Computer methods and programs in biomedicine · 2026
BMR-Restormer: A transformer-based blind inpainting network for ultrasound marker removal.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Yu Jiabin, Mi Hongmei, Chen Dong, Xiao Binggang, Wang Vicky Y, Zhou Yahan, Jin Zhiyan, Huang Jiaheng, Zhang Dongping, Sun Yu, Ma Yuan, Yang Li, Zhuang Min, Chen Chen, Yuan Shengxing, Xu Dong, Liu Qi
Аннотация
BACKGROUND AND OBJECTIVES: Artificial markers, including crosses, text, and measurement lines, are commonly embedded in clinical ultrasound images to aid visual diagnosis. However, these overlays introduce substantial bias in deep learning models by drawing attention away from pathological structures, leading to degraded performance in computer-aided diagnosis (CAD).
METHODS: To address this issue, we propose the BMR-Restormer network specifically optimized for ultrasound marker removal. The architecture integrates Multi-Group Transposed Attention (MGTA) and Masked Window Self-Attention (MWSA) to enhance local-global feature modeling under low-contrast and speckle-rich conditions. A progressive learning strategy was employed to support coarse-to-fine feature acquisition.
RESULTS: Our method achieved state-of-the-art image restoration performance, with PSNR/SSIM/LPIPS values of 63.68/0.999/0.003 on the internal dataset and 59.30/0.999/0.005 on the external dataset. To evaluate the clinical impact of artificial markers, we conducted three downstream tasks (classification, segmentation, and detection) under different training-testing configurations. The results showed that models trained on marker-containing data had markedly lower generalization performance when applied to marker-free images. For example, classification precision dropped from 0.8830 to 0.5484 when the model trained on marker-containing data was evaluated on clean images. Similarly, segmentation Dice scores fell from 0.8217 to 0.4759. In object detection, AP@50 decreased from 0.9928 to 0.2393.
CONCLUSIONS: These results demonstrated that artificial markers cause models to overfit spurious features and shift attention away from diagnostically relevant regions. In contrast, models trained on marker-free data maintained high accuracy on both clean and marked images, confirming that the proposed BMR-Restormer provides a robust and clinically reliable solution for ultrasound marker removal.
METHODS: To address this issue, we propose the BMR-Restormer network specifically optimized for ultrasound marker removal. The architecture integrates Multi-Group Transposed Attention (MGTA) and Masked Window Self-Attention (MWSA) to enhance local-global feature modeling under low-contrast and speckle-rich conditions. A progressive learning strategy was employed to support coarse-to-fine feature acquisition.
RESULTS: Our method achieved state-of-the-art image restoration performance, with PSNR/SSIM/LPIPS values of 63.68/0.999/0.003 on the internal dataset and 59.30/0.999/0.005 on the external dataset. To evaluate the clinical impact of artificial markers, we conducted three downstream tasks (classification, segmentation, and detection) under different training-testing configurations. The results showed that models trained on marker-containing data had markedly lower generalization performance when applied to marker-free images. For example, classification precision dropped from 0.8830 to 0.5484 when the model trained on marker-containing data was evaluated on clean images. Similarly, segmentation Dice scores fell from 0.8217 to 0.4759. In object detection, AP@50 decreased from 0.9928 to 0.2393.
CONCLUSIONS: These results demonstrated that artificial markers cause models to overfit spurious features and shift attention away from diagnostically relevant regions. In contrast, models trained on marker-free data maintained high accuracy on both clean and marked images, confirming that the proposed BMR-Restormer provides a robust and clinically reliable solution for ultrasound marker removal.