Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models

Antong Chen; Belma Dogdas; Saurin Mehta; Ansuman Bagchi; L. David Wise; Christopher Winkelmann

doi:10.1117/12.2043740

13 March 2014 Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models

Antong Chen, Belma Dogdas, Saurin Mehta, Ansuman Bagchi, L. David Wise, Christopher Winkelmann

Author Affiliations +

Proceedings Volume 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging; 903813 (2014) https://doi.org/10.1117/12.2043740
Event: SPIE Medical Imaging, 2014, San Diego, California, United States

Abstract

High-throughput micro-CT imaging has been used in our laboratory to evaluate fetal skeletal morphology in developmental toxicology studies. Currently, the volume-rendered skeletal images are visually inspected and observed abnormalities are reported for compounds in development. To improve the efficiency and reduce human error of the evaluation, we implemented a framework to automate the evaluation process. The framework starts by dividing the skull into regions of interest and then measuring various geometrical characteristics. Normal/abnormal classification on the bone segments is performed based on identifying statistical outliers. In pilot experiments using rabbit fetal skulls, the majority of the skeletal abnormalities can be detected successfully in this manner. However, there are shape-based abnormalities that are relatively subtle and thereby difficult to identify using the geometrical features. To address this problem, we introduced a model-based approach and applied this strategy on the squamosal bone. We will provide details on this active shape model (ASM) strategy for the identification of squamosal abnormalities and show that this method improved the sensitivity of detecting squamosal-related abnormalities from 0.48 to 0.92.

Citation Download Citation

Antong Chen, Belma Dogdas, Saurin Mehta, Ansuman Bagchi, L. David Wise, and Christopher Winkelmann "Automatic classification of squamosal abnormality in micro-CT images for the evaluation of rabbit fetal skull defects using active shape models", Proc. SPIE 9038, Medical Imaging 2014: Biomedical Applications in Molecular, Structural, and Functional Imaging, 903813 (13 March 2014); https://doi.org/10.1117/12.2043740

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