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A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images

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Abstract

The purpose of this research is to exploit a weak and semi-supervised deep learning framework to segment prostate cancer in TRUS images, alleviating the time-consuming work of radiologists to draw the boundary of the lesions and training the neural network on the data that do not have complete annotations. A histologic-proven benchmarking dataset of 102 case images was built and 22 images were randomly selected for evaluation. Some portion of the training images were strong supervised, annotated pixel by pixel. Using the strong supervised images, a deep learning neural network was trained. The rest of the training images with only weak supervision, which is just the location of the lesion, were fed to the trained network to produce the intermediate pixelwise labels for the weak supervised images. Then, we retrained the neural network on the all training images with the original labels and the intermediate labels and fed the training images to the retrained network to produce the refined labels. Comparing the distance of the center of mass of the refined labels and the intermediate labels to the weak supervision location, the closer one replaced the previous label, which could be considered as the label updates. After the label updates, test set images were fed to the retrained network for evaluation. The proposed method shows better result with weak and semi-supervised data than the method using only small portion of strong supervised data, although the improvement may not be as much as when the fully strong supervised dataset is used. In terms of mean intersection over union (mIoU), the proposed method reached about 0.6 when the ratio of the strong supervised data was 40%, about 2% decreased performance compared to that of 100% strong supervised case. The proposed method seems to be able to help to alleviate the time-consuming work of radiologists to draw the boundary of the lesions, and to train the neural network on the data that do not have complete annotations.

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Acknowledgements

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2017R1C1B5077068 and NRF-2013R1A1A2011398) and by Korea National University of Transportation in 2019, and also supported by the Technology Innovation Program funded By the Ministry of Trade, Industry and Energy (MOTIE) of Korea (10049785, Development of ‘medical equipment using (ionizing or non-ionizing) radiation’-dedicated R&D platform and medical device technology).

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, Korea National University of Transportation, Uiwang-si, Kyunggi-do, South Korea

    Seokmin Han

  2. Department of Radiology, Seoul National University Bundang Hospital, Seongnam-si, Kyunggi-do, South Korea

    Sung Il Hwang

  3. Department of Radiology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam-si, Kyunggi-do, South Korea

    Hak Jong Lee

  4. Department of Nanoconvergence, Seoul National University Graduate School of Convergence Science and Technology, Suwon-si, Kyunggi-do, South Korea

    Hak Jong Lee

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  1. Seokmin Han
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Correspondence to Sung Il Hwang.

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Han, S., Hwang, S. & Lee, H.J. A Weak and Semi-supervised Segmentation Method for Prostate Cancer in TRUS Images. J Digit Imaging 33, 838–845 (2020). https://doi.org/10.1007/s10278-020-00323-3

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  • DOI: https://doi.org/10.1007/s10278-020-00323-3

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