Dataset Information

Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.

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.

SUBMITTER: Xu X

PROVIDER: S-EPMC9709821 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature

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Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.

Xu Xuanang X Sanford Thomas T Turkbey Baris B Xu Sheng S Wood Bradford J BJ Yan Pingkun P

IEEE transactions on medical imaging 20220601 6

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, ...[more]

PMID: 34971530

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Dataset Information

Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.

Publications

Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation.

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