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Abstract #3489

Cascaded 3D fully convolutional neural network for segmenting amygdala and its subnuclei

Yilin Liu1, Brendon Nacewicz1, Gregory Kirk1, Andrew Alexander1, and Nagesh Adluru1

1University of Wisconsin Madison, Madison, WI, United States

We address the problem of segmenting subcortical brain structures that have small spatial extent but are associated with many neuropsychiatric disorders and neurodegenerative diseases. Specifically, we focused on the segmentation of amygdala and its subnuclei. Most existing methods including deep learning based focus on segmenting larger structures and the existing architectures do not perform well on smaller structures. Hence we designed a new cascaded fully convolutional neural network with architecture that can perform well even on small structures with limited training data. Several key characteristics of our architecture: (1) 3D convolutions (2) deep network with small kernels (3) no pooling layers.

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