Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation

TL;DR

This paper introduces a novel ensemble of semi-dense 3D CNNs, called SemiDenseNet, for infant brain MRI segmentation, providing accurate results and useful annotation suggestions despite challenging image quality.

Contribution

It presents the first ensemble of 3D CNNs for annotation suggestion and proposes SemiDenseNet, a lightweight dense architecture that improves training efficiency and segmentation accuracy.

Findings

Ensemble agreement correlates with segmentation errors.

SemiDenseNet outperforms existing models in accuracy and efficiency.

Method ranks first or second in the MICCAI iSEG-2017 Challenge.

Abstract

Precise 3D segmentation of infant brain tissues is an essential step towards comprehensive volumetric studies and quantitative analysis of early brain developement. However, computing such segmentations is very challenging, especially for 6-month infant brain, due to the poor image quality, among other difficulties inherent to infant brain MRI, e.g., the isointense contrast between white and gray matter and the severe partial volume effect due to small brain sizes. This study investigates the problem with an ensemble of semi-dense fully convolutional neural networks (CNNs), which employs T1-weighted and T2-weighted MR images as input. We demonstrate that the ensemble agreement is highly correlated with the segmentation errors. Therefore, our method provides measures that can guide local user corrections. To the best of our knowledge, this work is the first ensemble of 3D CNNs for suggesting annotations within images. Furthermore, inspired by the very recent success of dense networks, we propose a novel architecture, SemiDenseNet, which connects all convolutional layers directly to the end of the network. Our architecture allows the efficient propagation of gradients during training, while limiting the number of parameters, requiring one order of magnitude less parameters than popular medical image segmentation networks such as 3D U-Net. Another contribution of our work is the study of the impact that early or late fusions of multiple image modalities might have on the performances of deep architectures. We report evaluations of our method on the public data of the MICCAI iSEG-2017 Challenge on 6-month infant brain MRI segmentation, and show very competitive results among 21 teams, ranking first or second in most metrics.