We are analyzing https://link.springer.com/article/10.1007/s11517-019-02008-8.

Title:
A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images | Medical & Biological Engineering & Computing
Description:
This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color-normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast, and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the-art methods. Moreover, it is efficient that one 1000×1000 image can be segmented in less than 5 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation .
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Keywords {🔍}

segmentation, article, nuclei, ieee, images, google, scholar, pubmed, histopathology, breast, image, cancer, automatic, neural, deep, learning, international, liu, method, networks, trans, conference, imaging, analysis, data, information, medical, network, convolutional, detection, yang, stained, springer, privacy, cookies, content, cui, zhang, access, computer, publish, research, search, recognition, nuclear, biomed, eng, color, nature, biomedical,

Topics {✒️}

parameter-free post-processing procedure month download article/chapter /easycui/nuclei_segmentation/blob/master/license data augmentation methods high-resolution histopathology images automatic learning-based framework fully convolutional networks generalized nuclear segmentation structure-preserved color normalization image-specific color deconvolution medical image computing medical information engineering computer-based image analysis learning deconvolution network computer-aided image analysis deep neural networks robust nucleus segmentation stained histopathology images deep learning algorithm nucleus-boundary model full article pdf deep residual learning normalizing neural networks privacy choices/manage cookies cell images based automated segmentation neural network digital histopathology images pattern recognition health-related quality slide histopathology images automatic nuclei segmentation breast reconstruction timing normalizing histology slides nuclei segmentation approach adjusting nuclei segmentation automatic image segmentation references al-kofahi color-normalized image unsupervised color decomposition model directly outputs visual document analysis related subjects personal data estimated nuclei map european economic area overlapped patch extraction k-means clustering nonlinear mapping approach negative matrix factorization

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         description:This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color-normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast, and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the-art methods. Moreover, it is efficient that one 1000×1000 image can be segmented in less than 5 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation .
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            Computer Applications
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      headline:A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images
      description:This paper addresses the task of nuclei segmentation in high-resolution histopathology images. We propose an automatic end-to-end deep neural network algorithm for segmentation of individual nuclei. A nucleus-boundary model is introduced to predict nuclei and their boundaries simultaneously using a fully convolutional neural network. Given a color-normalized image, the model directly outputs an estimated nuclei map and a boundary map. A simple, fast, and parameter-free post-processing procedure is performed on the estimated nuclei map to produce the final segmented nuclei. An overlapped patch extraction and assembling method is also designed for seamless prediction of nuclei in large whole-slide images. We also show the effectiveness of data augmentation methods for nuclei segmentation task. Our experiments showed our method outperforms prior state-of-the-art methods. Moreover, it is efficient that one 1000×1000 image can be segmented in less than 5 s. This makes it possible to precisely segment the whole-slide image in acceptable time. The source code is available at https://github.com/easycui/nuclei_segmentation .
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