bibtext: @InProceedings{ Guo_Unsupervised_MICCAI2024,
                   author = { Guo, Juncheng and Lin, Jianxin and Tan, Guanghua and Lu, Yuhuan and Gao, Zhan and Li, Shengli and Li, Kenli },
                   title = { { Unsupervised Ultrasound Image Quality Assessment with Score Consistency and Relativity Co-learning } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15005 },
                   month = {October},
                   pages = { pending },
                }
abstract: Selecting an optimal standard plane in prenatal ultrasound is crucial for improving the accuracy of AI-assisted diagnosis. Existing approaches, typically dependent on detecting the presence of anatomical structures as defined by clinical protocols, have been constrained by a lack of consideration for image perceptual quality. Although supervised training with manually labeled quality scores seems feasible, the subjective nature and unclear definition of these scores make such learning error-prone and manual labeling excessively time-consuming. In this paper, we present an unsupervised ultrasound image quality assessment method with score consistency and relativity co-learning (CRL-UIQA). Our approach generates pseudo-labels by calculating feature distribution distances between ultrasound images and high-quality standard planes, leveraging consistency and relativity for training regression networks in quality prediction. Extensive experiments on the dataset demonstrate the impressive performance of the proposed CRL-UIQA, showcasing excellent generalization across diverse plane images.
title: Unsupervised Ultrasound Image Quality Assessment with Score Consistency and Relativity Co-learning
authors: Guo, Juncheng Lin, Jianxin Tan, Guanghua Lu, Yuhuan Gao, Zhan Li, Shengli Li, Kenli