1 research outputs found
Topology-preserving augmentation for cnn-based segmentation of congenital heart defects from 3d paediatric cmr
Patient-specific 3D printing of congenital heart anatomy demands an accurate
segmentation of the thin tissue interfaces which characterise these diagnoses.
Even when a label set has a high spatial overlap with the ground truth,
inaccurate delineation of these interfaces can result in topological errors.
These compromise the clinical utility of such models due to the anomalous
appearance of defects. CNNs have achieved state-of-the-art performance in
segmentation tasks. Whilst data augmentation has often played an important
role, we show that conventional image resampling schemes used therein can
introduce topological changes in the ground truth labelling of augmented
samples. We present a novel pipeline to correct for these changes, using a
fast-marching algorithm to enforce the topology of the ground truth labels
within their augmented representations. In so doing, we invoke the idea of
cardiac contiguous topology to describe an arbitrary combination of congenital
heart defects and develop an associated, clinically meaningful metric to
measure the topological correctness of segmentations. In a series of five-fold
cross-validations, we demonstrate the performance gain produced by this
pipeline and the relevance of topological considerations to the segmentation of
congenital heart defects. We speculate as to the applicability of this approach
to any segmentation task involving morphologically complex targets.Comment: To be published at MICCAI PIPPI 201