Evidence map›Paper›PMID 23690878›Full record

ArticleComputational and mathematical methods in medicine2013

Automatic vertebral column extraction by whole-body bone SPECT scan.

Sheng-Fang Huang, Hao-Yu Chao, Pan-Fu Kao, Wei-Chih Shen, Yu-Hsiang Chou, Shu-Hsin Liu

Open access · hybridAbstract read
In one paragraph

Article in Computational and mathematical methods in medicine, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
0.2field-weighted citation impact, top 43% of its field
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

1 citing paper in PubMed, 1 citations in OpenAlex.

  1. Article
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

6 authors at 4 institutions in 1 country.

Sheng-Fang HuangDepartment of Medical Informatics, Tzu Chi University, Hualien 97004, Taiwan. spiculate@hotmail.com
Hao-Yu Chao
Pan-Fu Kao
Wei-Chih Shen
Yu-Hsiang Chou
Shu-Hsin Liu
Buddhist Tzu Chi General Hospital · TWTzu Chi University · TWAsia University · TWChung Shan Medical University Hospital · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Bone extraction and division can enhance the accuracy of diagnoses based on whole-body bone SPECT data. This study developed a method for using conventional SPECT for automatic recognition of the vertebral column. A novel feature of the proposed approach is a novel "bone graph" image description method that represents the connectivity between these image regions to facilitate manipulation of morphological relationships in the skeleton before surgery. By tracking the paths shown on the bone graph, skeletal structures can be identified by performing morphological operations. The performance of the method was evaluated quantitatively and qualitatively by two experienced nuclear medicine physicians. Datasets for whole-body bone SPECT scans in 46 lung cancer patients with bone metastasis were obtained with Tc-99m MDP. The algorithm successfully segmented vertebrae in the thoracolumbar spine. The quantitative assessment shows that the segmentation method achieved an average TP, FP, and FN rates of 95.1%, 9.1%, and 4.9%. The qualitative evaluation shows an average acceptance rate of 83%, where the data for the acceptable and unacceptable groups had a Cronbach's alpha value of 0.718, which indicated reasonable internal consistency and reliability.

Indexed as

AlgorithmsBone and BonesBone NeoplasmsComputational BiologyHumansLung NeoplasmsRadiographic Image Interpretation, Computer-AssistedRadiopharmaceuticalsSpineTechnetium Tc 99m MedronateTomography, Emission-Computed, Single-PhotonWhole Body ImagingRadiopharmaceuticalsTechnetium Tc 99m Medronate

Identifiers

PMID23690878
PMCPMC3649305
OpenAlexW2030563031

What OpenQuestion holds

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LicenceCC BY
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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.