dc.contributor.author |
Wiehman, S
|
|
dc.contributor.author |
De Villiers, H
|
|
dc.date.accessioned |
2017-01-17T08:59:52Z |
|
dc.date.available |
2017-01-17T08:59:52Z |
|
dc.date.issued |
2016-07 |
|
dc.identifier.citation |
Wiehman, S and De Villiers, H.2016. Semantic segmentation of bioimages using convolutional neural networks. In:2016 International Joint Conference on Neural Networks (IJCNN), 24 -29 July 2016. |
en_US |
dc.identifier.uri |
http://hdl.handle.net/10204/8914
|
|
dc.description |
2016 International Joint Conference on Neural Networks (IJCNN), 24 -29 July 2016.Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website. |
en_US |
dc.description.abstract |
Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standard convolutional network which produces single pixel outputs, as well as Fully Convolutional Networks (FCN) for patch prediction. It was shown that the custom image processing pipeline, which achieved a worm segmentation accuracy of 94%, was outperformed by all of the architectures considered, with the best being 97.3% achieved by a FCN with a single downsampling layer. These results demonstrate the promise of employing convolutional neural network architectures as an alternative to ad-hoc image processing pipelines on optical microscopy images of C. elegans. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
IEEE |
en_US |
dc.relation.ispartofseries |
Workflow;17613 |
|
dc.subject |
Convolutional neural networks |
en_US |
dc.subject |
Bioimage segmentation |
en_US |
dc.title |
Semantic segmentation of bioimages using convolutional neural networks |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.identifier.apacitation |
Wiehman, S., & De Villiers, H. (2016). Semantic segmentation of bioimages using convolutional neural networks. IEEE. http://hdl.handle.net/10204/8914 |
en_ZA |
dc.identifier.chicagocitation |
Wiehman, S, and H De Villiers. "Semantic segmentation of bioimages using convolutional neural networks." (2016): http://hdl.handle.net/10204/8914 |
en_ZA |
dc.identifier.vancouvercitation |
Wiehman S, De Villiers H, Semantic segmentation of bioimages using convolutional neural networks; IEEE; 2016. http://hdl.handle.net/10204/8914 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Wiehman, S
AU - De Villiers, H
AB - Convolutional neural networks have shown great promise in both general image segmentation problems as well as bioimage segmentation. In this paper, the application of different convolutional network architectures is explored on the C. elegans live/dead assay dataset from the Broad Bioimage Benchmark Collection. These architectures include a standard convolutional network which produces single pixel outputs, as well as Fully Convolutional Networks (FCN) for patch prediction. It was shown that the custom image processing pipeline, which achieved a worm segmentation accuracy of 94%, was outperformed by all of the architectures considered, with the best being 97.3% achieved by a FCN with a single downsampling layer. These results demonstrate the promise of employing convolutional neural network architectures as an alternative to ad-hoc image processing pipelines on optical microscopy images of C. elegans.
DA - 2016-07
DB - ResearchSpace
DP - CSIR
KW - Convolutional neural networks
KW - Bioimage segmentation
LK - https://researchspace.csir.co.za
PY - 2016
T1 - Semantic segmentation of bioimages using convolutional neural networks
TI - Semantic segmentation of bioimages using convolutional neural networks
UR - http://hdl.handle.net/10204/8914
ER -
|
en_ZA |