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Automatic ovarian follicle quantification from 3D ultrasound data using global/local context with database guided segmentation (2009)
| Citations: | 4 - 4 self |
BibTeX
@MISC{Chen09automaticovarian,
author = {Terrence Chen and Wei Zhang and Sara Good and Kevin S. Zhou and Dorin Comaniciu},
title = {Automatic ovarian follicle quantification from 3D ultrasound data using global/local context with database guided segmentation},
year = {2009}
}
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Abstract
In this paper, we present a novel probabilistic framework for automatic follicle quantification in 3D ultrasound data. The proposed framework robustly estimates size and location of each individual ovarian follicle by fusing the information from both global and local context. Follicle candidates at detected locations are then segmented by a novel database guided segmentation method. To efficiently search hypothesis in a high dimensional space for multiple object detection, a clustered marginal space learning approach is introduced. Extensive evaluations conducted on 501 volumes containing 8108 follicles showed that our method is able to detect and segment ovarian follicles with high robustness and accuracy. It is also much faster than the current ultrasound manual workflow. The proposed method is able to streamline the clinical workflow and improve the accuracy of existing follicular measurements.
Keyphrases
ultrasound data automatic ovarian follicle quantification global local context clinical workflow local context automatic follicle quantification high robustness follicle candidate novel database follicular measurement segment ovarian follicle clustered marginal space multiple object detection segmentation method novel probabilistic framework individual ovarian follicle current ultrasound manual workflow extensive evaluation detected location high dimensional space
