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Paper Abstract and Keywords
Presentation 2022-01-27 15:22
Investigation on Blood Vessels Segmentation from Images of Mouse's Cranial Window Based on Deep Learning
Yunheng Wu, Masahiro Oda, Yuichiro Hayashi (Nagoya Univ.), Takanori Takebe (TMDU), Kensaku Mori (Nagoya Univ.) MI2021-84
Abstract (in Japanese) (See Japanese page) 
(in English) In this paper, we utilized the U-Net for blood vessels segmentation from stereo microscope images of mouse's cranial window (CW). Organoids are a kind of tissue with similar functions to human organs and they can be cultured in CW which is an optically accessible window for observing transplanted tissue after craniotomy. In order to increase the success rate of organoids transplantation, it is important to transplant on the mouse blood vessel and realize early vascular anastomosis. Therefore, it is very important to extract the blood vessels in the mouse's CW with high accuracy for the automatic transplantation and operation of organoids. However, the previous method based on the line detector did not achieve good performance for small blood vessel segmentation. Therefore, in this paper we use the U-Net to segment blood vessels from stereo microscope images of mouse's CW. The experimental results showed that the U-Net is more effective in extracting blood vessels from stereo microscope images of mouse's CW compared with the previous method.
Keyword (in Japanese) (See Japanese page) 
(in English) Cranial Window / Blood Vessels Segmentation / Retinal Fundus Images / / / / /  
Reference Info. IEICE Tech. Rep., vol. 121, no. 347, MI2021-84, pp. 174-179, Jan. 2022.
Paper # MI2021-84 
Date of Issue 2022-01-18 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2021-84

Conference Information
Committee MI  
Conference Date 2022-01-25 - 2022-01-27 
Place (in Japanese) (See Japanese page) 
Place (in English) Online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To MI 
Conference Code 2022-01-MI 
Language English (Japanese title is available) 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Investigation on Blood Vessels Segmentation from Images of Mouse's Cranial Window Based on Deep Learning 
Sub Title (in English)  
Keyword(1) Cranial Window  
Keyword(2) Blood Vessels Segmentation  
Keyword(3) Retinal Fundus Images  
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1st Author's Name Yunheng Wu  
1st Author's Affiliation Nagoya University (Nagoya Univ.)
2nd Author's Name Masahiro Oda  
2nd Author's Affiliation Nagoya University (Nagoya Univ.)
3rd Author's Name Yuichiro Hayashi  
3rd Author's Affiliation Nagoya University (Nagoya Univ.)
4th Author's Name Takanori Takebe  
4th Author's Affiliation Tokyo Medical and Dental University (TMDU)
5th Author's Name Kensaku Mori  
5th Author's Affiliation Nagoya University (Nagoya Univ.)
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Speaker Author-1 
Date Time 2022-01-27 15:22:00 
Presentation Time 13 minutes 
Registration for MI 
Paper # MI2021-84 
Volume (vol) vol.121 
Number (no) no.347 
Page pp.174-179 
#Pages 6 
Date of Issue 2022-01-18 (MI) 

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