AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation

Pierrick Coupé, Boris Mansencal, Michaël Clément, Rémi Giraud, Baudouin Denis de Senneville, Vinh-Thong Ta, Vincent Lepetit, José V Manjon

Research output: Contribution to journalArticleResearch

Abstract

Whole brain segmentation using deep learning (DL) is a very challenging task since the number of anatomical labels is very high compared to the number of available training images. To address this problem, previous DL methods proposed to use a global convolution neural network (CNN) or few independent CNNs. In this paper, we present a novel ensemble method based on a large number of CNNs processing different overlapping brain areas. Inspired by parliamentary decision-making systems, we propose a framework called AssemblyNet, made of two" assemblies" of U-Nets. Such a parliamentary system is capable of dealing with complex decisions and reaching a consensus quickly. AssemblyNet introduces sharing of knowledge among neighboring U-Nets, an" amendment" procedure made by the second assembly at higher-resolution to refine the decision taken by the first one, and a final decision obtained by majority voting. When using the same 45 training images, AssemblyNet outperforms global U-Net by 28% in terms of the Dice metric, patch-based joint label fusion by 15% and SLANT-27 by 10%. Finally, AssemblyNet demonstrates high capacity to deal with limited training data to achieve whole brain segmentation in practical training and testing times.
Subjects: Image and Video Processing (eess. IV); Computer Vision and Pattern Recognition (cs. CV); Machine Learning (cs. LG); Neurons and Cognition (q-bio. NC)
Original languageEnglish
Number of pages8
JournalarXiv.org e-Print archive
Publication statusPublished - 2019
Externally publishedYes

    Fingerprint

Cite this

Coupé, P., Mansencal, B., Clément, M., Giraud, R., de Senneville, B. D., Ta, V-T., ... V Manjon, J. (2019). AssemblyNet: A Novel Deep Decision-Making Process for Whole Brain MRI Segmentation. arXiv.org e-Print archive.