Sciweavers

Share
CVPR
2012
IEEE

Learning to segment dense cell nuclei with shape prior

9 years 2 months ago
Learning to segment dense cell nuclei with shape prior
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.
Xinghua Lou, Ullrich Köthe, Jochen Wittbrodt,
Added 28 Sep 2012
Updated 28 Sep 2012
Type Journal
Year 2012
Where CVPR
Authors Xinghua Lou, Ullrich Köthe, Jochen Wittbrodt, Fred A. Hamprecht
Comments (0)
books