- Abstract: For the majority of the learning-based segmentation methods, a large quantity of high-quality training data is required. In this paper, we present a novel learning-based segmentation model that could be trained semi- or un- supervised. Specifically, in the unsupervised setting, we parameterize the Active contour without edges (ACWE) framework via a convolutional neural network (ConvNet), and optimize the parameters of the ConvNet using a self-supervised method. In another setting (semi-supervised), the auxiliary segmentation ground truth is used during training. We show that the method provides fast and high-quality bone segmentation in the context of single-photon emission computed tomography (SPECT) image.
- Track: short paper
- Keywords: Image Segmentation, Convolutional Neural Networks, Unsupervised Learning
- Paper Type: methodological development
- Presentation Upload: zip
- Presentation Upload Agreement: I agree that my presentation material (videos and slides) will be made public.