Abstract
Deep convolutional neural networks are powerful tools for learning visual representations from images. However, designing efficient deep architectures to analyse volumetric medical images remains challenging. This work investigates efficient and flexible elements of modern convolutional networks such as dilated convolution and residual connection. With these essential building blocks, we propose a high-resolution, compact convolutional network for volumetric image segmentation. To illustrate its efficiency of learning 3D representation from large-scale image data, the proposed network is validated with the challenging task of parcellating 155 neuroanatomical structures from brain MR images. Our experiments show that the proposed network architecture compares favourably with state-of-the-art volumetric segmentation networks while being an order of magnitude more compact. We consider the brain parcellation task as a pretext task for volumetric image segmentation; our trained network potentially provides a good starting point for transfer learning. Additionally, we show the feasibility of voxel-level uncertainty estimation using a sampling approximation through dropout.
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Notes
- 1.
The source code is available at https://github.com/gift-surg/HighRes3DNet.
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Acknowledgements
This work was supported through an Innovative Engineering for Health award by the Wellcome Trust and EPSRC [WT101957, NS/A000027/1], the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative), UCL EPSRC CDT Scholarship Award [EP/L016478/1], a UCL Overseas Research Scholarship, a UCL Graduate Research Scholarship, and the Health Innovation Challenge Fund by the Department of Health and Wellcome Trust [HICF-T4-275, WT 97914]. The authors would like to acknowledge that the work presented here made use of Emerald, a GPU-accelerated High Performance Computer, made available by the Science & Engineering South Consortium operated in partnership with the STFC Rutherford-Appleton Laboratory.
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Li, W., Wang, G., Fidon, L., Ourselin, S., Cardoso, M.J., Vercauteren, T. (2017). On the Compactness, Efficiency, and Representation of 3D Convolutional Networks: Brain Parcellation as a Pretext Task. In: Niethammer, M., et al. Information Processing in Medical Imaging. IPMI 2017. Lecture Notes in Computer Science(), vol 10265. Springer, Cham. https://doi.org/10.1007/978-3-319-59050-9_28
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