Brain Tumor Segmentation Using Concurrent Fully Convolutional Networks and Conditional Random Fields

被引：9

作者：

Shen, Guangyu ^{[1
]}

Ding, Yi ^{[1
]}

Lan, Tian ^{[1
]}

Chen, Hao ^{[1
]}

Qin, Zhiguang ^{[1
]}

机构：

[1] Univ Elect Sci & Technol, Sch Software & Engn, Chengdu, Sichuan, Peoples R China

来源：

PROCEEDINGS OF 2018 THE 3RD INTERNATIONAL CONFERENCE ON MULTIMEDIA AND IMAGE PROCESSING (ICMIP 2018) | 2018年

基金：

中国国家自然科学基金;

关键词：

component; Deep Learning; concurrent fully convolutional networks; conditional random fields; multimodal MRI; brain tumor segmentation; MEDICAL IMAGE SEGMENTATION;

D O I：

10.1145/3195588.3195590

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

We propose a concurrent Fully Convolutional Networks(CFCN) structure which contains three Fully Convolutional Networks(FCN). Gaussian filter, Mean filter and Median filter are chosen to pre-process the original multimodal MR images. Then, we fuse the results from three networks. Finally, a Fully Connected Conditional Random Field (Fully Connected CRF) is used to accomplish the post-processing, improving the model's ability of detecting minute structures. Our model was trained and evaluated on BRATS 2015 challenge dataset. The results show that our model provides promising segmentations.

引用

页码：24 / 30

页数：7

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