Abstract
Medical image processing tasks such as segmentation often require capturing non-local information. As organs, bones, and tissues share common characteristics such as intensity, shape, and texture, the contextual information plays a critical role in correctly labeling them. Segmentation and labeling is now typically done with convolutional neural networks (CNNs) but the context of the CNN is limited by the receptive field which itself is limited by memory requirements and other properties. In this paper, we propose a new attention module, that we call Permutohedral Attention Module (PAM), to efficiently capture non-local characteristics of the image. The proposed method is both memory and computationally efficient. We provide a GPU implementation of this module suitable for 3D medical imaging problems. We demonstrate the efficiency and scalability of our module with the challenging task of vertebrae segmentation and labeling where context plays a crucial role because of the very similar appearance of different vertebrae.
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Acknowledgement
We thank E. Molteni, C. Sudre, B. Murray, K. Georgiadis, Z. Eaton-Rosen, M. Ebner for their useful comments. This work is supported by the Wellcome/EPSRC Centre for Medical Engineering [WT 203148/Z/16/Z]. TV is supported by a Medtronic/RAEng Research Chair [RCSRF1819/7/34].
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Joutard, S., Dorent, R., Isaac, A., Ourselin, S., Vercauteren, T., Modat, M. (2019). Permutohedral Attention Module for Efficient Non-local Neural Networks. In: Shen, D., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11769. Springer, Cham. https://doi.org/10.1007/978-3-030-32226-7_44
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DOI: https://doi.org/10.1007/978-3-030-32226-7_44
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