阅读量：

587

作者：

M Rajchl，M Lee，O Oktay，K Kamnitsas，J Passerat-Palmbach，W Bai，M Rutherford，J Hajnal，B Kainz，D Rueckert

展开

摘要：

In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled with bounding box annotations. It extends the approach of the well-known GrabCut method to include machine learning by training a neural network classifier from bounding box annotations. We formulate the problem as an energy minimisation problem over a densely-connected conditional random field and iteratively update the training targets to obtain pixelwise object segmentations. Additionally, we propose variants of the DeepCut method and compare those to a naive approach to CNN training under weak supervision. We test its applicability to solve brain and lung segmentation problems on a challenging fetal magnetic resonance dataset and obtain encouraging results in terms of accuracy.

展开

关键词：

Image segmentation Training Object segmentation Biological neural networks Optimization Computational modeling Imaging

DOI：

10.1109/TMI.2016.2621185

被引量：

56

年份：

2016