DualSwinUnet++: An Enhanced Swin-Unet Architecture With Dual Decoders For PTMC Segmentation

TL;DR

DualSwinUnet++ is a novel transformer-based architecture with dual decoders that improves PTMC segmentation accuracy in ultrasound images by explicitly modeling thyroid gland context, enabling near real-time surgical assistance.

Contribution

The paper introduces DualSwinUnet++, a dual-decoder transformer model with residual information flow, enhancing PTMC segmentation by explicitly incorporating thyroid gland context.

Findings

Outperforms state-of-the-art models in Dice and Jaccard scores

Achieves sub-200ms inference latency

Effective in challenging PTMC cases

Abstract

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.