Automatic carcinoma identification from breast epithelial tissue WSI through U-Net deep learning network
Rajasekaran Subramanian, R Devika Rubi, Sai Sandeep Mutyala · IP Journal of diagnostic pathology and oncology/IP journal of diagnostic pathology and oncology/Journal of diagnostic pathology and oncology · 2020
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