Direct Volume Estimation Of Left Atrial Aneurysm Based On Semi-supervised Manifold Learning

Accurate volume estimation of left atrial aneurysm plays a practically essential role in the early diagnosis and therapy planning.However,it is a challenging task due to huge shape variabilities of aneurysms,great appearance variations of images and low contrast with its surrounding,which tends to be intractable for manual and automatic segmentation.In this paper,we propose a novel volume estimation framework for direct estimation of atrial aneurysm volumes without segmentation.Accurate volume estimation with the help of Computer-aided technology(CAD)can significantly reduce the workload of doctors and provide valuably diagnostic information,which plays an important role in clinic practice.To handle the high variabilities and variations,we propose a sparse coding method based on multi-scale 3D morphological gradient(MTMGS)and a new multi-view semi-supervised manifold learning(MSML)algorithm.In the th:ird Chapter,MTMGS can make full use of the three-dimensional gradient information of CT images and then obtain more essential features with unsupervised learning method,i.e.,sparse coding.In the fourth chapter,MSML fuses multiple complementary features to generate compact,informative and discriminative aneurysm image representation by leveraging both labeled and unlabeled data.The MSML is able to jointly extract discriminate features that are directly related to aneurysm volumes while removing the redundant information,which enables more efficient and accurate volume estimation.Based on the obtained image representation by the MSML,we adopt random regression forests to conduct direct and efficient volume estimation.Experiments on a clinical dataset of 67 subjects with a total of 1220 images show that our method achieves a high correlation coefficient of around 0.912 with ground truth manually obtained by clinical experts and largely outperforms other methods,which demonstrates the effectiveness for aneurysm volume estimation,indicating its potential use in clinical practice.