Direct aneurysm volume estimation by multi-view semi-supervised manifold learning

Abstract

Accurate volume estimation of left atrial aneurysm plays an essential role in the early diagnosis and therapy planning. However, it is a challenging task due to huge shape variabilities of aneurysms and great appearance variations of images, which tends to be intractable for segmentation methods. In this paper, we propose a novel estimation method for direct estimation of atrial aneurysm volumes without segmentation. To handle the high variabilities and variations, we propose a new multi-view semi-supervised manifold learning (MSML) algorithm, which fuses multiple complementary features to generate compact, informative and discriminative aneurysm image representation by leveraging both labeled and unlabeled data. Based on the obtained image representation, we adopt random regression forests to conduct direct volume estimation. Our method for the first time achieves a fully automatic estimation of left atrial aneurysm volumes. Experiments on a clinical dataset of 67 subjects with a total of 1220 images show that our method achieves a high correlation co-efficient of 0.91 with ground truth manually labelled by clinical experts and largely outperforms other methods, which demonstrates the effectiveness for aneurysm volume estimation and indicates its potential use in clinical practise.