Intelligent scanning: automated standard plane selection and biometric measurement of early gestational sac in routine ultrasound examination.

Authors:
Dr. Ling Zhang, PhD
Dr. Ling Zhang, PhD
University of Iowa
Iowa city, IA | United States

Med Phys 2012 Aug;39(8):5015-27

Department of Biomedical Engineering, Zhejiang University, Hangzhou, Zhejiang 310027, China.

Purpose: To assist radiologists and decrease interobserver variability when using 2D ultrasonography (US) to locate the standardized plane of early gestational sac (SPGS) and to perform gestational sac (GS) biometric measurements.

Methods: In this paper, the authors report the design of the first automatic solution, called "intelligent scanning" (IS), for selecting SPGS and performing biometric measurements using real-time 2D US. First, the GS is efficiently and precisely located in each ultrasound frame by exploiting a coarse to fine detection scheme based on the training of two cascade AdaBoost classifiers. Next, the SPGS are automatically selected by eliminating false positives. This is accomplished using local context information based on the relative position of anatomies in the image sequence. Finally, a database-guided multiscale normalized cuts algorithm is proposed to generate the initial contour of the GS, based on which the GS is automatically segmented for measurement by a modified snake model.

Results: This system was validated on 31 ultrasound videos involving 31 pregnant volunteers. The differences between system performance and radiologist performance with respect to SPGS selection and length and depth (diameter) measurements are 7.5% ± 5.0%, 5.5% ± 5.2%, and 6.5% ± 4.6%, respectively. Additional validations prove that the IS precision is in the range of interobserver variability. Our system can display the SPGS along with biometric measurements in approximately three seconds after the video ends, when using a 1.9 GHz dual-core computer.

Conclusions: IS of the GS from 2D real-time US is a practical, reproducible, and reliable approach.

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Source
http://dx.doi.org/10.1118/1.4736415DOI Listing
August 2012
57 Reads
2 Citations
2.635 Impact Factor

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Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information
Feng et al.
2009
Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering
Tu et al.
2005

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