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Development of kidney segmentation models has largely focused on using contrast-enhanced CT exams. The KiTS segmentation challenge, in particular, has provided a benchmark using 300 annotated arterial phase CT scans. Review of the best performing models identifies 3D Unet models with residual connections as the best performing models for kidney segmentation. Li et al. found a U-net architecture with residual connections to provide the best performance for their segmentation task. Their work focused on segmenting kidney parenchyma alongside kidney stones using a dataset of 257 studies that was recently made available. Yu et al. investigated the ability to train a multi-organ nn-Unet model using simultaneous contrast and non-contrast images. The authors found the model to achieve high dice scores for kidney segmentation with average dice scores of 0.96.4 Inspection of output segmentation found the model to underperform for non-contrast images as measured by smaller quality assessment scores. Tang et al. took a similar approach where early and late arterial phase scans were used to train a patch-based network to segment renal structures. The group found the model to perform adequately on test data, with no distinction between late and early arterial phase performance. Lee et al. attempted to reduce the dependency of labeling multiple phases by using paired samples where only the contrast-enhanced volume was annotated. They were able to outperform existing models but were limited by the need for correct anatomical correspondence between scans. Ananda et al. removed the dependency for paired samples by training a dual discriminator-based network where the model is trained in three phases. One phase ensures the consistency of segmentation of contrast phase images; the following two phases ensure the image encoding and output maps are not significantly different from contrast and non-contrast images. Dinsdale et al. implemented a similar multi-step approach to improve segmentation quality by improving resiliency to agerelated physiological changes in Brain MRI, where cross entropy(CE) loss is utilized for training a discriminator on the patient’s age using features from the bottleneck and segmentation map. In contrast, the final phase uses a confusion loss, penalizing the model for having greater confidence for a particular age.8 Despite all the modeling effort, the application of evaluating patients with impaired kidney function is challenging since variation occurs not only due to phase of contrast but also the level of renal function, and thus the consistency of imaging appearance is not guaranteed. Establishing the need for techniques that would improve the robustness of kidney segmentation models. Within the scope of this work, we proposed various techniques to generate models resilient to different contrast phases and externally validated the models.
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