Data Management And Sharing System of China National Clinical Research Center for Neurological Diseases
A deep learning model integrating FCNNs and CRFs for brain tumor segmentation
First author：Zhao Xiaomei
Corresponding author：Fan Yong
essential_information
- Person in charge：Zhao Xiaomei
- Data Sources： OTHERS
- Number of papers：OTHERS20190608
- Published time：2018-01-01
2018-01-01
Published articles
Journal：Medical Image Analysis
Publication type: Article
IF(IF): 8.545
PMID: 29040911 Link To Pubmed Full text Download
Background And Purpose
Method
We train a deep learning based segmentation model using 2D image patches and image slices in following steps: 1) training FCNNs using image patches; 2) training CRFs as Recurrent Neural Networks (CRF-RNN) using image slices with parameters of FCNNs fixed; and 3) finetuning the FCNNs and the CRF-RNN using image slices. Particularly, we train 3 segmentation models using 2D image patches and slices obtained in axial, coronal and sagittal views respectively, and combine them to segment brain tumors using a voting based fusion strategy.
Conclusion
The experimental results have demonstrated that our method could build a segmentation model with Flair, T1c, and T2 scans and achieve competitive performance as those built with Flair, T1, T1c, and T2 scans.
Main results
Our method could segment brain images slice-by-slice, much faster than those based on image patches. We have evaluated our method based on imaging data provided by the Multimodal Brain Tumor Image Segmentation Challenge (BRATS) 2013, BRATS 2015 and BRATS 2016.