DualSwinUnet++用双解码器提升甲状腺微癌分割精度
DualSwinUnet++: An Enhanced Swin-Unet Architecture With Dual Decoders For PTMC Segmentation
- 双解码器分别处理甲状腺与肿瘤，共享中间特征增强定位
- 在691张超声图上达到更高Dice和Jaccard分数
- 适合实时手术辅助，尤其适用于小病灶复杂场景
在超声引导射频消融术中，精准分割乳头状甲状腺微癌（PTMC）对治疗效果至关重要，但受声学伪影、病灶微小及解剖变异影响，仍具挑战。本文提出DualSwinUnet++，一种基于Transformer的双解码器架构，通过引入甲状腺腺体上下文信息提升分割性能。该模型为每个解码器配置独立线性投影头，并设计残差信息流机制，将首个（甲状腺）解码器的中间特征经拼接与变换传递至第二个（PTMC）解码器，实现肿瘤预测对腺体形态的显式依赖，同时避免梯度干扰。在包含691例标注的临床超声数据集上训练并评估，结果优于当前主流模型，在保持亚200毫秒推理延迟的同时显著提升分割准确率，证明其适用于近实时手术辅助，尤其在复杂PTMC病例中表现优异。
原文摘要 · Abstract (English)
Precise segmentation of papillary thyroid microcarcinoma (PTMC) during ultrasound-guided radiofrequency ablation (RFA) is critical for effective treatment but remains challenging due to acoustic artifacts, small lesion size, and anatomical variability. In this study, we propose DualSwinUnet++, a dual-decoder transformer-based architecture designed to enhance PTMC segmentation by incorporating thyroid gland context. DualSwinUnet++ employs independent linear projection heads for each decoder and a residual information flow mechanism that passes intermediate features from the first (thyroid) decoder to the second (PTMC) decoder via concatenation and transformation. These design choices allow the model to condition tumor prediction explicitly on gland morphology without shared gradient interference. Trained on a clinical ultrasound dataset with 691 annotated RFA images and evaluated against state-of-the-art models, DualSwinUnet++ achieves superior Dice and Jaccard scores while maintaining sub-200ms inference latency. The results demonstrate the model's suitability for near real-time surgical assistance and its effectiveness in improving segmentation accuracy in challenging PTMC cases.
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