Slice correspondence estimation using SURF descriptors and context-based search for prostate whole-mount histology MRI registration.

Abstract

Registration of histopathology volumes to Magnetic Resonance Images(MRI) is a crucial step for finding correlations in Prostate Cancer (PCa) and assessing tumor agressivity. This paper proposes a two-stage framework aimed at registering both modalities. Firstly, Speeded-Up Robust Features (SURF) algorithm and a context-based search is used to automatically determine slice correspondences between MRI and histology volumes. This step initializes a multimodal nonrigid registration strategy, which allows to propagate histology slices to MRI. Evaluation was performed on 5 prospective studies using a slice index score and landmark distances. With respect to a manual ground truth, the first stage of the framework exhibited an average error of 1,54 slice index and 3,51 mm in the prostate specimen. The reconstruction of a three-dimensional Whole-Mount Histology (WMH) shows promising results aimed to perform later PCa pattern detection and staging.