Abstract
Robust and accurate segmentation of the left atrial aneurysm serves as an essential role in the clinical practice. However, automatic segmentation is an extremely challenging task because of the huge shape variabilities of the aneurysm and its complex surroundings. In this paper, we propose a novel framework based on deep residual networks (DRN) for automatic segmentation of the left atrial aneurysm in CT images. Our proposed approach is able to make full use of structure information and adopts extremely deep architectures to learn more discriminative features, which enables more efficient and accurate segmentation. The main procedures of our proposed method are as follows: in the first step, a large-scale of pre-processed images are divided into patches as training units which then are used to train a classification model by DRN; in the second step, based on the trained DRN model, the left atrial aneurysm is segmented with a novel structured fusion algorithm. The proposed method for the first time achieves a fully automatic segmentation of left atrial aneurysm. With sufficient training datasets and test datasets, experimental results show that the proposed framework outperforms the state-of-the-art methods in terms of accuracy and relative error. The proposed method has also a high correlation to the ground truth, which demonstrates it is a promising techniques to left atrial aneurysm segmentation and other clinical applications.
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Acknowledgement
This work was supported by National Natural Science Foundation of China (Grant No. 61301010, 61327001, 61271336), the Natural Science Foundation of Fujian Province (Grant No. 2014J05080), Research Fund for the Doctoral Program of Higher Education (20130121120045) and by the Fundamental Research Funds for the Central Universities (Grant No. 2013SH005, 20720150110).
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Wang, L., Li, S., Chen, Y., Lin, J., Liu, C. (2016). Structure Fusion for Automatic Segmentation of Left Atrial Aneurysm Based on Deep Residual Networks. In: Wang, L., Adeli, E., Wang, Q., Shi, Y., Suk, HI. (eds) Machine Learning in Medical Imaging. MLMI 2016. Lecture Notes in Computer Science(), vol 10019. Springer, Cham. https://doi.org/10.1007/978-3-319-47157-0_32
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DOI: https://doi.org/10.1007/978-3-319-47157-0_32
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