Normalization techniques for CNN based analysis of surgical cone beam CT volumes

Celia Martín Vicario; Florian Kordon; Felix Denzinger; Jan Siad El Barbari; Maxim Privalov; Jochen Franke; Andreas Maier; Holger Kunze

doi:10.1117/12.2606973

4 April 2022 Normalization techniques for CNN based analysis of surgical cone beam CT volumes

Celia Martín Vicario, Florian Kordon, Felix Denzinger, Jan Siad El Barbari, Maxim Privalov, Jochen Franke, Andreas Maier, Holger Kunze

Author Affiliations +

Proceedings Volume 12032, Medical Imaging 2022: Image Processing; 120322G (2022) https://doi.org/10.1117/12.2606973
Event: SPIE Medical Imaging, 2022, San Diego, California, United States

Conference Poster

Abstract

Purpose: The choice of input normalization has effects on the generalization and performance of deep neural networks. While this topic is explored for 2D imaging applications, the influence of different normalization techniques on medical imaging modalities, e.g. cone-beam CT (CBCT), differs due to a different value range and distribution. In this paper a good normalization technique for intra-operatively acquired surgical CBCT volumes is presented. Methods: A set of normalization strategies, namely histogram equalization, min-max scaling, z-score normalization, linear look up table (LUT) with clipping and sigmoid function with clipping is compared on a CBCT volume classification task. Results: The results show that a combination of parameterized LUTs and clipping with the range [-710, 1640] HU independent of the underlying intensity histogram provides the best performance for the task at hand. Conclusions: The clipping based normalization technique helps to compress the feature space to the relevant range. By this approach, most of the information about the intensity values of soft tissue and bone is retained. The clipping range presented in this paper is valid for surgical CBCTs.

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Celia Martín Vicario, Florian Kordon, Felix Denzinger, Jan Siad El Barbari, Maxim Privalov, Jochen Franke, Andreas Maier, and Holger Kunze "Normalization techniques for CNN based analysis of surgical cone beam CT volumes", Proc. SPIE 12032, Medical Imaging 2022: Image Processing, 120322G (4 April 2022); https://doi.org/10.1117/12.2606973

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