  Tissue window filtering has been widely used in deep learning for computed
tomography (CT) image analyses to improve training performance (e.g., soft
tissue windows for abdominal CT). However, the effectiveness of tissue window
normalization is questionable since the generalizability of the trained model
might be further harmed, especially when such models are applied to new cohorts
with different CT reconstruction kernels, contrast mechanisms, dynamic
variations in the acquisition, and physiological changes. We evaluate the
effectiveness of both with and without using soft tissue window normalization
on multisite CT cohorts. Moreover, we propose a stochastic tissue window
normalization (SWN) method to improve the generalizability of tissue window
normalization. Different from the random sampling, the SWN method centers the
randomization around the soft tissue window to maintain the specificity for
abdominal organs. To evaluate the performance of different strategies, 80
training and 453 validation and testing scans from six datasets are employed to
perform multi-organ segmentation using standard 2D U-Net. The six datasets
cover the scenarios, where the training and testing scans are from (1) same
scanner and same population, (2) same CT contrast but different pathology, and
(3) different CT contrast and pathology. The traditional soft tissue window and
nonwindowed approaches achieved better performance on (1). The proposed SWN
achieved general superior performance on (2) and (3) with statistical analyses,
which offers better generalizability for a trained model.
