Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

Austin J Ramme; Nicole DeVries; Nicole A Kallemyn; Vincent A Magnotta; Nicole M Grosland

doi:10.1007/s10278-008-9151-y

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Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

Journal article

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Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm

Austin J Ramme, Nicole DeVries, Nicole A Kallemyn, Vincent A Magnotta and Nicole M Grosland

Journal of digital imaging, Vol.22(5), pp.483-491

10/2009

DOI: 10.1007/s10278-008-9151-y

PMCID: PMC2745490

PMID: 18769967

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https://doi.org/10.1007/s10278-008-9151-yView

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Abstract

Medical imaging technologies have allowed for in vivo exploration and evaluation of the human musculoskeletal system. Three-dimensional bone models generated using image-segmentation techniques provide a means to optimize individualized orthopedic surgical procedures using engineering analyses. However, many of the current segmentation techniques are not clinically practical due to the required time and human intervention. As a proof of concept, we demonstrate the use of an expectation maximization (EM) algorithm to segment the hand phalanx bones, and hypothesize that this semi-automated technique will improve the efficiency while providing similar definitions as compared to a manual rater. Our results show a relative overlap of the proximal, middle, and distal phalanx bones of 0.83, 0.79, and 0.72 for the EM technique when compared to validated manual segmentations. The EM segmentations were also compared to 3D surface scans of the cadaveric specimens, which resulted in distance maps showing an average distance for the proximal, middle, and distal phalanx bones of 0.45, 0.46, and 0.51 mm, respectively. The EM segmentation improved on the segmentation speed of the manual techniques by a factor of eight. Overall, the manual segmentations had greater relative overlap metric values, which suggests that the manual segmentations are a better fit to the actual surface of the bone. As shown by the comparison to the bone surface scans, the EM technique provides a similar representation of the anatomic structure and offers an increase in efficiency that could help to reduce the time needed for defining anatomical structures from CT scans.

computed tomography

artificial neural network

bone

3D Segmentation

hand

Details

Title: Subtitle: Semi-automated Phalanx Bone Segmentation Using the Expectation Maximization Algorithm
Creators: Austin J Ramme - Carver College of Medicine, The University of Iowa, Iowa City, IA USA
Nicole DeVries - Department of Biomedical Engineering, The University of Iowa, Iowa City, IA USA
Nicole A Kallemyn - Department of Biomedical Engineering, The University of Iowa, Iowa City, IA USA
Vincent A Magnotta - Department of Radiology, The University of Iowa, Iowa City, IA USA
Nicole M Grosland - Department of Biomedical Engineering, The University of Iowa, Iowa City, IA USA
Resource Type: Journal article
Publication Details: Journal of digital imaging, Vol.22(5), pp.483-491
DOI: 10.1007/s10278-008-9151-y
PMID: 18769967
PMCID: PMC2745490
NLM abbreviation: J Digit Imaging
ISSN: 0897-1889
eISSN: 1618-727X
Publisher: Springer-Verlag; New York
Language: English
Date published: 10/2009
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Psychiatry; Iowa Technology Institute; Iowa Neuroscience Institute; Orthopedics and Rehabilitation; Injury Prevention Research Center
Record Identifier: 9984040019702771

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