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自动化的深度学习卷积神经网络用于细胞分割。

Automated Training of Deep Convolutional Neural Networks for Cell Segmentation.

机构信息

Department of Information Technology, Uppsala University, Sweden and SciLifeLab, Uppsala, Sweden.

Center for Biosciences, Department of Biosciences and Nutrition, Novum, Karolinska Institutet, Huddinge, Sweden.

出版信息

Sci Rep. 2017 Aug 10;7(1):7860. doi: 10.1038/s41598-017-07599-6.

DOI:10.1038/s41598-017-07599-6
PMID:28798336
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5552800/
Abstract

Deep Convolutional Neural Networks (DCNN) have recently emerged as superior for many image segmentation tasks. The DCNN performance is however heavily dependent on the availability of large amounts of problem-specific training samples. Here we show that DCNNs trained on ground truth created automatically using fluorescently labeled cells, perform similar to manual annotations.

摘要

深度卷积神经网络 (DCNN) 最近在许多图像分割任务中表现出色。然而，DCNN 的性能严重依赖于大量特定于问题的训练样本的可用性。在这里，我们展示了使用荧光标记细胞自动创建的真实数据训练的 DCNN 可以与手动注释相媲美。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad70/5552800/e888e6f73135/41598_2017_7599_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad70/5552800/0d73dea39a4a/41598_2017_7599_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad70/5552800/e888e6f73135/41598_2017_7599_Fig2_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad70/5552800/0d73dea39a4a/41598_2017_7599_Fig1_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad70/5552800/e888e6f73135/41598_2017_7599_Fig2_HTML.jpg

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本文引用的文献

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PLoS Comput Biol. 2016 Nov 4;12(11):e1005177. doi: 10.1371/journal.pcbi.1005177. eCollection 2016 Nov.
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Multiplex cytological profiling assay to measure diverse cellular states.用于测量多种细胞状态的多重细胞学分析检测法。
基于对抗特征学习的急性髓系白血病图像中原始粒细胞自动分割
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Different approaches to Imaging Mass Cytometry data analysis.成像质谱流式细胞术数据分析的不同方法。
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