DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks.
DeepCut: Object Segmentation From Bounding Box Annotations Using Convolutional Neural Networks.
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DOI：
10.1109/tmi.2016.2621185
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发表时间：
2017-03
影响因子：
10.6
通讯作者：
Rueckert D
中科院分区：
文献类型：
--
作者：
Rajchl M;Lee MC;Oktay O;Kamnitsas K;Passerat-Palmbach J;Bai W;Damodaram M;Rutherford MA;Hajnal JV;Kainz B;Rueckert D
在本文中，我们提出了DeepCut，这是一种给定标记为弱注释的图像数据集获得像素级对象分割的方法，在我们的情况下是边界框。它扩展了著名的GrabCut方法，通过从边界框注释中训练神经网络分类器来包括机器学习。我们将该问题表述为密集连接条件随机场上的能量最小化问题，并迭代更新训练目标以获得像素级目标分割。此外，我们提出了DeepCut方法的变体，并将其与弱监督下CNN训练的naïve方法进行比较。我们在一个具有挑战性的胎儿磁共振数据集上测试了它在解决脑和肺分割问题上的适用性，并在准确性方面获得了令人鼓舞的结果。
In this paper, we propose DeepCut, a method to obtain pixelwise object segmentations given an image dataset labelled weak annotations, in our case bounding boxes. 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 naïve 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.