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Abstract: Convolutional Neural Networks (CNNs) have been recently employed to solveproblems from both the computer vision and medical image analysis fields.Despite their popularity, most approaches are only able to process 2D imageswhile most medical data used in clinical practice consists of 3D volumes. Inthis work we propose an approach to 3D image segmentation based on avolumetric, fully convolutional, neural network. Our CNN is trained end-to-endon MRI volumes depicting prostate, and learns to predict segmentation for thewhole volume at once. We introduce a novel objective function, that we optimiseduring training, based on Dice coefficient. In this way we can deal withsituations where there is a strong imbalance between the number of foregroundand background voxels. To cope with the limited number of annotated volumesavailable for training, we augment the data applying random non-lineartransformations and histogram matching. We show in our experimental evaluationthat our approach achieves good performances on challenging test data whilerequiring only a fraction of the processing time needed by other previousmethods.
Submission history
From: Fausto Milletari [view email]
[v1]
Wed, 15 Jun 2016 14:55:09 UTC (3,806 KB)Full-text links:
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DBLP - CS Bibliography
Nassir Navab
Seyed-Ahmad Ahmadi
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