Swin-X2S: Reconstructing 3D Shape from 2D Biplanar X-ray with Swin Transformers

TL;DR

Swin-X2S is an end-to-end deep learning approach that uses Swin Transformers and cross-attention to accurately reconstruct 3D anatomical shapes from 2D biplanar X-ray images, improving clinical diagnostic processes.

Contribution

The paper introduces Swin-X2S, a novel encoder-decoder architecture with a dimension-expanding module for direct 3D reconstruction from 2D X-rays, outperforming previous methods.

Findings

Significant improvements in segmentation and labeling metrics.

Effective across multiple anatomies and datasets.

Clinically relevant parameter accuracy enhanced.

Abstract

The conversion from 2D X-ray to 3D shape holds significant potential for improving diagnostic efficiency and safety. However, existing reconstruction methods often rely on hand-crafted features, manual intervention, and prior knowledge, resulting in unstable shape errors and additional processing costs. In this paper, we introduce Swin-X2S, an end-to-end deep learning method for directly reconstructing 3D segmentation and labeling from 2D biplanar orthogonal X-ray images. Swin-X2S employs an encoder-decoder architecture: the encoder leverages 2D Swin Transformer for X-ray information extraction, while the decoder employs 3D convolution with cross-attention to integrate structural features from orthogonal views. A dimension-expanding module is introduced to bridge the encoder and decoder, ensuring a smooth conversion from 2D pixels to 3D voxels. We evaluate proposed method through extensive qualitative and quantitative experiments across nine publicly available datasets covering four anatomies (femur, hip, spine, and rib), with a total of 54 categories. Significant improvements over previous methods have been observed not only in the segmentation and labeling metrics but also in the clinically relevant parameters that are of primary concern in practical applications, which demonstrates the promise of Swin-X2S to provide an effective option for anatomical shape reconstruction in clinical scenarios. Code implementation is available at: \url{https://github.com/liukuan5625/Swin-X2S}.