title: Graph-regularized 3D shape reconstruction from highly anisotropic and noisy images.
abstract: 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.
authors: Sloan Kettering Institute, 1275 York avenue, New York, NY, USA.
authors: Christian
authors: C
authors: Widmer
authors: Institute for Biochemistry, ETH Zurich, Otto-Stern-Weg 3, Zurich, Switzerland.
authors: Stephanie
authors: S
authors: Heinrich
authors: Sloan Kettering Institute, 1275 York avenue, New York, NY, USA.
authors: Philipp
authors: P
authors: Drewe
authors: Sloan Kettering Institute, 1275 York avenue, New York, NY, USA.
authors: Xinghua
authors: X
authors: Lou
authors: Sloan Kettering Institute, 1275 York avenue, New York, NY, USA.
authors: Shefali
authors: S
authors: Umrania
authors: Sloan Kettering Institute, 1275 York avenue, New York, NY, USA.
authors: Gunnar
authors: G
authors: Rätsch
keywords: 3D fluorescent microscopic data; Cell nuclei detection; Nuclear segmentation; Shape reconstruction