Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation

Prostate segmentation in transrectal ultrasound (TRUS) image is an essential prerequisite for many prostate-related clinical procedures, which, however, is also a long-standing problem due to the challenges caused by the low image quality and shadow artifacts. In this paper, we propose a Shadow-consistent Semi-supervised Learning (SCO-SSL) method with two novel mechanisms, namely shadow augmentation (Shadow-AUG) and shadow dropout (Shadow-DROP), to tackle this challenging problem. Specifically, Shadow-AUG enriches training samples by adding simulated shadow artifacts to the images to make the network robust to the shadow patterns. Shadow-DROP enforces the segmentation network to infer the prostate boundary using the neighboring shadow-free pixels. Extensive experiments are conducted on two large clinical datasets (a public dataset containing 1,761 TRUS volumes and an in-house dataset containing 662 TRUS volumes). In the fully-supervised setting, a vanilla U-Net equipped with our Shadow-AUG&Shadow-DROP outperforms the state-of-the-arts with statistical significance. In the semi-supervised setting, even with only 20% labeled training data, our SCO-SSL method still achieves highly competitive performance, suggesting great clinical value in relieving the labor of data annotation. Source code is released at https://github.com/DIAL-RPI/SCO-SSL.

Reference

X. Xu, T. Sanford, B. Turkbey, S. Xu, B.J. Wood, P. Yan, " Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation ,"

IEEE Trans. Medical Imaging, vol. 41, no. 6, pp. 1331-1345, June 2022.

Bibtex

@article{Xu2022_TMI-SCOSSL,
  title={Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation}, 
  author={Xu, Xuanang and Sanford, Thomas and Turkbey, Baris and Xu, Sheng and Wood, Bradford J. and Yan, Pingkun},
  journal={IEEE Transactions on Medical Imaging}, 
  year={2022},
  volume={41},
  number={6},
  pages={1331-1345},
  publisher={IEEE},
  doi={10.1109/TMI.2021.3139999}
}