Evidence map›Paper›PMID 29297208›Full record

ArticleJournal of biomedical optics2018

Method for accurate registration of tissue autofluorescence imaging data with corresponding histology: a means for enhanced tumor margin assessment.

Jakob Unger, Tianchen Sun, Yi-Ling Chen, Jennifer E Phipps, Richard J Bold, Morgan A Darrow, Kwan-Liu Ma, Laura Marcu

Abstract read
In one paragraph

Article in Journal of biomedical optics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.

0numbers the graph read from it
0cells of the map it votes in
12citing 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

12 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

8 authors.

Jakob UngerUniversity of California Davis, Department of Biomedical Engineering, Davis, California, United States.
Tianchen SunUniversity of California Davis, Department of Computer Science, Davis, California, United States.
Yi-Ling ChenUniversity of California Davis, Department of Computer Science, Davis, California, United States.
Jennifer E PhippsUniversity of California Davis, Department of Biomedical Engineering, Davis, California, United States.
Richard J BoldUniversity of California Davis, Department of Surgery, Sacramento, California, United States.
Morgan A DarrowUniversity of California Davis, Department of Pathology and Laboratory Medicine, Sacramento, Califor, United States.
Kwan-Liu MaUniversity of California Davis, Department of Computer Science, Davis, California, United States.
Laura MarcuUniversity of California Davis, Department of Biomedical Engineering, Davis, California, United States.

Funding

Staff InvestigatorsP30CA093373 · NCI · UNIVERSITY OF CALIFORNIA DAVIS · PI Laura Marcu · 2002 to 2026
$84.9M
Fluorescence Lifetime Imaging/Spectroscopy System For Robotic Cancer Surgery GuidanceR01CA187427 · NCI · UNIVERSITY OF CALIFORNIA AT DAVIS · PI BIRKELAND, ANDREW CHARLES, MARCU, LAURA · 2014 to 2025
$4.6M
NCI NIH HHS P30 CA093373NCI NIH HHS R01 CA187427
6 · The paper itself

Abstract

An important step in establishing the diagnostic potential for emerging optical imaging techniques is accurate registration between imaging data and the corresponding tissue histopathology typically used as gold standard in clinical diagnostics. We present a method to precisely register data acquired with a point-scanning spectroscopic imaging technique from fresh surgical tissue specimen blocks with corresponding histological sections. Using a visible aiming beam to augment point-scanning multispectral time-resolved fluorescence spectroscopy on video images, we evaluate two different markers for the registration with histology: fiducial markers using a 405-nm CW laser and the tissue block's outer shape characteristics. We compare the registration performance with benchmark methods using either the fiducial markers or the outer shape characteristics alone to a hybrid method using both feature types. The hybrid method was found to perform best reaching an average error of 0.78±0.67  mm. This method provides a profound framework to validate diagnostical abilities of optical fiber-based techniques and furthermore enables the application of supervised machine learning techniques to automate tissue characterization.

Indexed as

AlgorithmsBreast NeoplasmsFemaleFiducial MarkersHumansImage Processing, Computer-AssistedOptical ImagingVideo Recordingfiducialsfluorescencehistologypoint-scanningregistrationshape matching

Identifiers

PMID29297208
PMCPMC5749583

What OpenQuestion holds

Textmetadata
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Registered trials

None linked

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.