Brain tissue segmentation is a prerequisite for many subsequent automatic quantitative analysis techniques. As with many medical imaging tasks, a shortage of manually annotated training data is a limiting factor which is not easily overcome, particularly using recent deep-learning technology. We present a deep convolutional neural network (CNN) trained on just 2 publicly available manually annotated volumes, trained to annotate 8 tissue types in neonatal T2 MRI. The network makes use of several recent deep-learning techniques as well as artificial augmentation of the training data, to achieve state-of-the- art results on public challenge data

Boylan, Geraldine B.

McGuinness, Kevin

Murphy, Keelin

Smeaton, Alan F.

DCU Online Research Access Service

A Deep Convolutional Neural Network forBrain Tissue Segmentation in Neonatal MRIKeelin Murphy, Geraldine B. Boylan, Alan Smeaton, Kevin McGuinness1. INFANT Research Centre, University College Cork, Ireland2. INSIGHT Research Centre, Dublin City University, Ireland1 1 2 2This work was carried out at the Irish Centre for Fetal and Neonatal Translational Research (INFANT) and was funded in part (or whole) by Science Foundation Ireland (12/RC/2272)IntroductionMethodResultsConclusionContactBrain tissue segmentation is a prerequisite for many subsequent automatic quantitative analysis techniques. As with many medical imaging tasks, a shortage of manually annotated training data is a limiting factor which is not easily overcome, particularly using recent deep-learning technology.  We present a deep convolutional neural network (CNN) trained on just 2 publicly available manually annotated volumes, trained to annotate 8 tissue types in neonatal T2 MRI.  The network makes use of several recent deep-learning techniques as well as artiﬁcial augmentation of the training data, to achieve state-of-the-art results on public challenge data.A fully convolutional neural network (see ﬁgure 1) with a total of 11 layers  is trained to map T2-weighted images to tissue segmentations.  The training data consists of just 2 axial volumes, available publicly via the neobrains12 challenge[1].  Eight diﬀerent tissue-types are segmented as illustrated in ﬁgure 3 (Results). Training data is augmented by applying a number of random perturbations to every slice extracted during training, as shown in ﬁgure 2.   This allows the network to learn variations which may not be present in the limited training dataNetwork technical details: The network uses batch-normalization, dilated convolutions and stacked residual layers.  The Adam optimizer is used and loss is calculated by cross-entropy. The trained network was used to segment tissue types on a further 5 axial T2-weighted scans which are provided by the neobrains12 public challenge[1].  Evaluation was performed independently by the challenge organisers and the reference standard is never seen by participants.  Compared to other participating algorithms, our method obtained the highest median Dice Coeﬃcient across various tissue types (ﬁgure 3).  The median value for Mean-Surface-Distance was second highest, being surpassed only by a participant who had access to additional related training data.  Figure 4 shows sample results on test data from the challenge.ReferencesFigure 1: (a) A schematic drawing of a fully convolutional neural network with T2-weighted MRI as input and segmentation data as output.  (b) A numerical demonstration of how a convolution with input values (blue) with weights (yellow) results in output (green).  (c) An illustration of how dilated convolution spans a larger region at no additional computational expense.(a)(b)(c)Figure 2: A demonstration of how  training data is augmented by adding subtle random variations to the original data.Original  Slice +    Rotation +     Aspect           ratio+   Gamma       (colour)+      Noise +      WarpFigure 3: Plots showing how the presented method (left) performs against comparable methods in the neobrains12 challenge[1] in terms of Dice Coeﬃcient for overlap.  Coloured points show mean performance for each tissue type, while box-plots summarise this data. **Indicates participants with access to related additional dataFigure 4: Sample network segmentation results on neobrains12 [1] test data.  Tissues are cerebellum (orange), cortex (darkest blue), white-matter (lightest blue), cerebro-spinal ﬂuid (dark red), ventricles (light red), brain-stem (yellow), basal-ganglia/thalamus (mid blue), myelination (light green).Deep learning using the appropriate network architecture and data augmentation techniques have enabled state-of-the-art segmentation results using minimal training data.  The presented method performs better than others which had access to additional related training scans.  This is a promising result for deep learning in the medical imaging domain where a shortage of training data is typically a signiﬁcant issue.keelin.murphy@ucc.ie 1. neobrains12.isi.uu.nlIsgum et al, Evaluation of automatic neonatal brain segmentation algorithms: The NeoBrainS12 challenge, Medical Image Analysis, 2015. 

A deep convolutional neural network for brain tissue segmentation in Neonatal MRI

Boylan, Geraldine B

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A deep convolutional neural network for brain tissue segmentation in Neonatal MRI

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