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Shadow-consistent Semi-supervised Learning (SCO-SSL)

Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation

This is a python (PyTorch) implementation of Shadow-consistent Semi-supervised Learning (SCO-SSL) method for prostate ultrasound segmentation proposed in our IEEE Transactions on Medical Imaging journal paper "Shadow-consistent Semi-supervised Learning for Prostate Ultrasound Segmentation".

Citation

X. Xu, T. Sanford, B. Turkbey, S. Xu, B. J. Wood and P. Yan, "Shadow-Consistent Semi-Supervised Learning for Prostate Ultrasound Segmentation," in IEEE Transactions on Medical Imaging, vol. 41, no. 6, pp. 1331-1345, June 2022, doi: 10.1109/TMI.2021.3139999.

@article{Xu2021SCOSSL,
  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}
}

Update

• Sep 15, 2022: Add a script UCLA_data_conversion.py for data format conversion (from DICOM/STL to NIfTI format) specially designed for UCLA Prostate-MRI-US-Biopsy dataset that was used in our paper.
• Mar 23, 2022: Add a script 3d_dist_visual.py for 3D distance error visualization that was shown in our paper.

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.

Method

Scheme of SCO-SSL

Scheme of Shadow augmentation (Shadow-AUG)

Scheme of Shadow dropout (Shadow-DROP)

Data

You can run our code using the public dataset, UCLA Prostate-MRI-US-Biopsy dataset, shared by the Institute of Urologic Oncology, University of California-Los Angeles (UCLA) on the Cancer Imaging Archive (TCIA) platform. We provided a Python script UCLA_data_conversion.py to convert the original data to the NIfTI format that was accepted by our dataloader interface.

Contact

You are welcome to contact us:

• xux12@rpi.edu (Dr. Xuanang Xu)