Paper
14 February 2012 Robust alignment of prostate histology slices with quantified accuracy
Cecilia Hughes, Olivier Rouviere, Florence Mege Lechevallier, Rémi Souchon, Rémy Prost
Author Affiliations +
Abstract
Prostate cancer is the most common malignancy among men yet no current imaging technique is capable of detecting the tumours with precision. To evaluate each technique, the histology data must be precisely mapped to the imaged data. As it cannot be assumed that the histology slices are cut along the same plane as the imaged data is acquired, the registration is a 3D problem. This requires the prior accurate alignment of the histology slices. We propose a protocol to create in a rapid and standardised manner internal fiducial markers in fresh prostate specimens and an algorithm by which these markers can then be automatically detected and classified enabling the automatic rigid alignment of each slice. The protocol and algorithm were tested on 10 prostate specimens, with 19.2 histology slices on average per specimen. On average 90.9% of the fiducial markers created were visible in the slices, of which 96.1% were automatically correctly detected and classified. The average accuracy of the alignment was 0.19 ± 0.15 mm at the fiducial markers. The algorithm took 5.46 min on average per specimen. The proposed protocol and algorithm were also tested using simulated images and a beef liver sample. The simulated images showed that the algorithm has no associated residual error and justified the choice of a rigid registration. In the beef liver images, the average accuracy of the alignment was 0.11 ± 0.09 mm at the fiducial markers and 0.63 ± 0.47 mm at a validation marker approximately 20 mm from the fiducial markers.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Cecilia Hughes, Olivier Rouviere, Florence Mege Lechevallier, Rémi Souchon, and Rémy Prost "Robust alignment of prostate histology slices with quantified accuracy", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141N (14 February 2012); https://doi.org/10.1117/12.911257
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Cited by 2 scholarly publications.
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KEYWORDS
Prostate

Liver

Tissues

Image segmentation

Computer simulations

3D image processing

Image registration

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