期刊
INFORMATION PROCESSING IN MEDICAL IMAGING (IPMI 2017)
卷 10265, 期 -, 页码 348-360出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-319-59050-9_28
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资金
- Innovative Engineering for Health by the Wellcome Trust [WT101957, NS/A000027/1]
- Innovative Engineering for Health by the EPSRC [WT101957, NS/A000027/1]
- 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]
- UCL Overseas Research Scholarship
- UCL Graduate Research Scholarship
- Health Innovation Challenge Fund by the Department of Health [HICF-T4-275, WT 97914]
- Health Innovation Challenge Fund by the Wellcome Trust [HICF-T4-275, WT 97914]
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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