Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps
This work addresses the need for medical image segmentation without labeled data, offering an incremental improvement over existing unsupervised techniques.
The paper tackles unsupervised medical image segmentation by generating synthetic images from edge diagrams and using them to train a supervised segmentation model, achieving higher accuracy than previous unsupervised methods and competitive performance on kidney ultrasound and skin lesion datasets.
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.