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

Medical Physics

Volumn 39, Issue 8, 2012, Pages 5015-5027

  Zhang, Ling ^a   Chen, Siping ^b   Chin, Chien Ting ^b   Wang, Tianfu ^b   Li, Shengli ^c  

^a ZHEJIANG UNIVERSITY   (China)

^b SHENZHEN UNIVERSITY   (China)

^c Hospital of Nanfang Medical University   (China)

Author keywords

AdaBoost learning; database guided multiscale normalized cuts; diameter of gestational sac; intelligent scanning; standard plane of gestational sac

Indexed keywords

ADAPTIVE BOOSTING; CLASSIFICATION (OF INFORMATION); SCANNING; ULTRASONIC APPLICATIONS;

ADA BOOST CLASSIFIERS; ADABOOST LEARNING; BIOMETRIC MEASUREMENTS; COARSE-TO-FINE DETECTIONS; DIAMETER OF GESTATIONAL SAC; INTEROBSERVER VARIABILITY; NORMALIZED CUTS; ULTRASOUND EXAMINATION;

BIOMETRICS;

EID: 84864666518     PISSN: 00942405     EISSN: None     Source Type: Journal    
DOI: 10.1118/1.4736415     Document Type: Article

References (40)

1. M. Rousian, A. H. J. Koning, W. C. Hop, P. J. van der Spek, N. Exalto, and E. A. P. Steegers, Gestational sac fluid volume measurements in virtual reality., Ultrasound Obstet. Gynecol. 38, 524-529 (2011). 10.1002/uog.9033
2. J. P. Neilson, Ultrasound for fetal assessment in early pregnancy., Cochrane Database Syst. Rev. 2, 1-22 (1998).
3. W. Lee, R. L. Deter, B. Mcnie, M. Powell, and M. Balasubramaniam, Quantitative and morphological assessment of early gestational sacs using three-dimensional ultrasonography., Ultrasound Obstet. Gynecol. 28, 255-260 (2006). 10.1002/uog.2840 (Pubitemid 44409050)
4. R. L. Deter, J. Li, W. Lee, S. Liu, and R. Romero, Quantitative assessment of gestational sac shape: the gestational sac shape score., Ultrasound Obstet. Gynecol. 29, 574-582 (2007). 10.1002/uog.3994 (Pubitemid 46826560)
5. O. Falcon, P. Wegrzyn, C. Faro, C. F. A. Peralta, and K. H. Nicolaides, Gestational sac volume measured by three-dimensional ultrasound at 11 to 13 6 weeks of gestation: relation to chromosomal defects., Ultrasound Obstet. Gynecol. 25, 546-550 (2005). 10.1002/uog.1898 (Pubitemid 40835456)
6. X. G. Lu, B. Georgescu, Y. F. Zheng, J. Otsuki, and D. Comaniciu, AUTOMPR: automatic detection of standard planes in 3D echocardiography., in Proceedings of IEEE International Symposium on Biomedical Imaging (IEEE, Paris, France, 2008), pp. 1279-1282.
7. S. Feng, S. K. Zhou, S. Good, and D. Comaniciu, Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information., in Proceedings of IEEE International Confeference on Computer Vision and Pattern Recognittion (IEEE Computer Society, Miami Beach, FL, 2009), pp. 2480-2487.
8. G. Carneiro, F. Amat, B. Georgescu, S. Good, and D. Comaniciu, Automatic fetal measurement from three-dimensional ultrasound data., Siemens Corporate Research, Inc., U. S. Pub. No. US 2009/0093717 A1, September 2008.
9. B. Rahmatullah, A. Papageorghiou, and J. A. Noble, Automated selection of standardized planes from ultrasound volume., Machine Learning in Medical Imaging Series, Lecture Notes in Computer Science Vol. 7009 (Springer, Toronto, Canada, 2011), pp. 35-42.
10. Z. Tu, Probabilistic boosting-tree: Learning discriminative models for classification, recognition, and clustering., in Proceedings of IEEE International Conference on Computer Vision (IEEE Computer Society, Beijing, China, 2005), pp. 1589-1596. (Pubitemid 44042367)
