Segmentation-Based X-Ray Multiobjective Quality Assessment Network

X-ray imaging is crucial in orthopedic disease detection and diagnosis, but it can impact the body significantly. Ensuring imaging quality is vital for accurate diagnoses and reducing repeat scans. However, quality inspection can decrease efficiency and be influenced by subjectivity when handling large data volumes, affecting evaluation outcomes. Current deep learning methods for medical image quality assessment rely on extensive labeled data, posing privacy and resource challenges. Our research aims to develop a quality assessment network for X-ray imaging independent of complex labels and large datasets, tailored for multi-index quality assessment. We propose an X-ray imaging quality assessment network based on segmentation priors, utilizing the “segment anything model” (SAM) for mask segmentation and a dual-feature extraction network to process prior information. Through a channel fully connected module, we transform the regression problem into a multiclassification problem, improving convergence speed and performance. Comparative analysis demonstrates the superiority of our proposed algorithm. Our X-ray imaging quality assessment network achieves accurate and efficient quality assessment without relying on extensive labeled data. <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/OPMZZZ/SAM-DRIQA/</uri>