Atlas Construction and Validation Using Cone-Beam Computed Tomography Images of Healthy Adult Human Hands

Anatomical atlases provide standardized spatial references for comparing complex structures. This study develops a high-resolution anatomical atlas of healthy hand bones using cone-beam computed tomography (CBCT), which provides enhanced spatial resolution and lower radiation dose compared to conventional CT. The atlas, constructed from CBCT bilateral scans of 18 healthy adult subjects (35 total hands), serves as a standardized spatial reference for bones in the carpal, metacarpal, and proximal phalangeal regions, where variations in individual scans can complicate analysis. An iterative registration and averaging process, coupled with image preprocessing, enabled the construction of the atlas. The atlas was validated using shape analysis of each bone in the atlas compared to the input images. Validation showed a mean z-score of 0.003 across metrics (volume, surface area, axis lengths, elongation, and flatness), confirming anatomical accuracy. Atlas-based segmentation accuracy, assessed on three independent images, yielded a Dice coefficient of 0.94 and Hausdorff distance of 1.73 mm. Additionally, a subtraction-based method applied to rheumatoid arthritis (RA)-affected joints achieved a mean erosion volume difference of 4.9% compared to manual annotations. This CBCT-based atlas is a valuable reference for segmentation and analysis in bone-related disease research, particularly for tracking RA-induced changes in bone morphology.

Authors

Institutions

Publication Details

Published
2025-04-14
DOI
https://doi.org/10.1109/isbi60581.2025.10980964
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Atlas Construction and Validation Using Cone-Beam Computed Tomography Images of Healthy Adult Human Hands

M. Ethan MacDonald, Abdoljalil Addeh, Sarah L. Manske, Biniyam Mezgebo et al.
Radiomics and Machine Learning in Medical Imaging
article

Atlas Construction and Validation Using Cone-Beam Computed Tomography Images of Healthy Adult Human Hands

M. Ethan MacDonald, Abdoljalil Addeh, Sarah L. Manske, Biniyam Mezgebo, Yousif Al-Khoury
article en

Abstract

Anatomical atlases provide standardized spatial references for comparing complex structures. This study develops a high-resolution anatomical atlas of healthy hand bones using cone-beam computed tomography (CBCT), which provides enhanced spatial resolution and lower radiation dose compared to conventional CT. The atlas, constructed from CBCT bilateral scans of 18 healthy adult subjects (35 total hands), serves as a standardized spatial reference for bones in the carpal, metacarpal, and proximal phalangeal regions, where variations in individual scans can complicate analysis. An iterative registration and averaging process, coupled with image preprocessing, enabled the construction of the atlas. The atlas was validated using shape analysis of each bone in the atlas compared to the input images. Validation showed a mean z-score of 0.003 across metrics (volume, surface area, axis lengths, elongation, and flatness), confirming anatomical accuracy. Atlas-based segmentation accuracy, assessed on three independent images, yielded a Dice coefficient of 0.94 and Hausdorff distance of 1.73 mm. Additionally, a subtraction-based method applied to rheumatoid arthritis (RA)-affected joints achieved a mean erosion volume difference of 4.9% compared to manual annotations. This CBCT-based atlas is a valuable reference for segmentation and analysis in bone-related disease research, particularly for tracking RA-induced changes in bone morphology.

University of Calgary (CA)
Canada Foundation for Innovation
Openalex Percentile: Top 84%
Radiomics and Machine Learning in Medical Imaging
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.