Collimation border with U-Net segmentation on chest radiographs compared to radiologists

Chest Radiography (CXR) is a common radiographic procedure. Radiation exposure to patients should be kept as low as reasonably achievable (ALARA), and monitored continuously as part of quality assurance (QA) programs. One of the most effective dose reduction tools is proper collimation practice.The purpose of this study is to determine whether a U-Net convolutional neural networks (U-CNN) can be trained to automatically segment the lungs and calculate an optimized collimation border on a limited CXR dataset.

Source: Radiography - May 3, 2023 Category: Radiology Authors: A.E. Pedersen, M.W. Kusk, G.H. Knudsen, C.A.G.R. Busk, S. Lysdahlgaard Source Type: research