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Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images

Part of the Lecture Notes in Computer Science book series (LNIP,volume 11766)


Accurate segmentations of neuroanatomical structures are essential for volumetric and morphological assessment, but manual segmentation is time-consuming and error-prone. We propose a convolutional neural network for structural segmentation based on deformation of an example mask that is disease-state agnostic, which we apply to the hippocampus. The hippocampus is one of the first subcortical structures affected by Alzheimer’s disease, atrophying as the disease progresses. As the disease state may be unknown, and due to the varying degrees of atrophy, an accurate shape prior is not always available. The proposed network is based on an adapted spatial transformer network that learns a deformation field to resample an initial binary mask, to create an output segmentation. This segmentation is learnt by the network from the input T1-weighted MRI in an end-to-end manner. Experiments on the HarP dataset show that the network outperforms other segmentation methods and is consistent across disease states, independent of the degree of disease-related atrophy. We also explore the effect of the initial binary mask on the segmentation, showing that the it is insensitive to the size and initial location of the binary mask.


  • Segmentation
  • Spatial transformer
  • Hippocampus

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  • DOI: 10.1007/978-3-030-32248-9_32
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This work was supported by funding from the Engineering and Physical Sciences Research Council (EPSRC) and Medical Research Council (MRC) [grant number EP/L016052/1]. A. Namburete is grateful for support from the UK Royal Academy of Engineering under the Engineering for Development Research Fellowships scheme. M. Jenkinson is supported by the National Institute for Health Research (NIHR) and the Oxford Biomedical Research Centre (BRC). Computation used the Oxford Biomedical Research Computing (BMRC) facility, a joint development between the Wellcome Centre for Human Genetics and the Big Data Institute supported by Health Data Research UK and the NIHR Oxford Biomedical Research Centre. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health.

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Correspondence to Nicola K. Dinsdale .

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Dinsdale, N.K., Jenkinson, M., Namburete, A.I.L. (2019). Spatial Warping Network for 3D Segmentation of the Hippocampus in MR Images. In: , et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2019. MICCAI 2019. Lecture Notes in Computer Science(), vol 11766. Springer, Cham.

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