  One of the challenges in developing deep learning algorithms for medical
image segmentation is the scarcity of annotated training data. To overcome this
limitation, data augmentation and semi-supervised learning (SSL) methods have
been developed. However, these methods have limited effectiveness as they
either exploit the existing data set only (data augmentation) or risk negative
impact by adding poor training examples (SSL). Segmentations are rarely the
final product of medical image analysis - they are typically used in downstream
tasks to infer higher-order patterns to evaluate diseases. Clinicians take into
account a wealth of prior knowledge on biophysics and physiology when
evaluating image analysis results. We have used these clinical assessments in
previous works to create robust quality-control (QC) classifiers for automated
cardiac magnetic resonance (CMR) analysis. In this paper, we propose a novel
scheme that uses QC of the downstream task to identify high quality outputs of
CMR segmentation networks, that are subsequently utilised for further network
training. In essence, this provides quality-aware augmentation of training data
in a variant of SSL for segmentation networks (semiQCSeg). We evaluate our
approach in two CMR segmentation tasks (aortic and short axis cardiac volume
segmentation) using UK Biobank data and two commonly used network architectures
(U-net and a Fully Convolutional Network) and compare against supervised and
SSL strategies. We show that semiQCSeg improves training of the segmentation
networks. It decreases the need for labelled data, while outperforming the
other methods in terms of Dice and clinical metrics. SemiQCSeg can be an
efficient approach for training segmentation networks for medical image data
when labelled datasets are scarce.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning