title: Standard plane localization in ultrasound by radial component model and selective search.
abstract: Acquisition of the standard plane is crucial for medical ultrasound diagnosis. However, this process requires substantial experience and a thorough knowledge of human anatomy. Therefore it is very challenging for novices and even time consuming for experienced examiners. We proposed a hierarchical, supervised learning framework for automatically detecting the standard plane from consecutive 2-D ultrasound images. We tested this technique by developing a system that localizes the fetal abdominal standard plane from ultrasound video by detecting three key anatomical structures: the stomach bubble, umbilical vein and spine. We first proposed a novel radial component-based model to describe the geometric constraints of these key anatomical structures. We then introduced a novel selective search method which exploits the vessel probability algorithm to produce probable locations for the spine and umbilical vein. Next, using component classifiers trained by random forests, we detected the key anatomical structures at their probable locations within the regions constrained by the radial component-based model. Finally, a second-level classifier combined the results from the component detection to identify an ultrasound image as either a "fetal abdominal standard plane" or a "non- fetal abdominal standard plane." Experimental results on 223 fetal abdomen videos showed that the detection accuracy of our method was as high as 85.6% and significantly outperformed both the full abdomen and the separate anatomy detection methods without geometric constraints. The experimental results demonstrated that our system shows great promise for application to clinical practice.
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Ni
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Yang
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China.
authors.forename: Xin
authors.initials: X
authors.lastname: Chen
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China.
authors.forename: Chien-Ting
authors.initials: CT
authors.lastname: Chin
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China.
authors.forename: Siping
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Hong Kong, China.
authors.forename: Pheng Ann
authors.initials: PA
authors.lastname: Heng
authors.affiliation: Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare Hospital of Nanfang Medical University, Shenzhen, China. Electronic address: lishengli63@126.com.
authors.forename: Shengli
authors.initials: S
authors.lastname: Li
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China; Center for Human Computer Interaction, Shenzhen Institute of Advanced Integration Technology, Shenzhen, China. Electronic address: harryqinjingcn@gmail.com.
authors.forename: Jing
authors.initials: J
authors.lastname: Qin
authors.affiliation: National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Medicine, Shenzhen University, Shenzhen, China. Electronic address: tfwang@szu.edu.cn.
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
mesh_terms: D000005:Abdomen / Q000000981:diagnostic imaging* / Q000196:embryology; D000465:Algorithms; D005260:Female; D005333:Fetus; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D011247:Pregnancy; D015203:Reproducibility of Results; D013131:Spine / Q000000981:diagnostic imaging; D013270:Stomach / Q000000981:diagnostic imaging; D060388:Support Vector Machine; D016216:Ultrasonography, Prenatal / Q000379:methods*; D014471:Umbilical Veins / Q000000981:diagnostic imaging
keywords: Component; Fetal abdomen; Machine learning; Object detection; Selective search; Standard plane; Ultrasound