Unsupervised Medical Image Segmentation with Adversarial Networks: From Edge Diagrams to Segmentation Maps

2019/11/12 by Umaseh Sivanesan, Sivanesan, Umaseh, Luis H. Braga +5 · 2 citations
Computer Science · Medicine · #AI in cancer detection #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #FOS: Electrical engineering #Generative Adversarial Networks and Image Synthesis #I.2.10 #I.4.6 #Image and Video Processing (eess.IV) #J.3 #Machine Learning (cs.LG) #Radiomics and Machine Learning in Medical Imaging #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1911.05140

openalex publication_date 2019/11/12 · openalex created_date 2019/11/22 · openalex updated_date 2026/07/28

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.

Citations

• Decoupled Deep Neural Network for Semi-supervised Semantic Segmentation
• Adam: A Method for Stochastic Optimization
• Very Deep Convolutional Networks for Large-Scale Image Recognition
• A Threshold Selection Method from Gray-Level Histograms
• Automatic Detection of Cerebral Microbleeds From MR Images via 3D Convolutional Neural Networks
• A Review on Deep Learning Techniques Applied to Semantic Segmentation
• W-Net: A Deep Model for Fully Unsupervised Image Segmentation
• TernausNet: U-Net with VGG11 Encoder Pre-Trained on ImageNet for Image Segmentation
• Attention U-Net: Learning Where to Look for the Pancreas
• Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)
• Embracing Imperfect Datasets: A Review of Deep Learning Solutions for Medical Image Segmentation

Cited by

• An ELIXIR scoping review on domain-specific evaluation metrics for synthetic data in life sciences
• TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors

Related

• TricycleGAN: Unsupervised Image Synthesis and Segmentation Based on Shape Priors
• Unsupervisedly Training GANs for Segmenting Digital Pathology with Automatically Generated Annotations
• Explainable Medical Image Segmentation via Generative Adversarial Networks and Layer-wise Relevance Propagation
• Progressive Adversarial Semantic Segmentation
• Is the medical image segmentation problem solved? A survey of current developments and future directions