标题： 对卷积神经网络感受野对双侧杏仁核亚核分割影响的研究 显示英文标题

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

摘要： 在医学影像中，对不同大小物体的分割研究相对较少，且在计算机视觉任务中一直是非常具有挑战性的。 我们假设深度模型的感受野与待分割物体的大小密切相关，这可能会显著影响不同大小物体的分割精度。 在本研究中，我们采用了“AmygNet”，一种具有两个不同感受野大小的双分支全卷积神经网络（FCNN），以研究感受野对分割双侧杏仁核四个主要亚核的影响。 实验是在14个受试者上进行的，这些受试者均为三维MRI人脑图像。 由于不同亚核组的尺度不同，通过在使用不同大小感受野的情况下调查每个亚核组的准确性，我们可以发现哪种感受野分别适用于哪种尺度的物体。 在给定条件下，具有多个感受野的AmygNet在分割不同大小的物体方面表现出巨大的潜力。 显示英文摘要

摘要： 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.