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Abstract

Computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs). The main issues arise during feature extraction, due to the large dynamic range of intensities and the varying number of recorded slices of CT volumes. In this paper we address these issues with a framework that combines domain-specific data pre-processing and augmentation with state-of-the-art CNN architectures. The focus is not limited to score optimization, but also to stabilize the achieved prediction performance, since this is a mandatory requirement for use in automated and semi-automated workflows in the clinical environment.
The framework is validated contextually to an architecture comparison to show CNN architecture independent effects of our framework functionality. This comparison includes a modified U-Net and a modified Mixed-Scale Dense Network (MS-D Net) to compare dilated convolutions for parallel multi-scale processing to the U-Net approach based on traditional scaling operations. Finally, in order to combine the superior recognition performance of 2D-CNN models with the more comprehensive spatial information of 3D-CNN models, we propose an ensemble model. The framework performs successfully when tested on a range of tasks such as liver and kidney segmentation, without significant differences in prediction performance on strongly differing volume sizes and varying slice thickness.