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A probabilistic model combining deep learning and multi-atlas segmentation for semi-automated labelling of histology

Atzeni, A; Jansen, M; Ourselin, S; Iglesias, JE; (2018) A probabilistic model combining deep learning and multi-atlas segmentation for semi-automated labelling of histology. In: Frangi, AF and Schnabel, JA and Davatzikos, C and Alberola-López, C and Fichtinger, G, (eds.) Medical Image Computing and Computer Assisted Intervention – MICCAI 2018: 21st International Conference, Proceedings, Part II. (pp. pp. 219-227). Springer: Cham, Switzerland. Green open access

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Abstract

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.

Type: Proceedings paper
Title: A probabilistic model combining deep learning and multi-atlas segmentation for semi-automated labelling of histology
Event: MICCAI 2018: 21st International Conference on Medical Image Computing and Computer-Assisted Intervention, 16-20 September 2018, Granada, Spain
ISBN-13: 9783030009335
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/978-3-030-00934-2_25
Publisher version: https://doi.org/10.1007/978-3-030-00934-2_25
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Medical Sciences > Cancer Institute > Research Department of Pathology
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10059025
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