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

TL;DR

AssemblyNet introduces a novel ensemble of U-Nets inspired by parliamentary decision-making, significantly improving whole brain MRI segmentation accuracy with limited training data.

Contribution

The paper presents AssemblyNet, a new ensemble framework with knowledge sharing and amendment procedures, outperforming existing methods in brain MRI segmentation.

Findings

Outperforms global U-Net by 28% in Dice metric.

Outperforms patch-based joint label fusion by 15%.

Achieves high accuracy with limited training data.

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.