结合颜色检测和神经网络进行腺体检测

Jie Shu, Jiang Lei, Q. Gao, Qian Zhang
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引用次数: 0

摘要

腺体是可用于组织学图像定量分析的感兴趣的对象。基于神经网络对H&E染色组织图像进行腺体检测,可能存在染色变异问题。本文提出了一种将统计颜色检测模型与神经网络相结合的方法来解决这一问题。在预处理步骤中预先检测和增强腺体边界显示的颜色。然后从这些颜色像素中学习基于Faster R-CNN的神经网络模型来检测腺体。该方法已在MICCAI 2015举办的结肠组织学图像挑战赛(GlaS)上进行了测试。实验结果表明,该方法优于未经颜色检测预处理的Faster R-CNN或U-net。此外,该方法在良性腺体检测中可达到f1 -评分等级8,在恶性腺体检测中可达到f1 -评分等级5。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Combing colour detection and neural networks for gland detection
Glands are objects of interest which can be used for quantitatively analysis of histology images. Detecting glands from H&E staining histological images based on neural networks, may suffer stain variation problem. In this paper, we present a new method which combines a statistical colour detection model and a neural network to cover this problem. Colours shown at glands boundaries are pre-detected and enhanced in a pre-processing step. Then a neural network model based on Faster R-CNN is learned from these colour pixels to detect glands. This method has been tested on a Colon Histology Images Challenge Contest (GlaS) held at MICCAI 2015. The experimental results have shown the proposed method is superior to either Faster R-CNN or U-net without colour detection pre-processing. In addition, this proposed method can achieve F1-score rank 8 in detecting benign glands and rank 5 in detecting malignant glands.
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