Journal article
Automated bony region identification using artificial neural networks: reliability and validation measurements
Skeletal radiology, Vol.37(4), pp.313-319
04/2008
DOI: 10.1007/s00256-007-0434-z
PMID: 18172639
Abstract
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.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.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.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.
Details
- Title: Subtitle
- Automated bony region identification using artificial neural networks: reliability and validation measurements
- Creators
- Esther Gassman - Center for Computer-Aided Design The University of Iowa Iowa City IA 52242 USAStephanie Powell - Department of Radiology University of Iowa Hospitals and Clinics, The University of Iowa Iowa City IA 52242 USANicole Kallemeyn - Center for Computer-Aided Design The University of Iowa Iowa City IA 52242 USANicole DeVries - Center for Computer-Aided Design The University of Iowa Iowa City IA 52242 USAKiran Shivanna - Center for Computer-Aided Design The University of Iowa Iowa City IA 52242 USAVincent Magnotta - Department of Radiology University of Iowa Hospitals and Clinics, The University of Iowa Iowa City IA 52242 USAAustin Ramme - Department of Radiology University of Iowa Hospitals and Clinics, The University of Iowa Iowa City IA 52242 USABrian Adams - Department of Orthopaedics and Rehabilitation University of Iowa Hospitals and Clinics, The University of Iowa Iowa City IA 52242 USANicole Grosland - Center for Computer-Aided Design The University of Iowa Iowa City IA 52242 USA
- Resource Type
- Journal article
- Publication Details
- Skeletal radiology, Vol.37(4), pp.313-319
- DOI
- 10.1007/s00256-007-0434-z
- PMID
- 18172639
- NLM abbreviation
- Skeletal Radiol
- ISSN
- 0364-2348
- eISSN
- 1432-2161
- Publisher
- Springer-Verlag; Berlin/Heidelberg
- Language
- English
- Date published
- 04/2008
- 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
- 9984040385502771
Metrics
33 Record Views