Ultrasound in medicine & biology · 2026
Frequency-aware Vision Mamba With Deformable Windowed Selective Scan for Ultrasound Image Segmentation.
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.
Liu Yan, Yang Yan, Jiang Yongquan, Zhao Xiaole, Fan Liang
Аннотация
OBJECTIVE: Accurate segmentation of ultrasound images is crucial for medical diagnosis and treatment. However, achieving precise lesion segmentation is challenging owing to inter-class indistinction caused by low contrast, high speckle noise and blurred boundaries, as well as intra-class inconsistency resulting from variations in lesion size, shape and location.
METHODS: To address these challenges, we propose a frequency-aware vision mamba network with deformable windowed selective scan. Specifically, we introduce the frequency-aware selective state space layer, which uses fast Fourier transform to extract frequency-domain information and applies frequency-aware weighting to enhance discriminative features, effectively mitigating intra-class inconsistency. Moreover, we developed a deformable windowed selective scan module that dynamically adjusts scanning paths by a learnable offset field to focus on ambiguous boundaries, significantly reducing inter-class indistinction.
RESULTS: Extensive experiments on 4 cross-domain ultrasound datasets demonstrate that the frequency-aware vision mamba network outperforms other state-of-the-art methods, achieving Dice coefficient scores of 0.84, 0.83,0.84 and 0.87 on breast ultrasound images, thyroid gland segmentation, thyroid nodule segmentation and classification in ultrasound images and multi-modal ovarian tumor ultrasound datasets, respectively, with corresponding intersection over union scores of 0.76,0.76,0.76 and 0.80, respectively.
CONCLUSION: Our results show that the proposed method provides a robust solution for clinical ultrasound image analysis, addressing inter-class indistinction and intra-class inconsistency challenges.
METHODS: To address these challenges, we propose a frequency-aware vision mamba network with deformable windowed selective scan. Specifically, we introduce the frequency-aware selective state space layer, which uses fast Fourier transform to extract frequency-domain information and applies frequency-aware weighting to enhance discriminative features, effectively mitigating intra-class inconsistency. Moreover, we developed a deformable windowed selective scan module that dynamically adjusts scanning paths by a learnable offset field to focus on ambiguous boundaries, significantly reducing inter-class indistinction.
RESULTS: Extensive experiments on 4 cross-domain ultrasound datasets demonstrate that the frequency-aware vision mamba network outperforms other state-of-the-art methods, achieving Dice coefficient scores of 0.84, 0.83,0.84 and 0.87 on breast ultrasound images, thyroid gland segmentation, thyroid nodule segmentation and classification in ultrasound images and multi-modal ovarian tumor ultrasound datasets, respectively, with corresponding intersection over union scores of 0.76,0.76,0.76 and 0.80, respectively.
CONCLUSION: Our results show that the proposed method provides a robust solution for clinical ultrasound image analysis, addressing inter-class indistinction and intra-class inconsistency challenges.