Paper
14 February 2012 Reconstruction of incomplete cell paths through a 3D-2D level set segmentation
Maia Hariri, Justin W. L. Wan
Author Affiliations +
Abstract
Segmentation of fluorescent cell images has been a popular technique for tracking live cells. One challenge of segmenting cells from fluorescence microscopy is that cells in fluorescent images frequently disappear. When the images are stacked together to form a 3D image volume, the disappearance of the cells leads to broken cell paths. In this paper, we present a segmentation method that can reconstruct incomplete cell paths. The key idea of this model is to perform 2D segmentation in a 3D framework. The 2D segmentation captures the cells that appear in the image slices while the 3D segmentation connects the broken cell paths. The formulation is similar to the Chan-Vese level set segmentation which detects edges by comparing the intensity value at each voxel with the mean intensity values inside and outside of the level set surface. Our model, however, performs the comparison on each 2D slice with the means calculated by the 2D projected contour. The resulting effect is to segment the cells on each image slice. Unlike segmentation on each image frame individually, these 2D contours together form the 3D level set function. By enforcing minimum mean curvature on the level set surface, our segmentation model is able to extend the cell contours right before (and after) the cell disappears (and reappears) into the gaps, eventually connecting the broken paths. We will present segmentation results of C2C12 cells in fluorescent images to illustrate the effectiveness of our model qualitatively and quantitatively by different numerical examples.
© (2012) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Maia Hariri and Justin W. L. Wan "Reconstruction of incomplete cell paths through a 3D-2D level set segmentation", Proc. SPIE 8314, Medical Imaging 2012: Image Processing, 83141O (14 February 2012); https://doi.org/10.1117/12.911160
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CITATIONS
Cited by 2 scholarly publications.
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KEYWORDS
Image segmentation

3D modeling

3D image processing

Microscopy

Edge detection

Image processing algorithms and systems

Luminescence

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