Evidence map›Paper›PMID 35664261›Full record

ArticleProceedings of SPIE--the International Society for Optical Engineering2020

Obtaining the potential number of object models/atlases needed in medical image analysis.

Ze Jin, Jayaram K Udupa, Drew A Torigian

Abstract read
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In one paragraph

Article in Proceedings of SPIE--the International Society for Optical Engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

3 authors.

Ze JinLaboratory for Future Interdisciplinary Research of Science and Technology, Institute of Innovative Research, Tokyo Institute of Technology, Tokyo, Japan.
Jayaram K UdupaMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.
Drew A TorigianMedical Image Processing Group, Department of Radiology, University of Pennsylvania, Philadelphia, PA, 19104, United States.

Funding

Automated Object Contouring Methods & Software for Radiotherapy PlanningR42CA199735 · NCI · QUANTITATIVE RADIOLOGY SOLUTIONS, LLC · PI OWENS, STEVE, UDUPA, JAYARAM K · 2018 to 2020
$1.9M
NCI NIH HHS R42 CA199735
6 · The paper itself

Abstract

Medical image processing and analysis operations, particularly segmentation, can benefit a great deal from prior information encoded to capture variations over a population in form, shape, anatomic layout, and image appearance of objects. Model/atlas-based methods are extant in medical image segmentation. Although multi-atlas/ multi-model methods have shown improved accuracy for image segmentation, if the atlases/models do not cover representatively the distinct groups, then the methods may not be generalizable to new populations. In a previous study, we have given an answer to address the following problem at image level:

Indexed as

head and neck CThierarchical agglomerative clusteringimage groupingmodel/ atlas-based methods

Identifiers

PMID35664261
PMCPMC9164934

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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.