Evidence map›Paper›PMID 26887436›Full record

ArticleBMC bioinformatics2016

CellECT: cell evolution capturing tool.

Diana L Delibaltov, Utkarsh Gaur, Jennifer Kim, Matthew Kourakis, Erin Newman-Smith, William Smith, Samuel A Belteton, Daniel B Szymanski, B S Manjunath

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In one paragraph

Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Diana L DelibaltovDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA. diana@ece.ucsb.edu.
Utkarsh GaurDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA.
Jennifer KimDepartment of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.
Matthew KourakisDepartment of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.
Erin Newman-SmithDepartment of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.
William SmithDepartment of Molecular, Cellular, and Developmental Biology, University of California, Santa Barbara, Santa Barbara, CA, USA.
Samuel A BeltetonDepartment of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USA.
Daniel B SzymanskiDepartment of Botany and Plant Pathology, Purdue University, West Lafayette, IN, USA.
B S ManjunathDepartment of Electrical and Computer Engineering, University of California, Santa Barbara, Santa Barbara, CA, USA.

Funding

Morphomic Analysis of a Simple ChordateR01HD059217 · NICHD · UNIVERSITY OF CALIFORNIA SANTA BARBARA · PI MANJUNATH, BANGALORE S, SMITH, WILLIAM · 2009 to 2016
$2.5M
NICHD NIH HHS HD059217NICHD NIH HHS R01 HD059217
6 · The paper itself

Abstract

backgroundRobust methods for the segmentation and analysis of cells in 3D time sequences (3D+t) are critical for quantitative cell biology. While many automated methods for segmentation perform very well, few generalize reliably to diverse datasets. Such automated methods could significantly benefit from at least minimal user guidance. Identification and correction of segmentation errors in time-series data is of prime importance for proper validation of the subsequent analysis. The primary contribution of this work is a novel method for interactive segmentation and analysis of microscopy data, which learns from and guides user interactions to improve overall segmentation.

resultsWe introduce an interactive cell analysis application, called CellECT, for 3D+t microscopy datasets. The core segmentation tool is watershed-based and allows the user to add, remove or modify existing segments by means of manipulating guidance markers. A confidence metric learns from the user interaction and highlights regions of uncertainty in the segmentation for the user's attention. User corrected segmentations are then propagated to neighboring time points. The analysis tool computes local and global statistics for various cell measurements over the time sequence. Detailed results on two large datasets containing membrane and nuclei data are presented: a 3D+t confocal microscopy dataset of the ascidian Phallusia mammillata consisting of 18 time points, and a 3D+t single plane illumination microscopy (SPIM) dataset consisting of 192 time points. Additionally, CellECT was used to segment a large population of jigsaw-puzzle shaped epidermal cells from Arabidopsis thaliana leaves. The cell coordinates obtained using CellECT are compared to those of manually segmented cells.

conclusionsCellECT provides tools for convenient segmentation and analysis of 3D+t membrane datasets by incorporating human interaction into automated algorithms. Users can modify segmentation results through the help of guidance markers, and an adaptive confidence metric highlights problematic regions. Segmentations can be propagated to multiple time points, and once a segmentation is available for a time sequence cells can be analyzed to observe trends. The segmentation and analysis tools presented here generalize well to membrane or cell wall volumetric time series datasets.

Indexed as

AlgorithmsBiological EvolutionAnimalsArabidopsisCell NucleusComputational BiologyHumansImage Interpretation, Computer-AssistedImaging, Three-DimensionalMicroscopyPlant LeavesUrochordata

Identifiers

PMID26887436
PMCPMC4756481

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LicenceCC BY
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.