title: Automatic staging of placental maturity based on dense descriptor.
abstract: Currently, placental maturity staging is mainly based on subjective observation of the physician. To address this issue, a new method is proposed for automatic staging of placental maturity based on B-mode ultrasound images. Due to small variations in the placental images, dense descriptor is utilized in place of the sparse descriptor to boost performance. Dense sampled DAISY descriptor is investigated for the demonstrated scale and translation invariant properties. Moreover, the extracted dense features are encoded by vector locally aggregated descriptor (VLAD) for performance boosting. The experimental results demonstrate an accuracy of 0.874, a sensitivity of 0.996 and a specificity of 0.874 for placental maturity staging. The experimental results also show that the dense features outperform the sparse features.
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.
authors.forename: Xinyao
authors.initials: X
authors.lastname: Li
authors.affiliation: Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare, Hospital of Nanfang Medical University, Shenzhen, China.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Yao
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.
authors.forename: Dong
authors.initials: D
authors.lastname: Ni
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.
authors.forename: Siping
authors.initials: S
authors.lastname: Chen
authors.affiliation: Department of Ultrasound, Affiliated Shenzhen Maternal and Child Healthcare, Hospital of Nanfang Medical University, Shenzhen, China.
authors.forename: Shengli
authors.initials: S
authors.lastname: Li
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.
authors.forename: Baiying
authors.initials: B
authors.lastname: Lei
authors.affiliation: Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen, China.
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D005865:Gestational Age; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D010920:Placenta / Q000000981:diagnostic imaging*; D011247:Pregnancy; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016216:Ultrasonography, Prenatal / Q000379:methods*
keywords: Placental staging; daisy descriptor; dense sampling; vector locally aggregated descriptor