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Threshold field painting saves the time for segmentation of minute arteries

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International Journal of Computer Assisted Radiology and Surgery Aims and scope Submit manuscript

Abstract

Purpose

It 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.

Methods

Seven 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.

Results

Except 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.

Conclusion

The 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.

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Funding

This research was supported by AMED under Grant Number 17he1602001h0001.

Author information

Authors and Affiliations

  1. Department of Neurosurgery, Graduate School of Medicine, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8655, Japan

    Naoyuki Shono, Taichi Kin & Nobuhito Saito

  2. Department of Computer Science, Graduate School of Information Science and Technology, The University of Tokyo, Tokyo, Japan

    Takeo Igarashi

  3. Department of Medical Information Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan

    Taichi Kin

  4. Department of Clinical Information Engineering, The University of Tokyo Graduate School of Medicine, Tokyo, Japan

    Toki Saito

Authors
  1. Naoyuki Shono
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  2. Takeo Igarashi
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  3. Taichi Kin
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  4. Toki Saito
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  5. Nobuhito Saito
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Corresponding author

Correspondence to Naoyuki Shono.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Informed consent was obtained from all individual participants included in the study.

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Supplementary Information

Below is the link to the electronic supplementary material.

Online Resource 1

Example of Multimodal Three-Dimensional Computer Graphics (TIFF 34866 kb)

Online Resource 2

Volume Paint Scheme “Three Datasets” The signal intensity, threshold value, and state (fixed or unfixed) for each voxel are stored in the system (EPS 7450 kb)

Online Resource 3

Graphical User Interface. The viewing point can be rotated using the right button drag, translated using the middle button drag, and zoomed in/out using the scroll wheel. “A” indicates the position of the buttons used to change the viewing angle to default settings (AP, HF, and LR projection, respectively). The threshold value of the unfixed voxels rendered in gray can be determined with the slider shown in “B.” “C” indicates the position of the buttons used to interact with the threshold field (TIFF 11928 kb)

Online Resource 4

Workflow of the Threshold Field Painting using the “Flood Fill” Function. a: When the threshold is set to the maximum at the initial state, nothing is rendered. b: With the threshold being lowered, the data are depicted. c, d, e: Bodies of the depicted data can be fixed using the “flood fill” function. f, g, h: The same procedure can be repeated for smaller structures. i: Finally, the threshold is set to the maximum to hide all unfixed structures (PNG 728 kb)

Supplementary file5 (PNG 1763 kb)

Supplementary file6 (PNG 772 kb)

Online Resource 5

Workflow of the Threshold Field Painting using the “Brush” Function. a: The major trunks are depicted using the “flood fill” function. Thereafter, elongation of the branches can be started using the “brush” function. b, c, d: By changing the size of the spherical cage and the threshold field inside it manually, the elongation is achieved (PNG 369 kb)

Online Resource 6

Voxel Dimensions in Each Case. The voxel dimensions of the raw data utilized in each case of the user test are shown (PDF 38 kb)

Online Resource 7

Sample Image of Each Case. AP view and lateral view of each case were provided to the examinees prior to the start of each task (PNG 245 kb)

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Shono, N., Igarashi, T., Kin, T. et al. Threshold field painting saves the time for segmentation of minute arteries. Int J CARS 17, 2121–2130 (2022). https://doi.org/10.1007/s11548-022-02682-4

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  • DOI: https://doi.org/10.1007/s11548-022-02682-4

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