ML Anthology

Automatic Ovarian Follicle Quantification from 3D Ultrasound Data Using Global/local Context with Database Guided Segmentation

Terrence Chen, Wei Zhang, Sara Good, Shaohua Kevin Zhou, Dorin Comaniciu
ICCV 2009 pp. 795-802
doi:10.1109/ICCV.2009.5459243 /iccv/2009/chen2009iccv-automatic/

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.

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Text

Chen et al. "Automatic Ovarian Follicle Quantification from 3D Ultrasound Data Using Global/local Context with Database Guided Segmentation." IEEE/CVF International Conference on Computer Vision, 2009. doi:10.1109/ICCV.2009.5459243

Markdown

[Chen et al. "Automatic Ovarian Follicle Quantification from 3D Ultrasound Data Using Global/local Context with Database Guided Segmentation." IEEE/CVF International Conference on Computer Vision, 2009.](https://mlanthology.org/iccv/2009/chen2009iccv-automatic/) doi:10.1109/ICCV.2009.5459243

BibTeX

@inproceedings{chen2009iccv-automatic,
  title     = {{Automatic Ovarian Follicle Quantification from 3D Ultrasound Data Using Global/local Context with Database Guided Segmentation}},
  author    = {Chen, Terrence and Zhang, Wei and Good, Sara and Zhou, Shaohua Kevin and Comaniciu, Dorin},
  booktitle = {IEEE/CVF International Conference on Computer Vision},
  year      = {2009},
  pages     = {795-802},
  doi       = {10.1109/ICCV.2009.5459243},
  url       = {https://mlanthology.org/iccv/2009/chen2009iccv-automatic/}
}