# SUSAN: Segment Unannotated image Structure using Adversarial Network

arXiv: https://arxiv.org/abs/1812.00555 · HTML: https://mlsanity.com/paper/1812.00555/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Dec 2018.

## Built on by (most cited first, 9 of 9)

- [Sketch guided and progressive growing GAN for realistic and editable ultrasound image synthesis](https://mlsanity.com/paper/2204.06929/) (2022)
- [COSMOS: Cross-Modality Unsupervised Domain Adaptation for 3D Medical Image Segmentation based on Target-aware Domain Translation and Iterative Self-Training](https://mlsanity.com/paper/2203.16557/) (2022)
- [MR-Contrast-Aware Image-to-Image Translations with Generative Adversarial Networks](https://mlsanity.com/paper/2104.01449/) (2021)
- [SDC-UDA: Volumetric Unsupervised Domain Adaptation Framework for Slice-Direction Continuous Cross-Modality Medical Image Segmentation](https://mlsanity.com/paper/2305.11012/) (2023)
- [Medical Image Segmentation with Domain Adaptation: A Survey](https://mlsanity.com/paper/2311.01702/) (2023)
- [Instance Segmentation of Unlabeled Modalities via Cyclic Segmentation GAN](https://mlsanity.com/paper/2204.03082/) (2022)
- [IMPORTANT-Net: Integrated MRI Multi-Parameter Reinforcement Fusion Generator with Attention Network for Synthesizing Absent Data](https://mlsanity.com/paper/2302.01788/) (2023)
- [MCR-VQGAN: A Scalable and Cost-Effective Tau PET Synthesis Approach for Alzheimer's Disease Imaging](https://mlsanity.com/paper/2512.15947/) (2025)
- [Multi-Channel Uncertainty-Weighted Score Matching for Conditional Diffusion in Medical UDA](https://mlsanity.com/paper/2509.22476/) (2025)

## Abstract (arXiv)

Segmentation of magnetic resonance (MR) images is a fundamental step in many medical imaging-based applications. The recent implementation of deep convolutional neural networks (CNNs) in image processing has been shown to have significant impacts on medical image segmentation. Network training of segmentation CNNs typically requires images and paired annotation data representing pixel-wise tissue labels referred to as masks. However, the supervised training of highly efficient CNNs with deeper structure and more network parameters requires a large number of training images and paired tissue masks. Thus, there is great need to develop a generalized CNN-based segmentation method which would be applicable for a wide variety of MR image datasets with different tissue contrasts. The purpose of this study was to develop and evaluate a generalized CNN-based method for fully-automated segmentation of different MR image datasets using a single set of annotated training data. A technique called cycle-consistent generative adversarial network (CycleGAN) is applied as the core of the proposed method to perform image-to-image translation between MR image datasets with different tissue contrasts. A joint segmentation network is incorporated into the adversarial network to obtain additional segmentation functionality. The proposed method was evaluated for segmenting bone and cartilage on two clinical knee MR image datasets acquired at our institution using only a single set of annotated data from a publicly available knee MR image dataset. The new technique may further improve the applicability and efficiency of CNN-based segmentation of medical images while eliminating the need for large amounts of annotated training data.