Martín Vicario C, Kordon F, Denzinger F, El Barbari JS, Privalov M, Franke J, Maier A, Kunze H (2022)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2022
Publisher: SPIE
Book Volume: 120322G
Conference Proceedings Title: Medial Imaging 2022, Image Processing
Event location: San Diego, California
DOI: 10.1117/12.2606973
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.
APA:
Martín Vicario, C., Kordon, F., Denzinger, F., El Barbari, J.S., Privalov, M., Franke, J.,... Kunze, H. (2022). Normalization techniques for CNN based analysis of surgical cone beam CT volumes. In Išgum I, Colliot O (Eds.), Medial Imaging 2022, Image Processing. San Diego, California: SPIE.
MLA:
Martín Vicario, Celia, et al. "Normalization techniques for CNN based analysis of surgical cone beam CT volumes." Proceedings of the SPIE Medical Imaging 2022, San Diego, California Ed. Išgum I, Colliot O, SPIE, 2022.
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