Fast globally optimal segmentation of cells in fluorescence microscopy images

Authors:
Jan-Philip Bergeest;Karl Rohr
Affiliations:
University of Heidelberg, BIOQUANT, IPMB;DKFZ Heidelberg, Dept. Bioinformatics and Functional Genomics, Biomedical Computer Vision Group
Venue:
MICCAI'11 Proceedings of the 14th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Year:
2011

Citing 8
Cited 0

A Variational Framework for Active and Adaptative Segmentation of Vector Valued Images

MOTION '02 Proceedings of the Workshop on Motion and Video Computing
Detection of Spatially Correlated Objects in 3D Images Using Appearance Models and Coupled Active Contours

MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part II
Nuclear segmentation in microscope cell images: a hand-segmented dataset and comparison of algorithms

ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Geometric Applications of the Split Bregman Method: Segmentation and Surface Reconstruction

Journal of Scientific Computing
Cell segmentation using coupled level sets and graph-vertex coloring

MICCAI'06 Proceedings of the 9th international conference on Medical Image Computing and Computer-Assisted Intervention - Volume Part I
Automatic Segmentation of High-Throughput RNAi Fluorescent Cellular Images

IEEE Transactions on Information Technology in Biomedicine
Active contours without edges

IEEE Transactions on Image Processing
Segmenting and tracking fluorescent cells in dynamic 3-D microscopy with coupled active surfaces

IEEE Transactions on Image Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Accurate and efficient segmentation of cells in fluorescence microscopy images is of central importance for the quantification of protein expression in high-throughput screening applications. We propose a new approach for segmenting cell nuclei which is based on active contours and convex energy functionals. Compared to previous work, our approach determines the global solution. Thus, the approach does not suffer from local minima and the segmentation result does not depend on the initialization. We also suggest a numeric approach for efficiently computing the solution. The performance of our approach has been evaluated using fluorescence microscopy images of different cell types. We have also performed a quantitative comparison with previous segmentation approaches.