INVESTIGATIONS OF THE
INFLUENCES OF A CNN’S RECEPTIVE FIELD ON SEGMENTATION OF SUBNUCLEI OF BILATERAL
AMYGDALAE
Han Bao1
School of Computer
Science, University of Nottingham Ningbo China, Ningbo, China
ABSTRACT
Segmentation of objects with
various sizes is relatively less explored in medical imaging, and has been very
challenging in computer vision tasks in general. We hypothesize that the
receptive field of a deep model corresponds closely to the size of object to be
segmented, which could critically influence the segmentation accuracy of
objects with varied sizes. In this study, we employed “AmygNet”, a dual-branch
fully convolutional neural network (FCNN) with two different sizes of receptive
fields, to investigate the effects of receptive field on segmenting four major
subnuclei of bilateral amygdalae. The experiment was conducted on 14 subjects,
which are all 3-dimensional MRI human brain images. Since the scale of
different subnuclear groups are different, by investigating the accuracy of
each subnuclear group while using receptive fields of various sizes, we may
find which kind of receptive field is suitable for object of which scale
respectively. In the given condition, AmygNet with multiple receptive fields
presents great potential in segmenting objects of different sizes.
KEYWORDS
Receptive Field, AmygNet,
Segmentation, Subnuclear Group
.png)
.png)