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|Title:||Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior|
|Citation:||Proceedings of the IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), 2017 / pp.305-309|
|Series/Report no.:||IEEE International Symposium on Biomedical Imaging|
|Conference Name:||IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) (18 Apr 2017 - 21 Apr 2017 : Melbourne, AUSTRALIA)|
|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|
|Appears in Collections:||Computer Science publications|
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