Semiautomatic registration of pre- and postbrain tumor resection laser range data: method and validation.

Ding S, Miga MI, Noble JH, Cao A, Dumpuri P, Thompson RC, Dawant BM
IEEE Trans Biomed Eng. 2009 56 (3): 770-80

PMID: 19272895 · PMCID: PMC2791533 · DOI:10.1109/TBME.2008.2006758

This paper presents a semiautomatic method for the registration of images acquired during surgery with a tracked laser range scanner (LRS). This method, which relies on the registration of vessels that can be visualized in the pre- and the postresection images, is a component of a larger system designed to compute brain shift that occurs during tumor resection cases. Because very large differences between pre- and postresection images are typically observed, the development of fully automatic methods to register these images is difficult. The method presented herein is semiautomatic and requires only the identification of a number of points along the length of the vessels. Vessel segments joining these points are then automatically identified using an optimal path finding algorithm that relies on intensity features extracted from the images. Once vessels are identified, they are registered using a robust point-based nonrigid registration algorithm. The transformation computed with the vessels is then applied to the entire image. This permits establishment of a complete correspondence between the pre- and post-3-D LRS data. Experiments show that the method is robust to operator errors in localizing homologous points and a quantitative evaluation performed on ten surgical cases shows submillimetric registration accuracy.

MeSH Terms (16)

Adult Aged Algorithms Brain Brain Neoplasms Female Humans Image Enhancement Image Interpretation, Computer-Assisted Imaging, Three-Dimensional Lasers Magnetic Resonance Imaging Male Middle Aged Models, Biological Surgery, Computer-Assisted

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