Infant Incubator Monitoring System

Pim Moeskops And Josien P.W. Pluim

Abstract. Quantitative analysis of brain MRI at the age of 6 months is difficult because of the limited contrast between white matter and gray matter. In this study, we use a dilated triplanar convolutional neural net- work in combination with a non-dilated 3D convolutional neural network for the segmentation of white matter, gray matter and cerebrospinal fluid in infant brain MR images, as provided by the MICCAI grand challenge on 6-month infant brain MRI segmentation.

Introduction

Brain MRI is important for quantitative analysis of neurodevelopment at all ages. A particularly difficult age in the context of brain MRI analysis is the age of 6 months, where the contrast between white matter and gray matter is limited because of the development of myelination .

Convolutional neural networks (CNNs) are an effective approach for infant brain MRI segmentation . A specific type of CNNs proposed for image segmentation are dilated CNNs, which can achieve a large receptive field using a limited number of trainable weights . In medical imaging, dilated CNNs have been used for cardiac MRI segmentation .

In this study, we investigate using a dilated triplanar CNN in combination with a non-dilated 3D CNN for the segmentation of brain MRI of infants at an age of 6 months.

Data

The data used in this study is provided by the MICCAI grand challenge on 6- month infant brain MRI segmentation1. 10 images are provided as training data, 13 images are used to evaluate the method in the challenge. For all patients, T1- and T2-weighted images are provided.

To allow evaluation of the performance in initial experiments, 3 training images were randomly selected as validation set, the remaining 7 images are used to train the method.

1 This paper is a submission to the MICCAI grand challenge on 6-month infant brain MRI segmentation (http://iseg2017.web.unc.edu/).

2

P. Moeskops et al. The images were preprocessed by the organisers of the challenge, this included skull stripping, intensity inhomogeneity correction and removal of the cerebellum and brain stem. As a reference standard, the images were manually segmented into white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF).

Method

A CNN with triplanar dilated convolutions and 3D convolutions in four network branches is used to segment the images into WM, GM and CSF (Table 1). The dilated branches consist of the same architecture as proposed by Yu et al. , which uses layers of 3 × 3 kernels with increasing dilation factors. Input for the dilated branches is obtained from the axial, sagittal and coronal planes, as also proposed by Wolterink et al. . In contrast, our network uses three network branches that are combined in the last layer, instead of processing in three directions and averaging of the output probabilities. Moreover, in addition to these three dilated triplanar network branches, we include an additional 3D network with non-dilated 3D convolutions. All four network branches use 2- channel input from the T1- and the T2-weighted images.

3D

Kernels Size Dilation Kernels Size Dilation Kernels Size Dilation Kernels

1×1×1

Table 1: Combined 7-layer dilated triplanar CNN and 12-layer 3D CNN archi- tecture. The dilated branches have receptive fields of 67 × 67 and the 3D branch has a receptive field of 25 × 25 × 25. Because the background class is already defined by skull stripping, three output classes are used in the last layer (WM, GM and CSF).

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