Automated Training of Deep Convolutional Neural Networks for Cell Segmentation

被引：98

作者：

Sadanandan, Sajith Kecheril ^{[1
,2
]}

Ranefall, Petter ^{[1
,2
]}

Le Guyader, Sylvie ^{[3
]}

Wahlby, Carolina ^{[1
,2
]}

机构：

[1] Uppsala Univ, Dept Informat Technol, Uppsala, Sweden

[2] SciLifeLab, Uppsala, Sweden

[3] Karolinska Inst, Novum, Dept Biosci & Nutr, Ctr Biosci, Huddinge, Sweden

来源：

SCIENTIFIC REPORTS | 2017年 / 7卷

基金：

欧洲研究理事会; 瑞典研究理事会;

关键词：

D O I：

10.1038/s41598-017-07599-6

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

引用

页数：7

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