A novel approach to remove foreign objects from chest X-ray images

TL;DR

This paper presents a deep learning pipeline that detects, segments, and inpaints foreign objects in chest X-ray images to improve diagnostic accuracy, achieving state-of-the-art results.

Contribution

The paper introduces a multi-step deep learning method combining detection, segmentation, and inpainting for foreign object removal in chest X-rays, a novel approach in this domain.

Findings

Achieved state-of-the-art accuracy in foreign object removal

Effective segmentation and inpainting of foreign objects

Potential applications in improving diagnostic image quality

Abstract

We initially proposed a deep learning approach for foreign objects inpainting in smartphone-camera captured chest radiographs utilizing the cheXphoto dataset. Foreign objects which can significantly affect the quality of a computer-aided diagnostic prediction are captured under various settings. In this paper, we used multi-method to tackle both removal and inpainting chest radiographs. Firstly, an object detection model is trained to separate the foreign objects from the given image. Subsequently, the binary mask of each object is extracted utilizing a segmentation model. Each pair of the binary mask and the extracted object are then used for inpainting purposes. Finally, the in-painted regions are now merged back to the original image, resulting in a clean and non-foreign-object-existing output. To conclude, we achieved state-of-the-art accuracy. The experimental results showed a new approach to the possible applications of this method for chest X-ray images detection.