# SCAN: Structure Correcting Adversarial Network for Organ Segmentation in Chest X-rays

arXiv: https://arxiv.org/abs/1703.08770 · HTML: https://mlsanity.com/paper/1703.08770/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Mar 2017.

## Built on by (most cited first, 12 of 12)

- [A multi-stage GAN for multi-organ chest X-ray image generation and segmentation](https://mlsanity.com/paper/2106.05132/) (2021)
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- [Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation](https://mlsanity.com/paper/2209.06353/) (2022)
- [Class dependency based learning using Bi-LSTM coupled with the transfer learning of VGG16 for the diagnosis of Tuberculosis from chest x-rays](https://mlsanity.com/paper/2108.04329/) (2021)
- [Training Generative Adversarial Networks with Adaptive Composite Gradient](https://mlsanity.com/paper/2111.05508/) (2021)
- [MLP-GAN for Brain Vessel Image Segmentation](https://mlsanity.com/paper/2207.08265/) (2022)
- [ReshapeIT: Reliable Shape Interaction with Implicit Template for Anatomical Structure Reconstruction](https://mlsanity.com/paper/2312.06164/) (2023)
- [Chest X-ray lung and heart segmentation based on minimal training sets](https://mlsanity.com/paper/2101.08309/) (2021)
- [Classification of Pneumonia and Tuberculosis from Chest X-rays](https://mlsanity.com/paper/2103.14562/) (2021)
- [Prediction of COVID-19 using chest X-ray images](https://mlsanity.com/paper/2204.03849/) (2022)
- [Advances in Medical Image Segmentation: A Comprehensive Survey with a Focus on Lumbar Spine Applications](https://mlsanity.com/paper/2510.03318/) (2025)

## Abstract (arXiv)

Chest X-ray (CXR) is one of the most commonly prescribed medical imaging procedures, often with over 2-10x more scans than other imaging modalities such as MRI, CT scan, and PET scans. These voluminous CXR scans place significant workloads on radiologists and medical practitioners. Organ segmentation is a crucial step to obtain effective computer-aided detection on CXR. In this work, we propose Structure Correcting Adversarial Network (SCAN) to segment lung fields and the heart in CXR images. SCAN incorporates a critic network to impose on the convolutional segmentation network the structural regularities emerging from human physiology. During training, the critic network learns to discriminate between the ground truth organ annotations from the masks synthesized by the segmentation network. Through this adversarial process the critic network learns the higher order structures and guides the segmentation model to achieve realistic segmentation outcomes. Extensive experiments show that our method produces highly accurate and natural segmentation. Using only very limited training data available, our model reaches human-level performance without relying on any existing trained model or dataset. Our method also generalizes well to CXR images from a different patient population and disease profiles, surpassing the current state-of-the-art.