Pyramid Network with Online Hard Example Mining for Accurate Left Atrium Segmentation

TL;DR

This paper introduces a 2D deep neural network with a pyramid module and online hard negative mining for accurate, efficient left atrium segmentation in MR volumes, outperforming traditional methods.

Contribution

It proposes a novel 2D pyramid network with online hard example mining and a competitive training scheme for improved atrium segmentation.

Findings

Achieves an average Dice score of 92.83% on test data.

Outperforms existing methods in accuracy and efficiency.

Effectively handles highly variable atrium shapes and pulmonary veins.

Abstract

Accurately segmenting left atrium in MR volume can benefit the ablation procedure of atrial fibrillation. Traditional automated solutions often fail in relieving experts from the labor-intensive manual labeling. In this paper, we propose a deep neural network based solution for automated left atrium segmentation in gadolinium-enhanced MR volumes with promising performance. We firstly argue that, for this volumetric segmentation task, networks in 2D fashion can present great superiorities in time efficiency and segmentation accuracy than networks with 3D fashion. Considering the highly varying shape of atrium and the branchy structure of associated pulmonary veins, we propose to adopt a pyramid module to collect semantic cues in feature maps from multiple scales for fine-grained segmentation. Also, to promote our network in classifying the hard examples, we propose an Online Hard Negative Example Mining strategy to identify voxels in slices with low classification certainties and penalize the wrong predictions on them. Finally, we devise a competitive training scheme to further boost the generalization ability of networks. Extensively verified on 20 testing volumes, our proposed framework achieves an average Dice of 92.83% in segmenting the left atria and pulmonary veins.