2D to 3D Evolutionary Deep Convolutional Neural Networks for Medical Image Segmentation

IEEE Trans Med Imaging. 2021 Feb;40(2):712-721. doi: 10.1109/TMI.2020.3035555. Epub 2021 Feb 2.


Developing a Deep Convolutional Neural Network (DCNN) is a challenging task that involves deep learning with significant effort required to configure the network topology. The design of a 3D DCNN not only requires a good complicated structure but also a considerable number of appropriate parameters to run effectively. Evolutionary computation is an effective approach that can find an optimum network structure and/or its parameters automatically. Note that the Neuroevolution approach is computationally costly, even for developing 2D networks. As it is expected that it will require even more massive computation to develop 3D Neuroevolutionary networks, this research topic has not been investigated until now. In this article, in addition to developing 3D networks, we investigate the possibility of using 2D images and 2D Neuroevolutionary networks to develop 3D networks for 3D volume segmentation. In doing so, we propose to first establish new evolutionary 2D deep networks for medical image segmentation and then convert the 2D networks to 3D networks in order to obtain optimal evolutionary 3D deep convolutional neural networks. The proposed approach results in a massive saving in computational and processing time to develop 3D networks, while achieved high accuracy for 3D medical image segmentation of nine various datasets.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Image Processing, Computer-Assisted
  • Imaging, Three-Dimensional*
  • Neural Networks, Computer*