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. 2019 Feb:10949:109490K.
doi: 10.1117/12.2512119. Epub 2019 Mar 15.

Cerebellum Parcellation with Convolutional Neural Networks

Shuo Han  1   2 , Yufan He  3 , Aaron Carass  3   4 , Sarah H Ying  5 , Jerry L Prince  1   3   4
Affiliations

Cerebellum Parcellation with Convolutional Neural Networks

Shuo Han et al. Proc SPIE Int Soc Opt Eng. 2019 Feb.

Abstract

To better understand cerebellum-related diseases and functional mapping of the cerebellum, quantitative measurements of cerebellar regions in magnetic resonance (MR) images have been studied in both clinical and neurological studies. Such studies have revealed that different spinocerebellar ataxia (SCA) subtypes have different patterns of cerebellar atrophy and that atrophy of different cerebellar regions is correlated with specific functional losses. Previous methods to automatically parcellate the cerebellum-that is, to identify its sub-regions-have been largely based on multi-atlas segmentation. Recently, deep convolutional neural network (CNN) algorithms have been shown to have high speed and accuracy in cerebral sub-cortical structure segmentation from MR images. In this work, two three-dimensional CNNs were used to parcellate the cerebellum into 28 regions. First, a locating network was used to predict a bounding box around the cerebellum. Second, a parcellating network was used to parcellate the cerebellum using the entire region within the bounding box. A leave-one-out cross validation of fifteen manually delineated images was performed. Compared with a previously reported state-of-the-art algorithm, the proposed algorithm shows superior Dice coefficients. The proposed algorithm was further applied to three MR images of a healthy subject and subjects with SCA6 and SCA8, respectively. A Singularity container of this algorithm is publicly available.

Keywords: Cerebellum; convolutional neural network; parcellation.

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Figures

Figure 1.
Figure 1.
Flowchart of the proposed algorithm. Before and after post-processing difference is marked by a yellow arrow.
Figure 2.
Figure 2.
Architecture of the locating network. The output number of features are marked on each block.
Figure 3.
Figure 3.
Architecture of the parcellating network. The output number of feature maps are marked on each block. The input spatial shape is marked on the left of each contracting block.
Figure 4.
Figure 4.
Architecture of building blocks: (a) input block, (b) contracting block, and (c) expanding block.
Figure 5.
Figure 5.
Data augmentation: (a) the original image, (b) the flipped image, (c) the translated image, (d) the rotated image, and (e) the deformed image, overlaid with their corresponding parcellation.
Figure 6.
Figure 6.
Comparison with CGCUTS. Significantly improved regions are marked by asterisks (p < 0.05); red asterisks for p-values and blue asterisks for adjusted p-values. CM: corpus medullare, L: left, R: right, and V: vermis.
Figure 7.
Figure 7.
Cerebellum parcellation by the manual rater and three different algorithms on three coronal slices: (a) image, (b) manual rater, (c) CGCUTS, (d) CNNs without post-processing, and (e) CNNs with post-processing. CM: corpus medullare, L: left, R: right, and V: vermis.
Figure 8.
Figure 8.
Parcellation results on three coronal slices: (a) healthy control, (b) subject with SCA6, and (c) subject with SCA8. CM: corpus medullare, L: left, R: right, and V: vermis.
See this image and copyright information in PMC

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