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

L. Guzmán (1) , F. Commandeur (1) , Oscar Acosta (1) , A. Simon (1) , A. Fautrel (2) , N. Rioux-Leclercq (3) , E. Romero , R. Mathieu (4) , R. de Crevoisier (5, 1)

1 LTSI - Laboratoire Traitement du Signal et de l'Image
2 H2P2 - H2P2 - Histo Pathologie Hight Precision
3 Service d'anatomie et cytologie pathologiques [Rennes] = Anatomy and Cytopathology [Rennes]
4 Service d'urologie [Rennes] = Urology [Rennes]
5 Service de radiothérapie

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. © 2016 IEEE.

Domains: Life Sciences [q-bio]; Bioengineering

L. Guzmán, F. Commandeur, Oscar Acosta, A. Simon, A. Fautrel, et al.. Slice correspondence estimation using SURF descriptors and context-based search for prostate whole-mount histology MRI registration. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2016, Aug 2016, Orlando, United States. pp.1163--1166, ⟨10.1109/EMBC.2016.7590911⟩. ⟨hal-01484705⟩