A Probabilistic Model Combining Deep Learning and Multi-atlas Segmentation for Semi-automated Labelling of Histology

  • Alessia AtzeniEmail author
  • Marnix Jansen
  • Sébastien Ourselin
  • Juan Eugenio Iglesias
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11071)


Thanks to their high resolution and contrast enhanced by different stains, histological images are becoming increasingly widespread in atlas construction. Building atlases with histology requires manual delineation of a set of regions of interest on a large amount of sections. This process is tedious, time-consuming, and rather inefficient due to the high similarity of adjacent sections. Here we propose a probabilistic model for semi-automated segmentation of stacks of histological sections, in which the user manually labels a sparse set of sections (e.g., one every n), and lets the algorithm complete the segmentation for other sections automatically. The proposed model integrates in a principled manner two families of segmentation techniques that have been very successful in brain imaging: multi-atlas segmentation (MAS) and convolutional neural networks (CNNs). Within this model, we derive a Generalised Expectation Maximisation algorithm to compute the most likely segmentation. Experiments on the Allen dataset show that the model successfully combines the strengths of both techniques (effective label propagation of MAS, and robustness to misregistration of CNNs), and produces significantly more accurate results than using either of them independently.



supported by the EPSRC (CDT in Medical Imaging, EP/ L016478/1), ERC (Starting Grant 677697) and NVIDIA (donation of GPU).


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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Alessia Atzeni
    • 1
    Email author
  • Marnix Jansen
    • 2
  • Sébastien Ourselin
    • 3
    • 4
  • Juan Eugenio Iglesias
    • 1
  1. 1.Medical Physics and Biomedical EngineeringUniversity College LondonLondonUK
  2. 2.University College London HospitalLondonUK
  3. 3.Wellcome EPSRC Centre for Interventional and Surgical Sciences (WEISS)University College LondonLondonUK
  4. 4.The School of Biomedical Engineering and Imaging ScienceKing’s College LondonLondonUK

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