A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks
By Gang Lin, U. Adiga, K. Olson, John Guzowski, C. A. Barnes, B. Roysam. Cytometry Part A, 2003
343 citations recorded by the source corpus.
Subfields: Biophysics, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/b1609ceb28b2c73ce92edd5fe19751ab62bda82f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biophysics
- Fuyong Xing
- Lin F. Yang
- Xiaobo Zhou
- Yuanpu Xie
- B. Roysam
- Jens Rittscher
- Dirk Ryan Padfield
- Erik H. W. Meijering
In Computer Vision and Pattern Recognition
- Lin F. Yang
- Fuyong Xing
- Yuanpu Xie
- Xiaobo Zhou
- Yousef Al-Kofahi
- B. Roysam
- Jens Rittscher
- Dirk Ryan Padfield
Citations in the corpus
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “Therefore, a statistical object model is introduced in [33] to merge undesired regions.”
- Coupled minimum-cost flow cell tracking for high-throughput quantitative analysis (2011, positive): “Usually, some merging and splitting is performed to refine the segmentations [56, 51, 57, 52, 58, 49].”
- Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images (2010, methodology): “its many variants and improvements [2], [3], [5], [9]–[11], [16], [44], [45].”
- Bias‐free Measurement of Giant Molecular Cloud Properties (2006, methodology): “Such brightness transforms are frequently used in the decomposition algorithms used in other fields such as medical imaging (e.g. Lin et al. 2003).”
- Bioimage informatics: a new area of engineering biology (2008, neutral): “…watershed segmentation, Gaussian mixture model estimation and active contour/snake methods, which can be further improved by considering different shape or intensity cues of the objects (Cong and Parvin, 1999; Han et al., 2007; Lin et al., 2003, 2005; Long et al., 2007c; Parvin et al., 2002).”
- Automated segmentation, classification, and tracking of cancer cell nuclei in time-lapse microscopy (2006, methodology): “A nuclei-fragment merging method based on the shapes and sizes of nuclei is used to deal with the problem of over-segmentation [1], [8], [11].”
- An Automatic Learning-Based Framework for Robust Nucleus Segmentation (2016, methodology): “[3] have proposed a gradient weighted-distance transform method to locate nucleus centroids, which are viewed as markers for watershed segmentation of nuclei in 3D fluorescence microscopy images.”