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https://hdl.handle.net/2440/87282
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Type: | Conference paper |
Title: | Robust left ventricle segmentation from ultrasound data using deep neural networks and efficient search methods |
Author: | Carneiro, G. Nascimento, J. Freitas, A. |
Citation: | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2010, pp.1085-1088 |
Publisher: | IEEE |
Publisher Place: | NJ, USA |
Issue Date: | 2010 |
Series/Report no.: | IEEE International Symposium on Biomedical Imaging |
ISBN: | 9781424441266 |
ISSN: | 1945-7928 |
Conference Name: | 7th IEEE International Symposium on Biomedical Imaging (14 Apr 2010 - 17 Apr 2010 : Rotterdam, The Netherlands) |
Statement of Responsibility: | Gustavo Carneiro, Jacinto Nascimento, António Freitas |
Abstract: | The automatic segmentation of the left ventricle of the heart in ultrasound images has been a core research topic in medical image analysis. Most of the solutions are based on low-level segmentation methods, which uses a prior model of the appearance of the left ventricle, but imaging conditions violating the assumptions present in the prior can damage their performance. Recently, pattern recognition methods have become more robust to imaging conditions by automatically building an appearance model from training images, but they present a few challenges, such as: the need of a large set of training images, robustness to imaging conditions not present in the training data, and complex search process. In this paper we handle the second problem using the recently proposed deep neural network and the third problem with efficient searching algorithms. Quantitative comparisons show that the accuracy of our approach is higher than state-of-the-art methods. The results also show that efficient search strategies reduce ten times the run-time complexity. |
Keywords: | Segmentation of the left ventricle of the heart deep neural networks optimization algorithms |
Rights: | ©2010 IEEE |
DOI: | 10.1109/ISBI.2010.5490181 |
Published version: | http://dx.doi.org/10.1109/isbi.2010.5490181 |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
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