A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

Lin, Gang; Adiga, Umesh; Olson, Kathy; Guzowski, John F.; Barnes, Carol A.; Roysam, Badrinath

doi:10.1002/cyto.a.10079

Cited by 300 publications

(314 citation statements)

References 52 publications

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“…A clearer picture of activation within the parahippocampal cortices could be obtained by analyzing many more areas and their various layers. Extensive analyses such as these using catFISH would be greatly aided by automated methods for quantifying labeled neurons that are now under development (Lin et al 2003).…”

Section: Discussionmentioning

confidence: 99%

Mapping neuronal activation and the influence of adrenergic signaling during contextual memory retrieval

Zhang¹,

Guzowski²,

Thomas³

2005

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We recently described a critical role for adrenergic signaling in the hippocampus during contextual and spatial memory retrieval. To determine which neurons are activated by contextual memory retrieval and its sequelae in the presence and absence of adrenergic signaling, transcriptional imaging for the immediate-early gene Arc was used in control and mutant mice lacking norepinephrine and epinephrine. This imaging approach permits the identification of neuronal genomic activation specific to one of two behavioral epochs in the same animal. Analysis revealed several brain regions that were more greatly activated by re-exposure to a salient versus neutral context 1 d after training in control mice (e.g., hippocampal CA3 and CA1, the amygdala, the dorsolateral caudate/putamen, the primary motor cortex, and parts of the rhinal cortices). In mice lacking norepinephrine and epinephrine, many of these regions exhibited significantly reduced activation (e.g., hippocampal CA1), while other regions did not (e.g., hippocampal CA3). In consideration with previous results, the current findings suggest a hypothesis in which adrenergic signaling may be critical for the transfer of retrieved contextual information from CA3 to CA1, where it would be compared to online sensory information coming directly from the cortex.One goal for understanding memory is to define the neurons activated during memory storage and retrieval. Electrophysiologic recordings and brain lesions have been mainstays in this pursuit. More recently, imaging techniques have been used. These include functional brain imaging studies in humans using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI), which are noninvasive and permit repeated measurements; however, they cannot resolve activity at the cellular level (Schacter and Wagner 1999;Mayes and Montaldi 2001). For this, induction of immediate-early gene (IEG) expression ("genomic activation") has been used in animals, offering high sensitivity and cellular resolution that can be analyzed throughout the brain (Clayton 2000). A disadvantage, however, is that each animal is assessed under a single condition.A technique that overcomes this disadvantage uses fluorescence in situ hybridization (FISH) to the IEG Arc. Arc (Lyford et al. 1995), also termed Arg3.1 (Link et al. 1995), is an activityregulated, cytoskeleton-associated protein implicated in synaptic plasticity and memory (Steward et al. 1998;Guzowski et al. 2000). Basal expression of Arc is very low in most neurons. However, transcription is rapidly induced by neuronal activation, and FISH to Arc reveals two small, intense intranuclear foci within 2 min of activation (Guzowski et al. 1999). By ∼20 min the nuclear signal has disappeared and perinuclear cytoplasmic labeling is transiently observed before disappearing due to dendritic transport of Arc mRNA (Steward et al. 1998). These properties allow one to determine the recent activation history of a neuron at two distinct times, referred to as cellular compartment analys...

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Section: Discussionmentioning

confidence: 99%

Mapping neuronal activation and the influence of adrenergic signaling during contextual memory retrieval

Zhang¹,

Guzowski²,

Thomas³

2005

Learn. Mem.

Self Cite

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“…Segmentation methods based on watersheds and active contours form the core of automatic cell segmentation procedures. Several methods based on morphological filters and watershed algorithm were proposed to detect cells or cell nuclei in fluorescence images [28][29][30][31][32]. The active contour models [33][34][35][36][37][38][39][40][41][42][43] became popular for automatic cell/nuclei segmentation at the beginning of this century when the classical CV model [33] was proposed.…”

Section: Introductionmentioning

confidence: 99%

Quantitative comparison of 3D third harmonic generation and fluorescence microscopy images

Zhang

Kuzmin

Groot

et al. 2017

Journal of Biophotonics

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Third harmonic generation (THG) microscopy is a label-free imaging technique that shows great potential for rapid pathology of brain tissue during brain tumor surgery. However, the interpretation of THG brain images should be quantitatively linked to images of more standard imaging techniques, which so far has been done qualitatively only. We establish here such a quantitative link between THG images of mouse brain tissue and all-nuclei-highlighted fluorescence images, acquired simultaneously from the same tissue area. For quantitative comparison of a substantial pair of images, we present here a segmentation workflow that is applicable for both THG and fluorescence images, with a precision of 91.3 % and 95.8 % achieved respectively. We find that the correspondence between the main features of the two imaging modalities amounts to 88.9 %, providing quantitative evidence of the interpretation of dark holes as brain cells. Moreover, 80 % bright objects in THG images overlap with nuclei highlighted in the fluorescence images, and they are 2 times smaller than the dark holes, showing that cells of different morphologies can be recognized in THG images. We expect that the described quantitative comparison is applicable to other types of brain tissue and with more specific staining experiments for cell type identification.

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“…Those methods can be broadly classified as edge-based [15], region-based [16], threshold-based [17], and watershed-based [18,19] segmentation schemes. Wu et al [15] stated that cell boundaries are not sharp enough to perform edge-based segmentation in leukocyte images.…”

Section: Literature Surveymentioning

confidence: 99%

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

Mohapatra

Patra

Kumar

2012

ISRN Artificial Intelligence

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The segmentation of leukocytes and their components acts as the foundation for all automated image-based hematological disease recognition systems. Perfection in image segmentation is a necessary condition for improving the diagnostic accuracy in automated cytology. Since the diagnostic information content of the segmented images is plentiful, suitable segmentation routines need to be developed for better disease recognition. Clustering is an essential image segmentation procedure which segments an image into desired regions. A judicious integration of rough sets and fuzzy sets is suitably employed towards leukocyte segmentation in a clustering framework. In this study, the goodness of fuzzy sets and rough sets is suitably integrated to achieve improved segmentation performance. The membership concept of fuzzy sets endow is efficient handling of overlapping partitions, and the rough sets provide a reasonable solution to deal with uncertainty, vagueness, and incompleteness in data. Such synergistic combination gives the proposed scheme an edge over standard cluster-based segmentation techniques, that is, K-means, K-medoid, fuzzy c-means, and rough c-means. Comparative analysis reveals that the hybrid rough fuzzy c-means algorithm is robust in segmenting stained blood microscopic images. The accomplished segmented nucleus and cytoplasm of a leukocyte can be used for feature extraction which leads to automated leukemia detection.

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A hybrid 3D watershed algorithm incorporating gradient cues and object models for automatic segmentation of nuclei in confocal image stacks

Cited by 300 publications

References 52 publications

Mapping neuronal activation and the influence of adrenergic signaling during contextual memory retrieval

Mapping neuronal activation and the influence of adrenergic signaling during contextual memory retrieval

Quantitative comparison of 3D third harmonic generation and fluorescence microscopy images

Unsupervised Leukocyte Image Segmentation Using Rough Fuzzy Clustering

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