CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI

Bontempi, D., Benini, S., Signoroni, A., Svanera, M. and Muckli, L. (2020) CEREBRUM: a fast and fully-volumetric Convolutional Encoder-decodeR for weakly-supervised sEgmentation of BRain strUctures from out-of-the-scanner MRI. Medical Image Analysis, 62, 101688. (doi: 10.1016/j.media.2020.101688) (PMID:32272345)

Abstract

Many functional and structural neuroimaging studies call for accurate morphometric segmentation of different brain structures starting from image intensity values of MRI scans. Current automatic (multi-) atlas-based segmentation strategies often lack accuracy on difficult-to-segment brain structures and, since these methods rely on atlas-to-scan alignment, they may take long processing times. Alternatively, recent methods deploying solutions based on Convolutional Neural Networks (CNNs) are enabling the direct analysis of out-of-the-scanner data. However, current CNN-based solutions partition the test volume into 2D or 3D patches, which are processed independently. This process entails a loss of global contextual information, thereby negatively impacting the segmentation accuracy. In this work, we design and test an optimised end-to-end CNN architecture that makes the exploitation of global spatial information computationally tractable, allowing to process a whole MRI volume at once. We adopt a weakly supervised learning strategy by exploiting a large dataset composed of 947 out-of-the-scanner (3 Tesla T1-weighted 1mm isotropic MP-RAGE 3D sequences) MR Images. The resulting model is able to produce accurate multi-structure segmentation results in only a few seconds. Different quantitative measures demonstrate an improved accuracy of our solution when compared to state-of-the-art techniques. Moreover, through a randomised survey involving expert neuroscientists, we show that subjective judgements favour our solution with respect to widely adopted atlas-based software.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Muckli, Professor Lars and Bontempi, Mr Dennis and Svanera, Dr Michele

Creator Roles

Bontempi, D.: Conceptualization, Methodology, Software, Formal analysis, Investigation, Data curation, Writing – original draft, Writing – review and editing, Visualization
Svanera, M.: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Data curation, Supervision, Project administration
Muckli, L.: Resources, Funding acquisition

• Authors: Bontempi, D., Benini, S., Signoroni, A., Svanera, M., and Muckli, L.
• College/School: College of Medical Veterinary and Life Sciences > School of Psychology & Neuroscience
• Journal Name: Medical Image Analysis
• Publisher: Elsevier
• ISSN: 1361-8415
• ISSN (Online): 1361-8423
• Published Online: 24 March 2020
• Copyright Holders: Copyright © 2020 The Authors
• First Published: First published in Medical Image Analysis 62: 101688
• Publisher Policy: Reproduced under a Creative Commons licence

Related URLs

• Pre-print

Project Code	Award No	Project Name	Principal Investigator	Funder's Name	Funder Ref	Lead Dept
304518		Human Brain Project SGA 2	Lars Muckli	European Commission (EC)	785907	NP - Centre for Cognitive Neuroimaging (CCNi)