Segmentation of confocal microscope images of cell nuclei in thick tissue sections
By C. Ortiz de Solórzano, E. G. Rodriguez, Enrique Garda Rodriguez, A. Jones, D. Pinkel, Joe, W. Gray, Damir Sudar, Stephen J. Lockett. 1999
Subfields: Medical Imaging, Computational Biology & Bioinformatics
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Medical Imaging
In Computational Biology & Bioinformatics
Citations in the corpus
- Hierarchical, model‐based merging of multiple fragments for improved three‐dimensional segmentation of nuclei (2005, methodology): “Solorzano et al. (48) applied a 3D segmentation approach to cancerous specimens.”
- Improved Automatic Detection and Segmentation of Cell Nuclei in Histopathology Images (2010, methodology): “2035102 interest in healthy and pathologic specimens [1], [2], and also for quantifying aspects of normal/diseased tissue architecture [1].”
- On the digital trail of mobile cells (2006, methodology): “A more general solution and extension to [1], [ 26 ], and [27] may be achieved with two types of active surfaces coupled by geometrical set inclusion relationships, one targeting stained nuclei and the other targeting cell membranes stained in a different color, as in Figure 1(d).”
- Robust Nucleus/Cell Detection and Segmentation in Digital Pathology and Microscopy Images: A Comprehensive Review (2016, methodology): “[178] have used a locally adaptive thresholding method to segment nuclear regions in 3-D confocal microscopy images, and another adaptive thresholding-based method [179] is evaluated on wholeslide lung cytology images.”
- A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks (2003, background): “(9) presented a 3D segmentation approach applied to cancerous specimens.”