Model Averaging and Augmented Inference for Stable Echocardiography Segmentation using 2D ConvNets

Joshua V. Stough

Keywords: Convolutional Neural Networks, Echocardiography, Segmentation, Data Augmentation

TL;DR: Accumulating outputs from multiple models, or over augmentations of the test image on a single model, can improve poor semantic segmentation.

Track: short paper

Paper Type: methodological development

Abstract: The automatic segmentation of heart substructures in 2D echocardiography images is a goal common to both clinicians and researchers. Convolutional neural networks (CNNs) have recently shown the best average performance. However, on the rare occasions that a trained CNN fails, it can fail spectacularly. To mitigate these errors, in this work we develop and validate two easily implementable schemes for regularizing performance in 2D CNNs: model averaging and augmented inference. Model averaging involves training multiple instances of a CNN with data augmentation over a sampled training set. Augmented inference involves accumulating network output over augmentations of the test image. Using the recently released CAMUS echocardiography dataset, we show significant incremental improvement in outlier performance over the baseline model. These encouraging results must still be validated against independent clinical data.