A Study on the Development of a System for Preventing Left-Right Reversal of Chest X-ray Images Using U-Net

Abstract

The X-ray images are sent to the image server via the imaging system to check the image resolution and patient misidentification. Chest X-ray images are usually taken in a standing position with back and front views, but for patients who cannot stand, the images are inverted to be taken in front and back views. However, if the image is inverted incorrectly and sent to the imaging system, and if the examiner is unaware of the inversion, an incident may occur in which a left-right inverted image is sent. We focused on the fact that the left and right lung fields have different shapes, and examined the feasibility of segmenting the contours and constructing a system to prevent left-right inversion. Using miniJSRT_database, a database of labeled chest X-ray images published by the Japanese Society of Radiological Technology, we constructed a U-Net-based segmentation model to extract lung field regions from chest X-ray images. We extracted lung field regions from 50 cases of chest X-ray images collected separately at Showa University Northern Yokohama Hospital using the above model. The IoU of the lung field region was calculated from the pseudo-inverted images and the original images, and showed a low value of 0.64. On the other hand, the IoU between the normal images showed a high value of 0.94. This indicates that the lung field segmentation can accurately detect false inversions in the images.