Abstract :
Epithelium tissue covers and lines all internal organs of the body. Breast cancer carcinomas arise from
the epithelial cells of the breast. The epithelial component lines tubules, ducts, etc. and segmenting out
this region from other tissues is important to detect breast cancer. This paper proposes application of deep
learning technique U-Net, to segment epithelium tissue automatically from a whole slide image (WSI)
image. It also implements U-Net, an image segmentation algorithm to automatically learn the features
of epithlium components from the experts-annotated WSI image dataset and tested the results. Various
image processing techniques such as thresholding are applied to improve the quality of dataset before and
after the training of images by U-Net. The performance of the U-Net is measured by statistic parameters
Sørensen–Dice coefficient and F1 score. The automatic system generated an epithelium segmentation of
accuracy of 0.932.
Context: Automatic, faster and accurate computational technique implementation for breast cancer
diagnostic pathology.
Aims: Automatic identification of carcinoma in WSI of Breast Epithelium Tissue by using deep learning
network, U-Net.
Settings and Design: Epithelium tissue covers and lines all internal organs of the body. Breast cancer
carcinomas arise from the epithelial cells of the breast. The epithelial component lines tubules, ducts, etc.
and segmenting out this region from other tissues is important to detect breast cancer.
Methods and Material: This paper proposes application of deep learning technique U-Net, to segment
epithelium tissue automatically from a whole slide image (WSI) image. It also implements U-Net, an
image segmentation algorithm to automatically learn the features of epithlium components from the
experts-annotated WSI image dataset and tested the results. Various image processing techniques such
as thresholding are applied to improve the quality of dataset before and after the training of images by
U-Net.
Statistical analysis used: The performance of the U-Net is measured by statistic parameters
Sørensen–Dice coefficient and F1 score.
Results: The automatic system achieved an accuracy of 0.932 for the given dataset.
Conclusions: The automatic system achieved an accuracy of 0.932 for 40 images. System should be trained
for more images to realistically achieve more accuracy for higher complex digital pathology images.
Key Messages: [S1] Automatic identification of carcinoma in WSI of Breast Epithelium Tissue by using
deep learning network, U-Net.
Keyword :
Carcinoma, Breast Cancer, Epithelial Tissue, U-Net, Epithelium Segmentation, CNN, Diagnostic Pathology, Digital Pathology, WSI Images