Breast Cancer Cell Segmentation
Elisa Drelie Gelasca and Jiyun Byun and Boguslaw Obara and B.S. Manjunath

BreastCancerCell_dataset (174 files)
ytma10_010704_benign1.TIF 689.06kB
ytma10_010704_benign1.TIF.xml 0.37kB
ytma10_010704_benign1_ccd.tif 2.08MB
ytma10_010704_benign2.TIF 689.06kB
ytma10_010704_benign2.TIF.xml 0.37kB
ytma10_010704_benign2_ccd.tif 2.08MB
ytma10_010704_benign3.TIF 689.06kB
ytma10_010704_benign3.TIF.xml 0.37kB
ytma10_010704_benign3_ccd.tif 2.08MB
ytma10_010704_malignant1.TIF 689.06kB
ytma10_010704_malignant1.TIF.xml 0.38kB
ytma10_010704_malignant1_ccd.tif 2.08MB
ytma10_010704_malignant2.TIF 689.06kB
ytma10_010704_malignant2.TIF.xml 0.38kB
ytma10_010704_malignant2_ccd.tif 2.08MB
ytma10_010704_malignant3.TIF 689.06kB
ytma10_010704_malignant3.TIF.xml 0.38kB
ytma10_010704_malignant3_ccd.tif 2.08MB
ytma12_010804_benign1.TIF 689.06kB
ytma12_010804_benign1.TIF.xml 0.37kB
ytma12_010804_benign1_ccd.tif 2.07MB
ytma12_010804_benign2.TIF 689.06kB
ytma12_010804_benign2.TIF.xml 0.37kB
ytma12_010804_benign2_ccd.tif 2.07MB
ytma12_010804_benign3.TIF 689.06kB
ytma12_010804_benign3.TIF.xml 0.37kB
ytma12_010804_benign3_ccd.tif 2.07MB
ytma12_010804_malignant1.TIF 689.06kB
ytma12_010804_malignant1.TIF.xml 0.38kB
ytma12_010804_malignant1_ccd.tif 2.07MB
ytma12_010804_malignant2.TIF 689.06kB
ytma12_010804_malignant2.TIF.xml 0.38kB
ytma12_010804_malignant2_ccd.tif 2.06MB
ytma12_010804_malignant3.TIF 689.06kB
ytma12_010804_malignant3.TIF.xml 0.38kB
ytma12_010804_malignant3_ccd.tif 2.05MB
ytma23_022103_benign1.TIF 689.06kB
ytma23_022103_benign1.TIF.xml 0.37kB
ytma23_022103_benign1_ccd.tif 2.07MB
ytma23_022103_benign2.TIF 689.06kB
ytma23_022103_benign2.TIF.xml 0.37kB
ytma23_022103_benign2_ccd.tif 2.07MB
ytma23_022103_benign3.TIF 689.06kB
ytma23_022103_benign3.TIF.xml 0.37kB
ytma23_022103_benign3_ccd.tif 2.02MB
ytma23_022103_malignant1.TIF 689.06kB
ytma23_022103_malignant1.TIF.xml 0.38kB
ytma23_022103_malignant1_ccd.tif 2.08MB
ytma23_022103_malignant2.TIF 689.06kB
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Type: Dataset
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Bibtex:
@article{,
title= {Breast Cancer Cell Segmentation},
keywords= {},
author= {Elisa Drelie Gelasca and Jiyun Byun and Boguslaw Obara and B.S. Manjunath},
abstract= {There are about 58 H&E stained histopathology images used in breast cancer cell detection with associated ground truth data available. Routine histology uses the stain combination of hematoxylin and eosin, commonly referred to as H&E. These images are stained since most cells are essentially transparent, with little or no intrinsic pigment. Certain special stains, which bind selectively to particular components, are be used to identify biological structures such as cells. In those images, the challenging problem is cell segmentation for subsequent classification in benign and malignant cells. The ground truth have been obtained for one image containing benign cells.


| Image: |Ground Truth: |
|---|---|
| ![](https://i.imgur.com/haa5X8O.png) | ![](https://i.imgur.com/gqBikTa.png) |






All images:

![](https://i.imgur.com/QM22bG2.png)},
terms= {},
license= {},
superseded= {},
url= {http://bioimage.ucsb.edu/research/bio-segmentation}
}

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