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Prostate MRI Registration Using Siamese Metric Learning

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Advances in Visual Computing (ISVC 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12510))

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Abstract

The process of registering intra-procedural prostate magnetic resonance images (MRI) with corresponding pre-procedural images improves the accuracy of certain surgeries, such as a prostate biopsy. Aligning the two images by means of rigid and elastic deformation may permit more precise use of the needle during the operation. However, gathering the necessary data and computing the ground truth is a problematic step. Currently, a single dataset is available and it is composed of only a few cases, making the training of standard deep convolutional neural networks difficult. To address this issue the moving image (intra-procedural) is randomly augmented producing different copies, and a convolutional siamese neural network tries to choose the best aligned copy with respect to the reference image (pre-procedural). The results of this research show that this method is superior to both a simple baseline obtained with standard image processing techniques and a deep CNN model. Furthermore, the best policy found for building the couple set for the siamese neural network reveals that a rule based on the mutual information that considers only the highest and the lowest value, representing similar and dissimilar cases, is the best option for training. The use of mutual information allows the model to be unsupervised, since the segmentation is no longer necessary. Finally, research on the size of the augmented set is conducted, showing that producing 18 different candidates is sufficient for a good performance.

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Author information

Authors and Affiliations

  1. Columbia Grammar and Preparatory School, New York, NY, 10025, USA

    Alexander Lyons

  2. Department of Information Engineering, University of Florence, Via di Santa Marta, 50129, Florence, FI, Italy

    Alberto Rossi

  3. Department of Information Engineering and Mathematics, University of Siena, Via Roma 56, 53100, Siena, Italy

    Alberto Rossi

Authors
  1. Alexander Lyons
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  2. Alberto Rossi
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Corresponding authors

Correspondence to Alexander Lyons or Alberto Rossi .

Editor information

Editors and Affiliations

  1. University of Nevada Reno, Reno, NV, USA

    George Bebis

  2. Stony Brook University, Stony Brook, NY, USA

    Zhaozheng Yin

  3. Drexel University, Philadelphia, PA, USA

    Edward Kim

  4. RWTH Aachen University, Aachen, Germany

    Jan Bender

  5. University of Edinburgh, Edinburgh, UK

    Kartic Subr

  6. IBM Research – Cambridge, Cambridge, MA, USA

    Bum Chul Kwon

  7. University of Waterloo, Waterloo, ON, Canada

    Jian Zhao

  8. Graz University of Technology, Graz, Austria

    Denis Kalkofen

  9. The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    George Baciu

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Lyons, A., Rossi, A. (2020). Prostate MRI Registration Using Siamese Metric Learning. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2020. Lecture Notes in Computer Science(), vol 12510. Springer, Cham. https://doi.org/10.1007/978-3-030-64559-5_47

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  • DOI: https://doi.org/10.1007/978-3-030-64559-5_47

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-64558-8

  • Online ISBN: 978-3-030-64559-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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