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


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.


  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},