Ultrasound Nerve Segmentation Using Deep Probabilistic Programming

Iresha Rubasinghe, Dulani Meedeniya


Deep probabilistic programming concatenates the strengths of deep learning to the context of probabilistic modeling for efficient and flexible computation in practice. Being an evolving field, there exist only a few expressive programming languages for uncertainty management. This paper discusses an application for analysis of ultrasound nerve segmentation-based biomedical images. Our method uses the probabilistic programming language Edward with the U-Net model and generative adversarial networks under different optimizers. The segmentation process showed the least Dice loss (‑0.54) and the highest accuracy (0.99) with the Adam optimizer in the U-Net model with the least time consumption compared to other optimizers. The smallest amount of generative network loss in the generative adversarial network model gained was 0.69 for the Adam optimizer. The Dice loss, accuracy, time consumption and output image quality in the results show the applicability of deep probabilistic programming in the long run. Thus, we further propose a neuroscience decision support system based on the proposed approach.


deep learning; deep probabilistic programming; generative adversarial network; nerve segmentation; neuroscience decision support.

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DOI: http://dx.doi.org/10.5614%2Fitbj.ict.res.appl.2019.13.3.5


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