Deep Learning-Based Nuclei Segmentation of Cleared Brain Tissue

被引：0

作者：

Khorrami, Pooya ^{[1
]}

Brady, Kevin ^{[1
]}

Hernandez, Mark ^{[1
]}

Gjesteby, Lars ^{[1
]}

Burke, Sara N. ^{[2
]}

Lamb, Damon G. ^{[2
]}

Melton, Matthew A. ^{[2
]}

Otto, Kevin J. ^{[2
]}

Brattain, Laura J. ^{[1
]}

机构：

[1] MIT, Lincoln Lab, 244 Wood St, Lexington, MA 02173 USA

[2] Univ Florida, Gainesville, FL 32611 USA

来源：

2019 IEEE HIGH PERFORMANCE EXTREME COMPUTING CONFERENCE (HPEC) | 2019年

关键词：

deep learning; low resource; neuron segmentation; transfer learning;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

We present a deep learning approach for nuclei segmentation at scale. Our algorithm aims to address the challenge of segmentation in dense scenes with limited annotated data available. Annotation in this domain is highly manual in nature, requiring time-consuming markup of the neuron and extensive expertise, and often results in errors. For these reasons, the approach under consideration employs methods adopted from transfer learning. This approach can also be extended to segment other components of the neurons.

引用

页数：2

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