报告详情
An Automatic Method for Brain Tumors Segmentation Based on Deep Convolutional Neural Network
编号：66
访问权限：仅限参会人
更新：2021-11-08 09:25:08
浏览：1160次
口头报告
报告开始：2021年11月13日 16:15（Asia/Shanghai）
报告时间：15min
所在会场：[PS1] Plenary Session 1 [AI1] Workshop on AI
暂无文件
摘要
[Purpose] Accurate outline of tumor targets is critical to a high quality radiotherapy plan. Manual segmentation is of great workload and has a strong artificial subjectivity. Using deep learning method to assist automatically segmenting of tumor target is the penetration and application of artificial intelligence in medicine.
[Methods] A 6-layer model of deep Convolution Neural Network (CNN) has been constructed by taking advantage of different types of layers for brain tumor segmentation. This model is a 6 layer CNN model (6-CNN) composed of three convolution layers, two pool layers and one full connection layer. To obtain enough samples for 6-CNN model training, a patch-based technology has been adopted. That is to successively extract a local area from the whole image as a patch. And the center pixel value is taken as the pixel value of the whole patch. Similarly, the label of the center pixel is also taken as the label of the whole patch. Thus the 6-CNN model transforms the brain tumor image segmentation into patch classification based on the excellent classification characteristics of deep convolution neural network. The model combines the local features of patch, the information extracted from shallow network and the global features to predict the category label of the central pixel of patch.
[Results] The model is validated on BRATS 2015 dataset and results show that the segmentation accuracy can be up to Dice Similarity Coefficient (DSC) 90%±4%.
[Conclusions] An automatic deep CNN segmentation model for brain tumors has been constructed based on MRI image patches, which is expected to assist or even substitute the manual segmentation of brain tumors.
[Methods] A 6-layer model of deep Convolution Neural Network (CNN) has been constructed by taking advantage of different types of layers for brain tumor segmentation. This model is a 6 layer CNN model (6-CNN) composed of three convolution layers, two pool layers and one full connection layer. To obtain enough samples for 6-CNN model training, a patch-based technology has been adopted. That is to successively extract a local area from the whole image as a patch. And the center pixel value is taken as the pixel value of the whole patch. Similarly, the label of the center pixel is also taken as the label of the whole patch. Thus the 6-CNN model transforms the brain tumor image segmentation into patch classification based on the excellent classification characteristics of deep convolution neural network. The model combines the local features of patch, the information extracted from shallow network and the global features to predict the category label of the central pixel of patch.
[Results] The model is validated on BRATS 2015 dataset and results show that the segmentation accuracy can be up to Dice Similarity Coefficient (DSC) 90%±4%.
[Conclusions] An automatic deep CNN segmentation model for brain tumors has been constructed based on MRI image patches, which is expected to assist or even substitute the manual segmentation of brain tumors.
关键词
Accurately radiotherapy,Brain Tumor Segmentation,Deep Learning,Convolutional Neural Network
报告人
辉 林
合肥工业大学稿件作者
全部评论
重要日期
-
会议日期
11月13日
2021
至11月14日
2021
-
09月30日 2021
报告提交截止日期
-
11月14日 2021
注册截止日期
主办单位
IEEE北京分会
中国生物医学工程学会医学物理分会
中国电子学会生命电子学分会
中国生物医学工程学会医学物理分会
中国电子学会生命电子学分会
承办单位
中国科学技术大学
安徽省生物医学工程学会
安徽省生物医学工程学会
联系方式
- Mrs. Yang
- ic******@ustc.edu.cn
- +86********
历届会议
-
2017年11月04日 中国 Beijing,China
2017 International Conference on Medical Imaging Physics and Engineering -
2017年11月03日 中国 海淀区
2017 IEEE 国际医学影像物理和工程大会 -
2013年10月19日 中国 沈阳市
2013 IEEE国际医学影像物理和工程大会暨第七届中国医学影像物理学术年会
发表评论