Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images

被引：1

作者：

Widmer, Christian ^{[1
]}

Heinrich, Stephanie ^{[2
]}

Drewe, Philipp ^{[1
]}

Lou, Xinghua ^{[1
]}

Umrania, Shefali ^{[1
]}

Raetsch, Gunnar ^{[1
]}

机构：

[1] Sloan Kettering Inst, New York, NY 10065 USA

[2] ETH, Inst Biochem, Zurich, Switzerland

来源：

SIGNAL IMAGE AND VIDEO PROCESSING | 2014年 / 8卷

关键词：

Cell nuclei detection; Shape reconstruction; 3D fluorescent microscopic data; Nuclear segmentation; CELL-NUCLEI; SEGMENTATION; MODEL;

D O I：

10.1007/s11760-014-0694-8

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Analysis of microscopy images can provide insight into many biological processes. One particularly challenging problem is cellular nuclear segmentation in highly anisotropic and noisy 3D image data. Manually localizing and segmenting each and every cellular nucleus is very time-consuming, which remains a bottleneck in large-scale biological experiments. In this work, we present a tool for automated segmentation of cellular nuclei from 3D fluorescent microscopic data. Our tool is based on state-of-the-art image processing and machine learning techniques and provides a user-friendly graphical user interface. We show that our tool is as accurate as manual annotation and greatly reduces the time for the registration.

引用

页码：S41 / S48

页数：8

共 32 条

[1] Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images [J].