Domain-specific cues improve robustness of deep learning-based segmentation of CT volumes.

ABSTRACT: Machine learning has considerably improved medical image analysis in the past years. Although data-driven approaches are intrinsically adaptive and thus, generic, they often do not perform the same way on data from different imaging modalities. In particular computed tomography (CT) data poses many challenges to medical image segmentation based on convolutional neural networks (CNNs), mostly due to the broad 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 adds domain-specific data preprocessing and augmentation to state-of-the-art CNN architectures. Our major focus is to stabilise the prediction performance over samples as a mandatory requirement for use in automated and semi-automated workfl

SUBMITTER: Kloenne M 

PROVIDER: S-EPMC7329868 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature