Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
arXiv:1911.05140
Abstract
We develop and approach to unsupervised semantic medical image segmentation that extends previous work with generative adversarial networks. We use existing edge detection methods to construct simple edge diagrams, train a generative model to convert them into synthetic medical images, and construct a dataset of synthetic images with known segmentations using variations on extracted edge diagrams. This synthetic dataset is then used to train a supervised image segmentation model. We test our approach on a clinical dataset of kidney ultrasound images and the benchmark ISIC 2018 skin lesion dataset. We show that our unsupervised approach is more accurate than previous unsupervised methods, and performs reasonably compared to supervised image segmentation models. All code and trained models are available at https://github.com/kiretd/Unsupervised-MIseg.
16 pages, 8 figures
References in corpus (9)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
- Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
- Automatic Liver and Lesion Segmentation in CT Using Cascaded Fully Convolutional Neural Networks and 3D Conditional Random Fields
- TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
- W-Net: A Deep Model for Fully Unsupervised Image Segmentation
- Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
- Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
- Synthetic Medical Images from Dual Generative Adversarial Networks