CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data

Ali Selman Aydin; Abhinandan Dubey; Daniel Dovrat; Amir Aharoni; Roy Shilkrot

doi:10.1109/CVPRW.2017.105

CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data

Ali Selman Aydin
, Abhinandan Dubey
, Daniel Dovrat
, Amir Aharoni
, Roy Shilkrot

Department of Life Sciences

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

25 Scopus citations

Abstract

We present a method for foreground segmentation of yeast cells in the presence of high-noise induced by intentional low illumination, where traditional approaches (e.g., threshold-based methods, specialized cell-segmentation methods) fail. To deal with these harsh conditions, we use a fully-convolutional semantic segmentation network based on the SegNet architecture. Our model is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments. Further, we show that simultaneous multi-modal observations of bio-fluorescent markers can result in better segmentation performance than the DIC channel alone.

Original language	English
Title of host publication	Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
Publisher	Institute of Electrical and Electronics Engineers
Pages	753-759
Number of pages	7
ISBN (Electronic)	9781538607336
DOIs	https://doi.org/10.1109/CVPRW.2017.105
State	Published - 22 Aug 2017
Event	30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 - Honolulu, United States Duration: 21 Jul 2017 → 26 Jul 2017

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume	2017-July
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017
Country/Territory	United States
City	Honolulu
Period	21/07/17 → 26/07/17

ASJC Scopus subject areas

Computer Vision and Pattern Recognition
Electrical and Electronic Engineering

Access to Document

10.1109/CVPRW.2017.105

Cite this

Aydin, A. S., Dubey, A., Dovrat, D., Aharoni, A., & Shilkrot, R. (2017). CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 (pp. 753-759). Article 8014839 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2017-July). Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/CVPRW.2017.105

Aydin, Ali Selman ; Dubey, Abhinandan ; Dovrat, Daniel et al. / CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data. Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. Institute of Electrical and Electronics Engineers, 2017. pp. 753-759 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

@inproceedings{b888d49cddbe4d59897f81adf68c42e8,

title = "CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data",

abstract = "We present a method for foreground segmentation of yeast cells in the presence of high-noise induced by intentional low illumination, where traditional approaches (e.g., threshold-based methods, specialized cell-segmentation methods) fail. To deal with these harsh conditions, we use a fully-convolutional semantic segmentation network based on the SegNet architecture. Our model is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments. Further, we show that simultaneous multi-modal observations of bio-fluorescent markers can result in better segmentation performance than the DIC channel alone.",

author = "Aydin, \{Ali Selman\} and Abhinandan Dubey and Daniel Dovrat and Amir Aharoni and Roy Shilkrot",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017 ; Conference date: 21-07-2017 Through 26-07-2017",

year = "2017",

month = aug,

day = "22",

doi = "10.1109/CVPRW.2017.105",

language = "English",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

publisher = "Institute of Electrical and Electronics Engineers",

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Aydin, AS, Dubey, A, Dovrat, D, Aharoni, A & Shilkrot, R 2017, CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data. in Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017., 8014839, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, vol. 2017-July, Institute of Electrical and Electronics Engineers, pp. 753-759, 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017, Honolulu, United States, 21/07/17. https://doi.org/10.1109/CVPRW.2017.105

CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data. / Aydin, Ali Selman; Dubey, Abhinandan; Dovrat, Daniel et al.
Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. Institute of Electrical and Electronics Engineers, 2017. p. 753-759 8014839 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2017-July).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

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AU - Dovrat, Daniel

AU - Aharoni, Amir

AU - Shilkrot, Roy

PY - 2017/8/22

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N2 - We present a method for foreground segmentation of yeast cells in the presence of high-noise induced by intentional low illumination, where traditional approaches (e.g., threshold-based methods, specialized cell-segmentation methods) fail. To deal with these harsh conditions, we use a fully-convolutional semantic segmentation network based on the SegNet architecture. Our model is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments. Further, we show that simultaneous multi-modal observations of bio-fluorescent markers can result in better segmentation performance than the DIC channel alone.

AB - We present a method for foreground segmentation of yeast cells in the presence of high-noise induced by intentional low illumination, where traditional approaches (e.g., threshold-based methods, specialized cell-segmentation methods) fail. To deal with these harsh conditions, we use a fully-convolutional semantic segmentation network based on the SegNet architecture. Our model is capable of segmenting patches extracted from yeast live-cell experiments with a mIOU score of 0.71 on unseen patches drawn from independent experiments. Further, we show that simultaneous multi-modal observations of bio-fluorescent markers can result in better segmentation performance than the DIC channel alone.

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M3 - Conference contribution

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BT - Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017

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Y2 - 21 July 2017 through 26 July 2017

ER -

Aydin AS, Dubey A, Dovrat D, Aharoni A, Shilkrot R. CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data. In Proceedings - 30th IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2017. Institute of Electrical and Electronics Engineers. 2017. p. 753-759. 8014839. (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops). doi: 10.1109/CVPRW.2017.105

CNN Based Yeast Cell Segmentation in Multi-modal Fluorescent Microscopy Data

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