A robust statistics-based global energy function for the alignment of serially acquired autoradiographic sections.
Fiche publication
Date publication
mars 2003
Journal
Journal of neuroscience methods
Auteurs
Membres identifiés du Cancéropôle Est :
Pr NAMER Izzie-Jacques
Tous les auteurs :
Nikou C, Heitz F, Nehlig A, Namer IJ, Armspach JP
Lien Pubmed
Résumé
Autoradiographic analysis of the functional changes occurring in the rat brain are most often performed on coronal sections that allow a good insight into the events occurring at the structural level but lacks the 3D context which is necessary to fully understand the involvement of the brain structures in specific situations like focal seizures with or without generalization. Therefore a robust, fully-automated algorithm for the registration of serially acquired autoradiographic sections is presented. The method accounts for the main difficulties of autoradiographic alignment: corrupted data (cuts and tears), dissimilarities or discontinuities between slices, non parallel or missing slices. The approach relies on the minimization of a global energy function based on robust statistics. The energy function measures the similarity between a slice and its neighborhood in the 3D volume. No particular direction is privileged in the method, so that global offsets, biases in the estimation or error propagations are avoided. The method is evaluated qualitatively and quantitatively on real autoradiographic data. Rat brain autoradiographic volumes are reconstructed with registration errors less than 1 degree in rotation and less than 1 pixel in translation.
Mots clés
Algorithms, Anatomy, Cross-Sectional, methods, Animals, Autoradiography, methods, Brain, diagnostic imaging, Humans, Imaging, Three-Dimensional, methods, Pattern Recognition, Automated, Quality Control, Radiographic Image Enhancement, methods, Rats, Reproducibility of Results, Sensitivity and Specificity, Subtraction Technique
Référence
J. Neurosci. Methods. 2003 Mar;124(1):93-102