11. A. Abuhamad, P. Falkensammer, F. Reichartseder, and Y. Zhao, Automated retrieval of standard diagnostic fetal cardiac ultrasound planes in the second trimester of pregnancy: a prospective evaluation of software., Ultrasound Obstet. Gynecol. 31, 30-36 (2008). 10.1002/uog.5228 (Pubitemid 351211928)
12. G. Carneiro, B. Georgescu, S. Good, and D. Comaniciu, Detection and measurement of fetal anatomies from ultrasound images using a constrained probabilistic boosting tree., IEEE Trans. Med. Imaging 27, 1342-1355 (2008). 10.1109/TMI.2008.928917
13. N. Archip, R. Rohling, P. Cooperberg, and H. Tahmasebpour, Ultrasound image segmentation using spectral clustering., Ultrasound Med. Biol. 30, 1485-1497 (2005). 10.1016/j.ultrasmedbio.2005.07.005 (Pubitemid 41606263)
14. J. B. Shi and J. Malik, Normalized cuts and image segmentation., IEEE Trans. Pattern Anal. Mach. Intell. 22, 888-905 (2000). 10.1109/34.868688
15. M. Borenstein, G. Azumendi Perez, F. Molina Garcia, M. Romero, and J. R. Anderica, Gestational sac volume: Comparison between SonoAVC and VOCAL measurements at 11 0 to 13 6 weeks of gestation., Ultrasound Obstet. Gynecol. 34, 510-514 (2009). 10.1002/uog.7342
16. G. Acharya and H. Morgan, First-trimester, three-dimensional transvaginal ultrasound volumetry in normal pregnancies and spontaneous miscarriages., Ultrasound Obstet. Gynecol. 19, 575-579 (2002). 10.1046/j.1469-0705.2002.00712.x (Pubitemid 34626121)
17. T. Muller, M. Sutterlin, U. Pohls, and J. Dietl, Transvaginal volumetry of first trimester gestational sac: a comparison of conventional with three-dimensional ultrasound., J. Perinat. Med. 28, 214-220 (2000). 10.1515/JPM.2000.029 (Pubitemid 30417248)
18. L. W. Chan, T. Y. Fung, T. Y. Leung, D. S. Sahota and T. K. Lau, Volumetric (3D) imaging reduces inter- and intraobserver variation of fetal biometry measurements., Ultrasound Obstet. Gynecol. 33, 447-452 (2009). 10.1002/uog.6321
19. S. T. Elliott, Volume ultrasound: the next big thing, Br. J. Radiol. 81, 8-9 (2008). 10.1259/bjr/13475432 (Pubitemid 351154768)
20. M. Eckhardt, I. Fasel, and J. Movellan, Towards practical facial feature detection., Int. J. Pattern Recognit. Artif. Intell. 23, 379-400 (2009). 10.1142/S0218001409007247
21. B. Georgescu, X. Zhou, D. Comaniciu, and A. Gupta, Database-guided segmentation of anatomical structures with complex appearance., in Proceedings of IEEE International Conference on Computter Vision and Pattern Recognition (IEEE Computer Society, San Diego, CA, 2005), pp. 429-436.
22. R. Lienhart, A. Kuranov, and V. Pisarevsky, Empirical analysis of detection cascade of boosted classifiers for rapid object detection., Pattern Recognition, Proceedings, Lecture Notes in Computer Science Vol. 2781 (Springer, Magdeburg, Germany, 2003), pp. 297-304.
23. Y. Freund and R. Schapire, A decision-theoretic generalization of on-line learning and an application to boosting., J. Comput. Syst. Sci. 55, 119-139 (1996). 10.1006/jcss.1997.1504 (Pubitemid 127433398)
24. P. Viola and M. Jones, Robust real-time face detection., Int. J. Comput. Vis. 57, 137-154 (2004). 10.1023/B:VISI.0000013087.49260.fb
