Dual-Triple Transformer Networks for Accurate CT Pleural Effusion Segmentation

Abstract

Pleural effusion segmentation in computed tomography images is essential to its precise diagnosis and treatment but remains challenging due to blurred boundaries, heterogeneous morphology, and low contrast with adjacent anatomical structures. This work shows a first study on pleural effusion segmentation by introducing a new deep learning architecture of dual-triple transformer networks. Specifically, this architecture builds a dual encoder of swin transformer and 3D deformable convolution, leveraging the multiscale representation capability to capture global contextual information and model complex deformations. Moreover, a triple decoder with a fusion module, a boundary-awareness mechanism, and a transposed-residual convolution block is introduced to effectively propagate these global and local features and refine the segmentation by mitigating ambiguity at the interfaces with surrounding tissues. We validate our method on 143 chest computed tomography scans. The experimental results demonstrate that our proposed model significantly outperforms state-of-the-art segmentation approaches, greatly improving the dice similarity coefficient and mean intersection over union while critically reducing both Hausdorff distance 95 and average symmetric surface distance.