Conference proceeding
Combining fully convolutional networks and graph-based approach for automated segmentation of cervical cell nuclei
2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp.406-409
04/2017
DOI: 10.1109/ISBI.2017.7950548
Abstract
Cervical nuclei carry substantial diagnostic information for cervical cancer. Therefore, in automation-assisted reading of cervical cytology, automated and accurate segmentation of nuclei is essential. This paper proposes a novel approach for segmentation of cervical nuclei that combines fully convolutional networks (FCN) and graph-based approach (FCNG). FCN is trained to learn the nucleus high-level features to generate a nucleus label mask and a nucleus probabilistic map. The mask is used to construct a graph by image transforming. The map is formulated into the graph cost function in addition to the properties of the nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are also utilized to modify the local cost functions. The globally optimal path in the constructed graph is identified by dynamic programming. Validation of our method was performed on cell nuclei from Herlev Pap smear dataset. Our method shows a Zijdenbos similarity index (ZSI) of 0.92 ± 0.09, compared to the best state-of-the-art approach of 0.89 ± 0.15. The nucleus areas measured by our method correlated strongly with the independent standard (r 2 = 0.91).
Details
- Title: Subtitle
- Combining fully convolutional networks and graph-based approach for automated segmentation of cervical cell nuclei
- Creators
- Ling Zhang - National Institutes of HealthMilan Sonka - University of IowaLe Lu - National Institutes of HealthRonald M Summers - National Institutes of HealthJianhua Yao - National Institutes of Health
- Resource Type
- Conference proceeding
- Publication Details
- 2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017), pp.406-409
- DOI
- 10.1109/ISBI.2017.7950548
- ISSN
- 1945-7928
- eISSN
- 1945-8452
- Publisher
- IEEE
- Language
- English
- Date published
- 04/2017
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Electrical and Computer Engineering; Radiation Oncology; Fraternal Order of Eagles Diabetes Research Center; Injury Prevention Research Center; Ophthalmology and Visual Sciences
- Record Identifier
- 9984186602802771
Metrics
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