Open AccessAuto Whole Heart Segmentation from CT images Using an Improved Unet-GAN
Author(s) -
Kening Le,
Zeyu Lou,
Wei-Liang Huo,
Xiaolin Tian
Publication year - 2021
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1769/1/012016
Subject(s) - discriminative model , segmentation , computer science , artificial intelligence , generative adversarial network , deep learning , convolutional neural network , network architecture , pattern recognition (psychology) , modality (human–computer interaction) , architecture , image segmentation , computer vision , machine learning , computer network , art , visual arts
The development of deep learning is rapid, and convolutional neural network especially U-Net plays an important role in the medical image segmentation tasks, which is lack of data. Lots of models and methods are proposed to segment cardiac CT images. In this paper, we proposed a new network architecture. The network architecture is based on a traditional architecture called conditional generative adversarial network (cGAN), where R2U-Net acts as the generative network and FCN as the discriminative network. The performance of this model running on the dataset from MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge (MM-WHS 2017) is good.