ICON: An interactive approach to train deep neural networks for segmentation of neuronal structures

F. Gonda; V. Kaynig; Thouis R. Jones; D. Haehn; J. W. Lichtman; T. Parag; H. Pfister

doi:10.1109/ISBI.2017.7950530

ICON: An interactive approach to train deep neural networks for segmentation of neuronal structures

F. Gonda
, V. Kaynig
, Thouis R. Jones
, D. Haehn
, J. W. Lichtman
, T. Parag
, H. Pfister

Computer Science

Harvard University

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

Original language	English
Title of host publication	2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017
Publisher	IEEE Computer Society
Pages	327-331
Number of pages	5
ISBN (Electronic)	9781509011711
DOIs	https://doi.org/10.1109/ISBI.2017.7950530
State	Published - Jun 15 2017
Event	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017 - Melbourne, Australia Duration: Apr 18 2017 → Apr 21 2017

Publication series

Name	Proceedings - International Symposium on Biomedical Imaging
ISSN (Print)	1945-7928
ISSN (Electronic)	1945-8452

Conference

Conference	14th IEEE International Symposium on Biomedical Imaging, ISBI 2017
Country/Territory	Australia
City	Melbourne
Period	4/18/17 → 4/21/17

ASJC Scopus Subject Areas

Biomedical Engineering
Radiology Nuclear Medicine and imaging

Keywords

Annotations
Connectomics
Interactive
Neural Networks
Segmentation

Access to Document

10.1109/ISBI.2017.7950530

Cite this

Gonda, F., Kaynig, V., Jones, T. R., Haehn, D., Lichtman, J. W., Parag, T., & Pfister, H. (2017). ICON: An interactive approach to train deep neural networks for segmentation of neuronal structures. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017 (pp. 327-331). Article 7950530 (Proceedings - International Symposium on Biomedical Imaging). IEEE Computer Society. https://doi.org/10.1109/ISBI.2017.7950530

ICON: An interactive approach to train deep neural networks for segmentation of neuronal structures. / Gonda, F.; Kaynig, V.; Jones, Thouis R. et al.
2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society, 2017. p. 327-331 7950530 (Proceedings - International Symposium on Biomedical Imaging).

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

Gonda, F, Kaynig, V, Jones, TR, Haehn, D, Lichtman, JW, Parag, T & Pfister, H 2017, ICON: An interactive approach to train deep neural networks for segmentation of neuronal structures. in 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017., 7950530, Proceedings - International Symposium on Biomedical Imaging, IEEE Computer Society, pp. 327-331, 14th IEEE International Symposium on Biomedical Imaging, ISBI 2017, Melbourne, Australia, 4/18/17. https://doi.org/10.1109/ISBI.2017.7950530

Gonda F, Kaynig V, Jones TR, Haehn D, Lichtman JW, Parag T et al. ICON: An interactive approach to train deep neural networks for segmentation of neuronal structures. In 2017 IEEE 14th International Symposium on Biomedical Imaging, ISBI 2017. IEEE Computer Society. 2017. p. 327-331. 7950530. (Proceedings - International Symposium on Biomedical Imaging). doi: 10.1109/ISBI.2017.7950530

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ICON: An interactive approach to train deep neural networks for segmentation of neuronal structures

Publication series

Conference

ASJC Scopus Subject Areas

Keywords

Access to Document

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Cite this