Name
Title | ||
|---|---|---|
Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior |
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. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/ISBI.2017.7950525 | 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) |
Keywords | Field | DocType |
breast cancer,deep learning,energy-based segmentation,shape prior,breast mass segmentation,breast MRI,global optimization | Cut,Computer vision,Scale-space segmentation,Pattern recognition,Sørensen–Dice coefficient,Computer science,Segmentation,Segmentation-based object categorization,Level set,Image segmentation,Artificial intelligence,Test set | Conference |
ISBN | Citations | PageRank |
978-1-5090-1173-5 | 5 | 0.55 |
References | Authors | |
10 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriel Maicas | 1 | 14 | 2.90 |
| Gustavo Carneiro | 2 | 292 | 27.63 |
| Andrew P. Bradley | 3 | 2087 | 195.95 |