title: Discriminative Learning for Automatic Staging of Placental Maturity via Multi-layer Fisher Vector.
abstract: Currently, placental maturity is performed using subjective evaluation, which can be unreliable as it is highly dependent on the observations and experiences of clinicians. To address this problem, this paper proposes a method to automatically stage placenta maturity from B-mode ultrasound (US) images based on dense sampling and novel feature descriptors. Specifically, our proposed method first densely extracts features with a regular grid based on dense sampling instead of a few unreliable interest points. Followed by, these features are clustered using generative Gaussian mixture model (GMM) to obtain high order statistics of the features. The clustering representatives (i.e., cluster means) are encoded by Fisher vector (FV) for staging accuracy enhancement. Differing from the previous studies, a multi-layer FV is investigated to exploit the spatial information rather than the single layer FV. Experimental results show that the proposed method with the dense FV has achieved an area under the receiver of characteristics (AUC) of 96.77%, sensitivity and specificity of 98.04% and 93.75% for the placental maturity staging, respectively. Our experimental results also demonstrate that the dense feature outperforms the traditional sparse feature for placental maturity staging.
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, Nanhai Ave 3688, Shenzhen, Guangdong, 518060, P.R.China.
authors.forename: Baiying
authors.initials: B
authors.lastname: Lei
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, Nanhai Ave 3688, Shenzhen, Guangdong, 518060, P.R.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, Nanhai Ave 3688, Shenzhen, Guangdong, 518060, P.R.China.
authors.forename: Wanjun
authors.initials: W
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, Nanhai Ave 3688, Shenzhen, Guangdong, 518060, P.R.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, Nanhai Ave 3688, Shenzhen, Guangdong, 518060, P.R.China.
authors.forename: Tianfu
authors.initials: T
authors.lastname: Wang
mesh_terms: D016000:Cluster Analysis; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010920:Placenta / Q000000981:diagnostic imaging* / Q000502:physiology; D011247:Pregnancy; D060388:Support Vector Machine; D016216:Ultrasonography, Prenatal / Q000379:methods*