Accurate Automatic Segmentation of Amygdala Subnuclei and Modeling of Uncertainty via Bayesian Fully Convolutional Neural Network

by   Yilin Liu, et al.

Recent advances in deep learning have improved the segmentation accuracy of subcortical brain structures, which would be useful in neuroimaging studies of many neurological disorders. However, most of the previous deep learning work does not investigate the specific difficulties that exist in segmenting extremely small but important brain regions such as the amygdala and its subregions. To tackle this challenging task, a novel 3D Bayesian fully convolutional neural network was developed to apply a dilated dualpathway approach that retains fine details and utilizes both local and more global contextual information to automatically segment the amygdala and its subregions at high precision. The proposed method provides insights on network design and sampling strategy that target segmentations of small 3D structures. In particular, this study confirms that a large context, enabled by a large field of view, is beneficial for segmenting small objects; furthermore, precise contextual information enabled by dilated convolutions allows for better boundary localization, which is critical for examining the morphology of the structure. In addition, it is demonstrated that the uncertainty information estimated from our network may be leveraged to identify atypicality in data. Our method was compared with two state-of-the-art deep learning models and a traditional multi-atlas approach, and exhibited excellent performance as measured both by Dice overlap as well as average symmetric surface distance. To the best of our knowledge, this work is the first deep learning-based approach that targets the subregions of the amygdala.



There are no comments yet.


page 6

page 14

page 15

page 18

page 20


A Deep Attentive Convolutional Neural Network for Automatic Cortical Plate Segmentation in Fetal MRI

Fetal cortical plate segmentation is essential in quantitative analysis ...

A Deep Learning Approach for Semantic Segmentation of Unbalanced Data in Electron Tomography of Catalytic Materials

Heterogeneous catalysts possess complex surface and bulk structures, rel...

Brain segmentation based on multi-atlas guided 3D fully convolutional network ensembles

In this study, we proposed and validated a multi-atlas guided 3D fully c...

Bayesian convolutional neural network based MRI brain extraction on nonhuman primates

Brain extraction or skull stripping of magnetic resonance images (MRI) i...

Vortex Pooling: Improving Context Representation in Semantic Segmentation

Semantic segmentation is a fundamental task in computer vision, which ca...

SegET: Deep Neural Network with Rich Contextual Features for Cellular Structures Segmentation in Electron Tomography Image

Electron tomography (ET) allows high-resolution reconstructions of macro...

FastSurfer – A fast and accurate deep learning based neuroimaging pipeline

Traditional neuroimage analysis pipelines involve computationally intens...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.