25. S. H. Huang, Y. H. Chu, S. H. Lai, and C. L. Novak, Learning-based vertebra detection and iterative normalized-cut segmentation for spinal MRI., IEEE Trans. Med. Imaging 28, 1595-1605 (2009). 10.1109/TMI.2009.2023362
26. T. Karavides, K. Y. E. Leung, P. Paclik, E. A. Hendriks, and J. G. Bosch, Database guided detection of anatomical landmark points in 3D images of the heart., Proc. SPIE 7623, 76234Q.1-76234Q.8 (2010). 10.1117/12.843802
27. T. Cour, F. Benezit, and J. B. Shi, Spectral segmentation with multiscale graph decomposition., in Proceedings of IEEE International Confeference on Computer Vision Pattern Recognittion (IEEE Computer Society, San Diego, CA, (2005), pp. 1124-1131.
28. M. Kass, A. Witkin, and D. Terzopoulos, Snakes: Active contour models., Int. J. Comput. Vis. 1, 321-331 (1988). 10.1007/BF00133570
29. J. A. Noble and D. Boukerroui, Ultrasound image segmentation: A survey., IEEE Trans. Med. Imaging 25, 987-1010 (2006). 10.1109/TMI.2006.877092 (Pubitemid 44192172)
30. T. Chen, W. Zhang, S. Good, K. S. Zhou, and D. Comaniciu, Automatic follicle quantification from 3D ultrasound data using globallocal context with database guided segmentation., in Proceedings of International Conference on Computer Vision (IEEE Computer Society, Kyoto, Japan, 2009), pp. 795-802.
31. L. Zhang, S. P. Chen, S. L. Li, and T. F. Wang, Automated measurement of early gestational sac diameters from one scan session., Proc. SPIE 7963, 796342.1-796342.8 (2011). 10.1117/12.881343
32. Y. Zheng, A. Barbu, B. Georgescu, M. Scheuering, and D. Comaniciu, Four-chamber heart modeling and automatic segmentation for 3D cardiac CT volumes using marginal space learning and steerable features., IEEE Trans. Med. Imaging 27, 1668-1681 (2008). 10.1109/TMI.2008.2004421
33. H. A. Rowley, S. Baluja, and T. Kanade, Neural network-based face detection., IEEE Trans. Pattern Anal. Mach. Intell. 20, 22-38 (1998). 10.1109/34.655647
34. Y. Peng, Segmentation of medical ultrasound image based on active contour model., M. S. thesis, Sichuan University, China, 2006.
35. D. Comaniciu and P. Meer, Mean shift: A robust approach toward feature space analysis., IEEE Trans. Pattern Anal. Mach. Intell. 24, 603-619 (2002). 10.1109/34.1000236 (Pubitemid 34550429)
36. A. Madabhushi and D. N. Metaxas, Combining low-, high-level, and empirical domain knowledge for automated segmentation of ultrasonic breast lesions., IEEE Trans. Med. Imaging 22, 155-169 (2003). 10.1109/TMI.2002.808364
37. J. H. Yu, Y. Y. Wang, P. Chen, and Y. Z. Shen, Fetal abdominal contour extraction and measurement in ultrasound images., Ultrasound Med. Biol. 34, 169-182 (2008). 10.1016/j.ultrasmedbio.2007.06.026
38. V. Chalana and Y. Kim, A methodology for evaluation of boundary detection algorithms on medical images., IEEE Trans. Med. Imaging 16, 642-652 (1997). 10.1109/42.640755 (Pubitemid 127767643)
39. M. Everingham, L. V. Gool, C. K. I. Williams, J. Winn, and A. Zisserman, The PASCAL visual object classes (VOC) challenge., Int. J. Comput. Vis. 88, 303-338 (2010). 10.1007/s11263-009-0275-4
40. Y. Wang, S. Jiang, H. Wang, Y. H. Guo, B. Liu, Y. Hou, H. Cheng, and J. Tian, CAD algorithms for solid breast masses discrimination: Evaluation of the accuracy and interobserver variability., Ultrasound Med. Biol. 36, 1273-1281 (2010). 10.1016/j.ultrasmedbio.2010.05.010