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Xu X, Sanford T, Turkbey B, et al. Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation. IEEE Trans Med Imaging. 2021;doi: 10.1109/TMI.2021.3139999.
Xu, X., Sanford, T., Turkbey, B., Xu, S., Wood, B. J., & Yan, P. (2021). Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation. IEEE transactions on medical imaging, . https://doi.org/10.1109/TMI.2021.3139999
Xu, Xuanang, et al. "Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation." IEEE transactions on medical imaging vol. (2021). doi: https://doi.org/10.1109/TMI.2021.3139999
Xu X, Sanford T, Turkbey B, Xu S, Wood BJ, Yan P. Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation. IEEE Trans Med Imaging. 2021 Dec 31; doi: 10.1109/TMI.2021.3139999. Epub 2021 Dec 31. PMID: 34971530.
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IEEE Trans Med Imaging. 2021 Dec 31; doi: 10.1109/TMI.2021.3139999. Epub 2021 Dec 31.

Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation.

IEEE transactions on medical imaging

Xuanang Xu, Thomas Sanford, Baris Turkbey, Sheng Xu, Bradford J Wood, Pingkun Yan

PMID: 34971530 DOI: 10.1109/TMI.2021.3139999

Abstract

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.

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