Even though it is a crucial step for achieving suitable results, the preprocessing of data before it is used as input to deep neural networks is often only described as a side note. This work elaborates on the required steps in this preprocessing procedure. Specifically, we provide insights into the selection of appropriate segmentation algorithms to generate reference volumes from X-ray computed tomography (XCT) scans as training data. Furthermore, this work evaluates the criteria for the selection of an appropriate deep learning network architecture, and a quantitative comparison between networks based on U-Net and V-Net.
|Original language||English (American)|
|Title of host publication||Proceedings of the Industrial Computed Tomography Conference (iCT) 2022|
|Publication status||Accepted/In press - 2022|