# Towards Robust Lung Segmentation in Chest Radiographs with Deep Learning

arXiv: https://arxiv.org/abs/1811.12638 · HTML: https://mlsanity.com/paper/1811.12638/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [A multi-stage GAN for multi-organ chest X-ray image generation and segmentation](https://mlsanity.com/paper/2106.05132/) (2021)
- [Efficient and Accurate Pneumonia Detection Using a Novel Multi-Scale Transformer Approach](https://mlsanity.com/paper/2408.04290/) (2024)
- [Deep learning for understanding multilabel imbalanced Chest X-ray datasets](https://mlsanity.com/paper/2207.14408/) (2022)

## Abstract (arXiv)

Automated segmentation of Lungs plays a crucial role in the computer-aided diagnosis of chest X-Ray (CXR) images. Developing an efficient Lung segmentation model is challenging because of difficulties such as the presence of several edges at the rib cage and clavicle, inconsistent lung shape among different individuals, and the appearance of the lung apex. In this paper, we propose a robust model for Lung segmentation in Chest Radiographs. Our model learns to ignore the irrelevant regions in an input Chest Radiograph while highlighting regions useful for lung segmentation. The proposed model is evaluated on two public chest X-Ray datasets (Montgomery County, MD, USA, and Shenzhen No. 3 People's Hospital in China). The experimental result with a DICE score of 98.6% demonstrates the robustness of our proposed lung segmentation approach.