Investigations of the Influences of a CNN's Receptive Field on Segmentation of Subnuclei of Bilateral Amygdalae

TL;DR

This study explores how the receptive field size of CNNs affects the segmentation accuracy of amygdala subnuclei in MRI images, demonstrating that multi-receptive field models improve segmentation of varied-sized objects.

Contribution

Introduces AmygNet, a dual-branch FCNN with multiple receptive fields, to analyze the impact of receptive field size on segmenting subnuclei of the amygdala.

Findings

Multi-receptive field AmygNet improves segmentation accuracy.

Receptive field size correlates with object scale in segmentation.

Potential for better segmentation of varied-sized objects in medical imaging.

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.