eprintid: 10192739
rev_number: 9
eprint_status: archive
userid: 699
dir: disk0/10/19/27/39
datestamp: 2024-05-24 13:06:10
lastmod: 2024-05-24 13:06:59
status_changed: 2024-05-24 13:06:10
type: article
metadata_visibility: show
sword_depositor: 699
creators_name: Didziokas, Marius
creators_name: Pauws, Erwin
creators_name: Kölby, Lars
creators_name: Khonsari, Roman H
creators_name: Moazen, Mehran
title: BounTI (boundary‐preserving threshold iteration): A user‐friendly tool for automatic hard tissue segmentation
ispublished: inpress
divisions: UCL
divisions: B04
divisions: C05
divisions: F45
keywords: 3D reconstruction, bone, computed tomography, craniofacial system, craniosynostosis, image processing, skull
note: © 2024 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society. This is an open access article under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/).
abstract: AX‐ray Computed Tomography (CT) images are widely used in various fields of natural, physical, and biological sciences. 3D reconstruction of the images involves segmentation of the structures of interest. Manual segmentation has been widely used in the field of biological sciences for complex structures composed of several sub‐parts and can be a time‐consuming process. Many tools have been developed to automate the segmentation process, all with various limitations and advantages, however, multipart segmentation remains a largely manual process. The aim of this study was to develop an open‐access and user‐friendly tool for the automatic segmentation of calcified tissues, specifically focusing on craniofacial bones. Here we describe BounTI, a novel segmentation algorithm which preserves boundaries between separate segments through iterative thresholding. This study outlines the working principles behind this algorithm, investigates the effect of several input parameters on its outcome, and then tests its versatility on CT images of the craniofacial system from different species (e.g. a snake, a lizard, an amphibian, a mouse and a human skull) with various scan qualities. The case studies demonstrate that this algorithm can be effectively used to segment the craniofacial system of a range of species automatically. High‐resolution microCT images resulted in more accurate boundary‐preserved segmentation, nonetheless significantly lower‐quality clinical images could still be segmented using the proposed algorithm. Methods for manual intervention are included in this tool when the scan quality is insufficient to achieve the desired segmentation results. While the focus here was on the craniofacial system, BounTI can be used to automatically segment any hard tissue. The tool presented here is available as an Avizo/Amira add‐on, a stand‐alone Windows executable, and a Python library. We believe this accessible and user‐friendly segmentation tool can benefit the wider anatomical community.
date: 2024-05-17
date_type: published
publisher: Wiley
official_url: http://dx.doi.org/10.1111/joa.14063
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
verified: verified_manual
elements_id: 2277173
doi: 10.1111/joa.14063
lyricists_name: Didziokas, Marius
lyricists_name: Moazen, Mehran
lyricists_id: MDIDZ43
lyricists_id: MMOAZ42
actors_name: Moazen, Mehran
actors_id: MMOAZ42
actors_role: owner
funding_acknowledgements: EP/W008092/1 [Engineering and Physical Sciences Research Council]; EP/R513143/1 – 2592407 [Engineering and Physical Sciences Research Council]; EP/T517793/1 - 2592407 [Engineering and Physical Sciences Research Council]
full_text_status: public
publication: Journal of Anatomy
citation:        Didziokas, Marius;    Pauws, Erwin;    Kölby, Lars;    Khonsari, Roman H;    Moazen, Mehran;      (2024)    BounTI (boundary‐preserving threshold iteration): A user‐friendly tool for automatic hard tissue segmentation.                   Journal of Anatomy        10.1111/joa.14063 <https://doi.org/10.1111/joa.14063>.    (In press).    Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10192739/7/Moazen_BounTI%20boundary%E2%80%90preserving%20threshold%20iteration_AOP.pdf