Investigations of the Influences of a CNN's Receptive Field on Segmentation of Subnuclei of Bilateral Amygdalae
This work addresses segmentation of varied-sized objects in medical imaging, which is challenging and less explored, but it is incremental as it applies an existing dual-branch FCNN method to a specific domain.
The study investigated how a CNN's receptive field size affects segmentation accuracy for objects of varying sizes, specifically segmenting four subnuclei of bilateral amygdalae in 3D MRI images from 14 subjects, finding that AmygNet with multiple receptive fields shows great potential for this task.
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.