Micro-Net: A unified model for segmentation of various objects in microscopy images
Micro-Net: A unified model for segmentation of various objects in microscopy images
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DOI：
10.1016/j.media.2018.12.003
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发表时间：
2019-02-01
影响因子：
10.9
通讯作者：
Rajpoot, Nasir M.
中科院分区：
文献类型：
--
作者：
Raza, Shan E. Ahmed;Cheung, Linda;Rajpoot, Nasir M.
目标分割和结构定位是显微图像自动分析流程中的重要步骤。我们提出了一种基于卷积神经网络（CNN）的深度学习架构，用于显微图像中的对象分割。建议的网络可以用来分割细胞，细胞核和腺体在荧光显微镜和组织学图像后，轻微调整输入参数。该网络以输入图像的多个分辨率进行训练，连接中间层以更好地定位和上下文，并使用多分辨率反卷积滤波器生成输出。绕过最大池化操作的额外卷积层允许网络针对可变输入强度和对象大小进行训练，并使其对噪声数据具有鲁棒性。我们在公开数据集上比较了我们的结果，并表明所提出的网络优于最近的深度学习算法。(C)2018爱思唯尔B.V.保留所有权利。
Object segmentation and structure localization are important steps in automated image analysis pipelines for microscopy images. We present a convolution neural network (CNN) based deep learning architecture for segmentation of objects in microscopy images. The proposed network can be used to segment cells, nuclei and glands in fluorescence microscopy and histology images after slight tuning of input parameters. The network trains at multiple resolutions of the input image, connects the intermediate layers for better localization and context and generates the output using multi-resolution deconvolution filters. The extra convolutional layers which bypass the max-pooling operation allow the network to train for variable input intensities and object size and make it robust to noisy data. We compare our results on publicly available data sets and show that the proposed network outperforms recent deep learning algorithms. (C) 2018 Elsevier B.V. All rights reserved.