EncapNet-3D and U-EncapNet for Cell Segmentation

Abstract

EncapNet is a kind of Capsule network that significantly improved routing problems that has thought to be the main bottleneck of capsule network. In this paper, we propose EncapNet-3D that has stronger connection between master and aide branch, which original EncapNet has only single co-efficient per capsule. We achieved this by adding 3D convolution and Dropout layers to connection between them. 3D convolution makes connection between capsules stronger. We also propose U-EncapNet, which uses U-net architecture to achieve high accuracy in semantic segmentation task. EncapNet-3D has successfully accomplished to reduce network parameters 321 times smaller compared to U-EncapNet, 52 times smaller than U-net. We show the result on segmentation problem of cell images. U-EncapNet has advanced performance of 1.1% in cell mean IoU in comparison with U-net. EncapNet-3D has achieved 3% increase in comparison with ResNet-6 in cell membrane IoU.