Renal cell cancer nuclei segmentation from histopathology image using synthetic data
Abstract
Renal cell cancer nuclei segmentation from histopathology image is an important step in cancer diagnosis and treatment. However, an automatic cell nuclei segmentation in medical application is a difficult task. Particularly a large amount of annotated data is required for deep learning network. In this work, synthetic but annotate renal cell nuclei data are generated based on non-synthetic data reference. Furthermore, background texture is created according to reference image. The Random shapes of nuclei polygon are created where polygon regions are filled up with foreground nuclei color. The synthetic cell nuclei patches are refined with speeded-up robust feature selection algorithm. The non-synthetic reference patches are considered to assign a score for each synthetic patch where synthetic patches with highest scores are collected. The synthetic patches with corresponding masks are utilized to train U-net segmentation network, which provides better performance than existing methods. In this approach, manual data annotation does not require, which is time-consuming and expensive. The renal cell cancer histopathology data has been collected from Cancer Image Archive, USA which is used to produce non-synthetic reference patches. The proposed framework is validated with Kidney Renal Clear Cell Carcinoma (KIRC) datasets. We have achieved average 0.923 cell nuclei segmentation accuracy from histopathology patches where accuracy provides better segmentation result than other existing methods.
RAS ID
31518
Document Type
Conference Proceeding
Date of Publication
2020
School
School of Science
Copyright
subscription content
Publisher
IEEE
Comments
Hossain, M. S., & Sakib, N. (2020). Renal Cell Cancer Nuclei Segmentation from Histopathology Image Using Synthetic Data. In 2020 16th IEEE International Colloquium on Signal Processing & Its Applications (CSPA), Langkawi, Malaysia, 2020, pp. 236-241.
https://doi.org/10.1109/CSPA48992.2020.9068701