Presentation 2022-03-03
A Study on Automatic Segmentation Method for Plant Specimen Images Using U-Net
Depeng Zhang, Yasuhiko Higaki, Yasuo Sugai,
PDF Download Page PDF download Page Link
Abstract(in Japanese) (See Japanese page)
Abstract(in English) the image processing required to digitize images of plant specimens. Digitized images of plant specimens are very diverse and contain visual noise and undisclosed information. As a pre-processing step for downstream deep learning applications, systematic removal of noise and segmentation of plant bodies is required. In this study, we developed a workflow that uses deep learning to segment and remove background from plant bodies in plant specimen images. A combination of automatic and manual tools was used to generate ground truth masks, train U-Net, and automatically segment the specimen images with good results.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) Plant specimen image / Deep learning / Automatic segmentation / U-Net
Paper # LOIS2021-47
Date of Issue 2022-02-24 (LOIS)

Conference Information
Committee LOIS
Conference Date 2022/3/3(1days)
Place (in Japanese) (See Japanese page)
Place (in English) Online
Topics (in Japanese) (See Japanese page)
Topics (in English)
Chair Toru Kobayashi(Nagasaki Univ.)
Vice Chair Hiroyuki Toda(NTT)
Secretary Hiroyuki Toda(Nagasaki Univ.)
Assistant Kazuki Fukae(Nagasaki Univ.)

Paper Information
Registration To Technical Committee on Life Intelligence and Office Information Systems
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) A Study on Automatic Segmentation Method for Plant Specimen Images Using U-Net
Sub Title (in English)
Keyword(1) Plant specimen image
Keyword(2) Deep learning
Keyword(3) Automatic segmentation
Keyword(4) U-Net
1st Author's Name Depeng Zhang
1st Author's Affiliation Chiba University(Chiba Univ.)
2nd Author's Name Yasuhiko Higaki
2nd Author's Affiliation Chiba University(Chiba Univ.)
3rd Author's Name Yasuo Sugai
3rd Author's Affiliation Chiba University(Chiba Univ.)
Date 2022-03-03
Paper # LOIS2021-47
Volume (vol) vol.121
Number (no) LOIS-401
Page pp.pp.45-50(LOIS),
#Pages 6
Date of Issue 2022-02-24 (LOIS)