-based interpolation, the proposed method produces more accurate segmentations and smoother 3D reconstructions.