Frequency-aware Vision Mamba With Deformable Windowed Selective Scan for Ultrasound Image Segmentation.

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.