Background and Objective : Accurate nodule delineation plays a significant role in the intelligent diagnosis of thyroid disease. However, the labels accessing is difficult since it is time-consuming and laborious. To ...
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Background and Objective : Accurate nodule delineation plays a significant role in the intelligent diagnosis of thyroid disease. However, the labels accessing is difficult since it is time-consuming and laborious. To mitigate the over-dependence of the segmentation accuracy on the labels, we proposed a dual-route transformer network (DRTNet) based on bbox-supervised learning that only requires the rough bounding rectangle as labels instead of a precise boundary for thyroid ultrasound segmentation. Methods : DRTNet incorporates double-branch foreground class activation mappings (CAMs) into Transformers to combine key areas. Meanwhile, double-branch architecture dynamically adjusts the feature distribution of nodules of different sizes in frequency channels and spatial dimensions, effectively addressing the localization of nodules of different sizes. Moreover, ultrasound prior background-aware pooling (UPBAP) is proposed in both branches to deal with the ambiguous boundary of thyroid nodules. Finally, adaptive uncertainty estimate multi-scale consistency (AUEMC) is proposed to help mitigate the risk of excessive over-fitting because of pseudo annotations, which further guarantees consistency among nodules with diverse resolutions. Results : Substantial improvement of segmentation accuracy is shown on the public thyroid dataset of TN3k and DDTI dataset with Dice similarity coefficient (DSC) of 84.94% and 83.98%, with 95% of the asymmetric Hausdorff distance (HD95) of 27.69 and 29.18, respectively. And our private dataset has a DSC of 84.39% and HD95 of 14.53. Conclusions : The proposed DRTNet used rectangular box labeled for thyroid ultrasound images based on bbox-supervised learning. The experimental results show that the DRTNet is comparable to these fully supervised methods. Code is available at https://***/ccjcv/DRTNet .
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