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BRIEFnet: Deep Pancreas Segmentation Using Binary Sparse Convolutions

  • Mattias P. HeinrichEmail author
  • Ozan Oktay
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10435)

Abstract

Dense prediction using deep convolutional neural networks (CNNs) has recently advanced the field of segmentation in computer vision and medical imaging. In contrast to patch-based classification, it requires only a single path through a deep network to segment every voxel in an image. However, it is difficult to incorporate contextual information without using contracting (pooling) layers, which would reduce the spatial accuracy for thinner structures. Consequently, huge receptive fields are required which might lead to disproportionate computational demand. Here, we propose to use binary sparse convolutions in the first layer as a particularly effective approach to reduce complexity while achieving high accuracy. The concept is inspired by the successful BRIEF descriptors and complemented with \(1\times 1\) convolutions (cf. network in network) to further reduce the number of trainable parameters. Sparsity is in particular important for small datasets often found in medical imaging. Our experimental validation demonstrates accuracies for pancreas segmentation in CT that are comparable with state-of-the-art deep learning approaches and registration based multi-atlas segmentation with label fusion. The whole network, which also includes a classic CNN path to improve local details, can be trained in 10 min. Segmenting a new scan takes 3 s even without using a GPU.

Keywords

Context features Dilated convolutions Dense prediction 

Notes

Acknowledgements

We thank Maurice Sambale, whose work during his B.Sc. thesis gave inspiration to some of the new ideas in this paper.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Medical InformaticsUniversity of LübeckLübeckGermany
  2. 2.Biomedical Image Analysis GroupImperial College LondonLondonUK

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