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DeepCut: object segmentation from bounding box annotations using convolutional neural networks
Abstract
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[1] 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- Journal Article
- Science & Technology
- Technology
- Life Sciences & Biomedicine
- Computer Science, Interdisciplinary Applications
- Engineering, Biomedical
- Engineering, Electrical & Electronic
- Imaging Science & Photographic Technology
- Radiology, Nuclear Medicine & Medical Imaging
- Computer Science
- Engineering
- Bounding box
- convolutional neural networks
- DeepCut
- image segmentation
- machine learning
- weak annotations
- GRAPH-CUT SEGMENTATION
- FLOW SEGMENTATION
- MRI
- OPTIMIZATION
- GRABCUT
- cs.CV
- cs.CV
- Nuclear Medicine & Medical Imaging
- 08 Information And Computing Sciences
- 09 Engineering