Nuclear Segmentation of Clear Cell Renal Cell Carcinoma based on Deep Convolutional Neural Networks
LU Haoda
XU Jun
LIU Lihui
ZHOU Chao
ZHOU Xiaojun
ZHANG Zelin
Abstract：The shape feature and location information of clear cell renal cell carcinoma ' s nucleus is important for the diagnosis of benign and malignant renal cell carcinoma .To improve nuclear segmentation accuracy , nuclear segmentation based on deep convolution neural networks was proposed .First, nuclear sample dataset was formed according to the nuclear contour labeled by pathologist .Then, deep convolution neural network extracted implicit nuclear feature instead of artificial nuclear characteristic and the nuclear segmenta -tion model were trained .Finally, the nuclear segmentation model did nuclear segmentation by pixel -wise.The experimental results show that the clear cell renal cell carcinoma ' s nucleus segmentation algorithm of deep convolution neural network is as high as 90.33%in the nucleus pixel accuracy and the nucleus segmentation performance is stable .The strong robustness and adaptability of deep convo-lution neural network makes nuclear auto -segmentation possible .
Keywords：SegmentationConvolution neural networkNucleiClear cell renal cell carcinomaPixel-wise
Publication Date：2017-01-01
Online Publishing Date：2026-09-11(First online date of this platform, not the publication date of the document)
Pages：6( 340-345 )