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Development and Evaluation of Intraoperative Ultrasound Segmentation with Negative Image Frames and Multiple Observer Labels

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Simplifying Medical Ultrasound (ASMUS 2021)


When developing deep neural networks for segmenting intraoperative ultrasound images, several practical issues are encountered frequently, such as the presence of ultrasound frames that do not contain regions of interest and the high variance in ground-truth labels. In this study, we evaluate the utility of a pre-screening classification network prior to the segmentation network. Experimental results demonstrate that such a classifier, minimising frame classification errors, was able to directly impact the number of false positive and false negative frames. Importantly, the segmentation accuracy on the classifier-selected frames, that would be segmented, remains comparable to or better than those from standalone segmentation networks. Interestingly, the efficacy of the pre-screening classifier was affected by the sampling methods for training labels from multiple observers, a seemingly independent problem. We show experimentally that a previously proposed approach, combining random sampling and consensus labels, may need to be adapted to perform well in our application. Furthermore, this work aims to share practical experience in developing a machine learning application that assists highly variable interventional imaging for prostate cancer patients, to present robust and reproducible open-source implementations, and to report a set of comprehensive results and analysis comparing these practical, yet important, options in a real-world clinical application.

L. F. Chalcroft, J. Qu, S. A. Martin, I. J. M. B. Gayo, G. V. Minore, I. R. D. Singh—Contributed equally.

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  1. Anas, E.M.A., Mousavi, P., Abolmaesumi, P.: A deep learning approach for real time prostate segmentation in freehand ultrasound guided biopsy. Med. Image Anal. 48, 107–116 (2018).

  2. Ghose, S., Oliver, A., et al.: A survey of prostate segmentation methodologies in ultrasound, magnetic resonance and computed tomography images. Comput. Meth. Programs Biomed. 108(1), 262–287 (2012).

  3. Giordano, D., Kavasidis, I., et al.: Rejecting false positives in video object segmentation. In: Azzopardi, G., Petkov, N. (eds.) Computer Analysis of Images and Patterns, pp. 100–112. Springer International Publishing, Cham (2015).

  4. He, K., Zhang, X., et al.: Deep residual learning for image recognition (2015)

    Google Scholar 

  5. Hossain, M.S., Paplinski, A.P., Betts, J.M.: Prostate segmentation from ultrasound images using residual fully convolutional network (2019)

    Google Scholar 

  6. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization (2017)

    Google Scholar 

  7. Lei, Y., Tian, S., et al.: Ultrasound prostate segmentation based on multidirectional deeply supervised v-net. Med. Phys. 46(7), 3194–3206 (2019).

  8. Orlando, N., Gillies, D.J., et al.: Automatic prostate segmentation using deep learning on clinically diverse 3D transrectal ultrasound images. Med. Phys. 47(6), 2413–2426 (2020).

  9. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. CoRR abs/1505.04597 (2015).

  10. Rottmann, M., Maag, K., Chan, R., Hüger, F., Schlicht, P., Gottschalk, H.: Detection of false positive and false negative samples in semantic segmentation (2019)

    Google Scholar 

  11. Sarkar, S., Das, S.: A review of imaging methods for prostate cancer detection. Biomed. Eng. Comput. Biol. 7s1, BECB–S34255 (2016).

  12. Sudre, C.H., Anson, B.G., et al.: Let’s agree to disagree: learning highly debatable multirater labelling. CoRR abs/1909.01891 (2019).

  13. Sudre, C.H., Li, W., Vercauteren, T., Ourselin, S., Jorge Cardoso, M.: Generalised dice overlap as a deep learning loss function for highly unbalanced segmentations. In: Cardoso, M., et al. (eds.) Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support. DLMIA 2017, ML-CDS 2017. Lecture Notes in Computer Science, pp. 240–248 (2017).

  14. Wang, Y., et al.: Deep attentional features for prostate segmentation in ultrasound. In: Frangi, A.F., Schnabel, J.A., Davatzikos, C., Alberola-López, C., Fichtinger, G. (eds.) MICCAI 2018. LNCS, vol. 11073, pp. 523–530. Springer, Cham (2018).

    Chapter  Google Scholar 

  15. Xie, S., Girshick, R., Dollár, P., Tu, Z., He, K.: Aggregated residual transformations for deep neural networks (2017)

    Google Scholar 

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This work is supported by the EPSRC-funded UCL Centre for Doctoral Training in Intelligent, Integrated Imaging in Healthcare (i4health) (EP/S021930/1) and the Department of Health’s NIHR-funded Biomedical Research Centre at University College London Hospitals. Z.M.C. Baum is supported by the Natural Sciences and Engineering Research Council of Canada Postgraduate Scholarships-Doctoral Program, and the University College London Overseas and Graduate Research Scholarships. This work is also supported by the Wellcome/EPSRC Centre for Interventional and Surgical Sciences (203145Z/16/Z).

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Correspondence to Liam F. Chalcroft .

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Chalcroft, L.F. et al. (2021). Development and Evaluation of Intraoperative Ultrasound Segmentation with Negative Image Frames and Multiple Observer Labels. In: Noble, J.A., Aylward, S., Grimwood, A., Min, Z., Lee, SL., Hu, Y. (eds) Simplifying Medical Ultrasound. ASMUS 2021. Lecture Notes in Computer Science(), vol 12967. Springer, Cham.

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