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https://hdl.handle.net/2440/111346
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Type: | Conference paper |
Title: | Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior |
Author: | Maicas, G. Carneiro, G. Bradley, A. |
Citation: | Proceedings / IEEE International Symposium on Biomedical Imaging: from nano to macro. IEEE International Symposium on Biomedical Imaging, 2017, pp.305-309 |
Publisher: | IEEE |
Publisher Place: | Online |
Issue Date: | 2017 |
Series/Report no.: | IEEE International Symposium on Biomedical Imaging |
ISBN: | 9781509011711 |
ISSN: | 1945-7928 1945-8452 |
Conference Name: | IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA) |
Statement of Responsibility: | Gabriel Maicas, Gustavo Carneiro, Andrew P. Bradley |
Abstract: | We introduce a new fully automated breast mass segmentation method from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The method is based on globally optimal inference in a continuous space (GOCS) using a shape prior computed from a semantic segmentation produced by a deep learning (DL) model. We propose this approach because the limited amount of annotated training samples does not allow the implementation of a robust DL model that could produce accurate segmentation results on its own. Furthermore, GOCS does not need precise initialisation compared to locally optimal methods on a continuous space (e.g., Mumford-Shah based level set methods); also, GOCS has smaller memory complexity compared to globally optimal inference on a discrete space (e.g., graph cuts). Experimental results show that the proposed method produces the current state-of-the-art mass segmentation (from DCEMRI) results, achieving a mean Dice coefficient of 0.77 for the test set. |
Keywords: | Breast cancer; deep learning; energy-based segmentation; shape prior; breast mass segmentation; breast MRI; global optimization |
Rights: | ©2017 IEEE |
DOI: | 10.1109/ISBI.2017.7950525 |
Grant ID: | http://purl.org/au-research/grants/arc/DP140102794 http://purl.org/au-research/grants/arc/FT110100623 |
Published version: | http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7944115 |
Appears in Collections: | Aurora harvest 3 Computer Science publications |
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