Evidence map›Paper›PMID 35689722›Full record

ArticleInternational journal of computer assisted radiology and surgery2022

Threshold field painting saves the time for segmentation of minute arteries.

Naoyuki Shono, Takeo Igarashi, Taichi Kin, Toki Saito, Nobuhito Saito

Abstract read
PubMed Publisher
In one paragraph

Article in International journal of computer assisted radiology and surgery, 2022. 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
–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

1 citing paper in PubMed.

  1. Patient-specific cerebral 3D vessel model reconstruction using deep learning.Medical & biological engineering & computing · 2024
    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

5 authors.

Naoyuki ShonoDepartment of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan. nshono-tky@outlook.com.ORCID http://orcid.org/0000-0002-4968-6168
Takeo IgarashiDepartment of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan.
Taichi KinDepartment of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.
Toki SaitoDepartment of Clinical Information Engineering, The University of Tokyo Graduate School of Medicine, Tokyo, Japan.
Nobuhito SaitoDepartment of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan.

Funding

Japan Agency for Medical Research and Development Grant Number 17he1602001h0001
6 · The paper itself

Abstract

purposeIt is often time-consuming to segment fine structures, such as the cerebral arteries from magnetic resonance imaging (MRI). Moreover, extracting anatomically abnormal structures is generally difficult. The segmentation workflow called threshold field painting was tested for its feasibility in morbid minute artery segmentation with special emphasis on time efficiency.

methodsSeven patients with meningioma with ten-sided feeding arteries (n = 10) originating from middle meningeal arteries (MMA) were investigated by three experts of the conventional method for segmentation. The MRI time-of-flight sequence was utilized for the segmentation of each procedure. The tasks were accomplished using both the conventional method and the proposed method in random order. The task completion time and usability score were analyzed using the Wilcoxon signed-rank test.

resultsExcept for one examinee (P = 0.06), the completion time significantly decreased (both P < 0.01) with the use of the proposed method. The average task completion time among the three examinees for the conventional method was 2.8 times longer than that for the proposed method. The usability score was generally in favor of the proposed method.

conclusionThe normally nonexistent minute arteries, such as the MMA feeders, were deemed more efficiently segmented with the proposed method than with the conventional method. While automatic segmentation might be the ultimate solution, our semiautomatic method incorporating expert knowledge is expected to work as the practical solution.

Indexed as

Cerebral ArteriesMagnetic Resonance ImagingHumansImage Processing, Computer-AssistedMeningiomaNeurosurgerySegmentationTumor-feeding artery

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.