A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning-Reference-Cited by-同舟云学术

A Deep Learning Model for Automatic Segmentation of Intraparenchymal and Intraventricular Hemorrhage for Catheter Puncture Path Planning

Author:

Tong Guoyu¹^ORCID,Wang Xi¹,Jiang Huiyan¹^ORCID,Wu Anhua²^ORCID,Cheng Wen²^ORCID,Cui Xiao³,Bao Long⁴,Cai Ruikai²,Cai Wei⁵^ORCID

Affiliation:

1. Software College, Northeastern University, Shenyang, China

2. Department of Neurosurgery, Shengjing Hospital of China Medical University, Shenyang, China

3. Department of Neurosurgery, The First Hospital of China Medical University, Shenyang, China

4. Department of Neurosurgery, The First Affiliated Hospital of Jinzhou Medical University, Jinzhou, China

5. Neusoft Research of Intelligent Healthcare Technology, Company Ltd., Shenyang, China

Funder

National Key Research and Development Program of China

Natural Science Foundation of Liaoning Province

National Natural Science Foundation of China

Science and Technology Planning Project of Liaoning Province

Shenyang Medical-Industry Integration Collaborative Innovation Research Project

345 talent Project of Shengjing Hospital

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Health Information Management,Electrical and Electronic Engineering,Computer Science Applications,Health Informatics

Link

Reference35 articles.

1. Detection and segmentation of hemorrhage stroke using textural analysis on brain CT images;ali;Int J Soft Comput Eng,2015

5. U-net: Convolutional networks for biomedical image segmentation;ronneberger;Proc Int Conf Med Image Comput Comput - Assist Interv,0

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