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Learning to segment dense cell nuclei with shape prior

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4 Author(s)
Xinghua Lou ; HCI, IWR, University of Heidelberg, 69115, Germany ; Ullrich Koethe ; Jochen Wittbrodt ; Fred A. Hamprecht

We study the problem of segmenting multiple cell nuclei from GFP or Hoechst stained microscope images with a shape prior. This problem is encountered ubiquitously in cell biology and developmental biology. Our work is motivated by the observation that segmentations with loose boundary or shrinking bias not only jeopardize feature extraction for downstream tasks (e.g. cell tracking), but also prevent robust statistical analysis (e.g. modeling of fluorescence distribution). We therefore propose a novel extension to the graph cut framework that incorporates a “blob”-like shape prior. The corresponding energy terms are parameterized via structured learning. Extensive evaluation and comparison on 2D/3D datasets show substantial quantitative improvement over other state-of-the-art methods. For example, our method achieves an 8.2% Rand index increase and a 4.3 Hausdorff distance decrease over the second best method on a public hand-labeled 2D benchmark.

Published in:

Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on

Date of Conference:

16-21 June 2012