The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net)
Abstract
Bio-medical image segmentation is one of the promising sectors where nuclei segmentation from high-resolution histopathological images enables extraction of very high-quality features for nuclear morphometrics and other analysis metrics in the field of digital pathology. The traditional methods including Otsu thresholding and watershed methods do not work properly in different challenging cases. However, Deep Learning (DL) based approaches are showing tremendous success in different modalities of bio-medical imaging including computation pathology. Recently, the Recurrent Residual U-Net (R2U-Net) has been proposed, which has shown state-of-the-art (SOTA) performance in different modalities (retinal blood vessel, skin cancer, and lung segmentation) in medical image segmentation. However, in this implementation, the R2U-Net is applied to nuclei segmentation for the first time on a publicly available dataset that was collected from the Data Science Bowl Grand Challenge in 2018. The R2U-Net shows around 92.15% segmentation accuracy in terms of the Dice Coefficient (DC) during the testing phase. In addition, the qualitative results show accurate segmentation, which clearly demonstrates the robustness of the R2U-Net model for the nuclei segmentation task.
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AI in Medical Imaging Informatics: Current Challenges and Future Directions
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RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans
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Metadata
- Authors
- Alom, M. Z., Yakopcic, C., Taha, T. M., & Asari, V. K.
- Venue
- NAECON 2018 - IEEE National Aerospace and Electronics Conference
- Publisher
- IEEE
- Published
- 2018-07
- Pages
- 228-233
- OpenAlex
- W2905338897
- Cited by
- 1,020
- References
- 18
- FWCI
- 22.57
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Alom, M. Z., Yakopcic, C., Taha, T. M., & Asari, V. K. (2018). Nuclei Segmentation with Recurrent Residual Convolutional Neural Networks based U-Net (R2U-Net). NAECON 2018 - IEEE National Aerospace and Electronics Conference. https://doi.org/10.1109/naecon.2018.8556686
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