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

by   Pierrick Coupé, et al.

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 of the Dice metric, patch-based joint label fusion by 15 Finally, AssemblyNet demonstrates high capacity to deal with limited training data to achieve whole brain segmentation in practical training and testing times.


page 3

page 7


AssemblyNet: A large ensemble of CNNs for 3D Whole Brain MRI Segmentation

Whole brain segmentation using deep learning (DL) is a very challenging ...

VoteNet: A Deep Learning Label Fusion Method for Multi-Atlas Segmentation

Deep learning (DL) approaches are state-of-the-art for many medical imag...

3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles

Detailed whole brain segmentation is an essential quantitative technique...

DDU-Nets: Distributed Dense Model for 3D MRI Brain Tumor Segmentation

Segmentation of brain tumors and their subregions remains a challenging ...

Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data

Whole brain segmentation on a structural magnetic resonance imaging (MRI...

Deep Label Fusion: A 3D End-to-End Hybrid Multi-Atlas Segmentation and Deep Learning Pipeline

Deep learning (DL) is the state-of-the-art methodology in various medica...

Please sign up or login with your details

Forgot password? Click here to reset