Automatic 3D Medical Image Segmentation with Densely-Connected Volumetric ConvNets

Authors
Zhu, Qikui
Du, Bo
Yan, Pingkun

Issue Date
2019

Publisher
University of Minnesota Libraries Publishing

Type
Article

Abstract
The Automated medical image segmentation in 3D medical images play an important role in many clinical applications, such as diagnosis of prostatitis, medical image cancer and enlarged medical image. However, it is still a challenging task due to the complex background, lacking of clear boundary and various shape and texture between the slices. In this paper, we propose a novel 3D convolutional neural network with densely-connected layers to automatically segment the medical image. Compared with other methods, our method has three compelling advantages. First, our model can effectively detect the medical image region in a volume-to-volume manner by utilizing the 3D convolution rather than the 2D convolution, which can fully exploit both spatial and region information. Second, the proposed network architecture alleviates the vanishing-gradient problem, strengthens the information propagation between layers, overcomes the problem of overfitting and makes the network deeper by adopting a densely-connected manner. Third, besides the densely-connected manner inside each block, we also adopt the long connections strategy between blocks.

Identifiers
doi: 10.24926/548719.048