3D cell nuclei segmentation based on gradient flow tracking

Li, Gang; Liu, Tianming; Tarokh, Ashley; Nie, Jingxin; Guo, Lei; Mara, Andrew; Holley, Scott A.; Wong, Stephen T.C.

doi:10.1186/1471-2121-8-40

Cited by 142 publications

(155 citation statements)

References 17 publications

(22 reference statements)

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“…This approach was successful in segmenting the clustered cell nuclei of C. elegans, where the nuclei are very uniform in size, texture, and structure [3]. Not surprisingly, this approach worked well for detecting small and consistent lesions, even if they were densely packed.…”

Section: Microscopic Cell Nuclei: Gradient Vector Diffusionmentioning

confidence: 92%

“…First, wavelet decompositions at different scales identify putative lesions, independent of shape and size [21,22]. Then, the results of this multiresolution analysis are passed to a gradient vector diffusion algorithm for a preliminary estimate of the lesions' boundaries [3]. These two steps work well for detecting and approximating lesion boundaries, but for the final segmentation the Chan-Vese active contour segmentation algorithm is used [20].…”

Section: Synopsis Of Our Approachmentioning

confidence: 99%

“…Chief among these is the ability to identify and segment the phenotype of interest from unaffected structures in the rest of the image. Image segmentation is an important task in many medical and biological applications [3][4][5][6].…”

Section: The Impact Of Phenotypic Complexity On Image Segmentationmentioning

confidence: 99%

“…Macroscopic skin lesions, MRI scans of body regions, and cellular microscopy share several key features with maize lesions, though none of them exhibit the heterogeneity of disease lesion mimic mutants. Common approaches to these segmentation problems include color analysis and feature extraction [4], edge detection, watershed and thresholding, gradient vector flow and diffusion [3], active contour evolution, or some combination of these [5].…”

Section: Individual Performance Of Standard Algorithms On Maize Lesionsmentioning

confidence: 99%

“…Cell nuclei and maize lesions often appear as localized regions of brightened pixels that can be sparsely to densely packed within the region of interest. The common approaches to segmenting cell nuclei include intensity thresholding, feature detection, morphological filtering, watershed transformation, deformable model fitting, and graph-cut approaches [3,19].…”

Section: Microscopic Cell Nuclei: Gradient Vector Diffusionmentioning

confidence: 99%

See 4 more Smart Citations

Extracting complex lesion phenotypes in Zea mays

Kelly

Vatsa

Mayham

et al. 2015

Machine Vision and Applications

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Complex phenotypes are of growing importance in agriculture and medicine. In Zea mays, the most widely produced crop in the world (United States Department of Agriculture. World Agricultural Production. United States Department of Agriculture, Foreign Agricultural Service, Washington, 2015), the disease lesion mimic mutants produce regions of discolored or necrotic tissue in otherwise healthy plants. These mutants are of particular interest due to their apparent action on immune response pathways, providing insight into how plants protect against infectious agents. These phenotypes vary considerably as a function of genotype and environmental conditions, making them a rich, though challenging, phenotypic problem. To segment and quantitate these lesions, we present a novel cascade of adaptive algorithms able to accurately segment the diversity of Z. mays lesions. First, multiresolution analysis of the image allows for salient features to be detected at multiple scales. Next, gradient vector diffusion enhances relevant gradient vectors while suppressing noise. Finally, an active contour algorithm refines the lesion boundary, producing a final segmentation for each lesion. We compare the results from this cascade with manual segmentations from human observers, demonstrating that our algorithm is comparable to humans while having the potential to speed analysis by several orders of magnitude.

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Section: Microscopic Cell Nuclei: Gradient Vector Diffusionmentioning

confidence: 92%

Section: Synopsis Of Our Approachmentioning

confidence: 99%

Section: The Impact Of Phenotypic Complexity On Image Segmentationmentioning

confidence: 99%

Section: Individual Performance Of Standard Algorithms On Maize Lesionsmentioning

confidence: 99%

Section: Microscopic Cell Nuclei: Gradient Vector Diffusionmentioning

confidence: 99%

See 3 more Smart Citations

Extracting complex lesion phenotypes in Zea mays

Kelly

Vatsa

Mayham

et al. 2015

Machine Vision and Applications

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Image‐BasedIn silicoModels of Organogenesis

et al. 2017

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DeadEasy neurons: Automatic counting of HB9 neuronal nuclei in Drosophila

Forero¹,

Pennack²,

Hidalgo³

2010

Cytometry Pt A

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Research into the genetic basis of nervous system development and neurodegenerative diseases requires counting neurons to find out the extent of neurogenesis or neuronal loss. Drosophila is a widely used model organism for in vivo studies. However, counting neurons throughout the nervous system of the intact animal is humanly unfeasible. Automatic methods for cell counting in intact Drosophila are desirable. Here, we show a method called DeadEasy Neurons to count the number of neurons stained with anti-HB9 antibodies in Drosophila embryos. DeadEasy Neurons employs image filtering and mathematical morphology techniques in 2D and 3D, followed by identification of nuclei in 3D based on minimum volume, to count automatically the number of HB9 neurons in vivo. The resultant method has been validated for Drosophila embryos and we show here how it can be used to address biological questions. Counting neurons with DeadEasy is very fast, extremely accurate, and objective, and it enables analyses otherwise humanly unmanageable. DeadEasy Neurons can be modified by the user for other applications, and it will be freely available as an ImageJ plug-in. DeadEasy Neurons will be of interest to the microscopy, image processing, Drosophila, neurobiology, and biomedical communities. '

show abstract

3D cell nuclei segmentation based on gradient flow tracking

Cited by 142 publications

References 17 publications

Extracting complex lesion phenotypes in Zea mays

Extracting complex lesion phenotypes in Zea mays

Image‐BasedIn silicoModels of Organogenesis

DeadEasy neurons: Automatic counting of HB9 neuronal nuclei in Drosophila

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