Automated bony region identification using artificial neural networks: reliability and validation measurements.
OBJECTIVE
The objective was to develop tools for automating the identification of bony structures, to assess the reliability of this technique against manual raters, and to validate the resulting regions of interest against physical surface scans obtained from the same specimen.


METHODS
Artificial intelligence-based algorithms have been used for image segmentation, specifically artificial neural networks (ANNs). For this study, an ANN was created and trained to identify the phalanges of the human hand.


RESULTS
The relative overlap between the ANN and a manual tracer was 0.87, 0.82, and 0.76, for the proximal, middle, and distal index phalanx bones respectively. Compared with the physical surface scans, the ANN-generated surface representations differed on average by 0.35 mm, 0.29 mm, and 0.40 mm for the proximal, middle, and distal phalanges respectively. Furthermore, the ANN proved to segment the structures in less than one-tenth of the time required by a manual rater.


CONCLUSIONS
The ANN has proven to be a reliable and valid means of segmenting the phalanx bones from CT images. Employing automated methods such as the ANN for segmentation, eliminates the likelihood of rater drift and inter-rater variability. Automated methods also decrease the amount of time and manual effort required to extract the data of interest, thereby making the feasibility of patient-specific modeling a reality.
Gassman
Esther E
EE
Department of Biomedical Engineering, Seamans Center for the Engineering Arts and Sciences, The University of Iowa, Iowa City, IA 52242, USA.
Powell
Stephanie M
SM
Kallemeyn
Nicole A
NA
Devries
Nicole A
NA
Shivanna
Kiran H
KH
Magnotta
Vincent A
VA
Ramme
Austin J
AJ
Adams
Brian D
BD
Grosland
Nicole M
NM
D000368:Aged; D000465:Algorithms; D002102:Cadaver; D005260:Female; D050278:Finger Phalanges / Q000000981:diagnostic imaging*; D006801:Humans; D008297:Male; D016571:Neural Networks, Computer; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed