Placenta segmentation in magnetic resonance imaging: Addressing position and shape of uncertainty and blurred placenta boundary

Authors:
raw_author_name: Zhijie Shi
raw_affiliation_string: School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
raw_author_name: Jin Liu
raw_affiliation_string: School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China
raw_author_name: Lihua Qiu
raw_affiliation_string: Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Liqun Wang
raw_affiliation_string: School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
raw_author_name: Yuling Li
raw_affiliation_string: Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, China
raw_author_name: Dawei Zhang
raw_affiliation_string: School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
raw_author_name: Haima Yang
raw_affiliation_string: Key Laboratory of Space Active Opto-electronics Technology, Chinese Academy of Sciences, Shanghai, China; School of Optical Electrical and Computer Engineering, University of Shanghai for Science and Technology, Shanghai, China
raw_author_name: Le Fu
raw_affiliation_string: Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai, China

Abstract: Automatic segmentation of the placenta in MRI facilitates quantitative measurements and further analysis. Automatic segmentation of the placenta in magnetic resonance imaging (MRI) is challenging due to the fact that (1) the position of the placenta varies greatly in different views of MRI. In addition, the shape of the placenta changes layer by layer in different MRI slices. The position and shape of the placenta are highly uncertain. (2) PAS results in a blurred placental boundary. To address the above issues, a refinement fusion based on the U-Net (RFU-Net) is proposed for the segmentation of the placenta with variable position and shape in MR images, and the boundary de-precision explores the distribution of blood vessels around the placental boundary. In RFU-Net, ResNet34 is used as a feature extractor, and a fusion multiscale feature (FMF) is proposed to extract multiscale features and their contextual information to solve the problem of variable placental shape. A refinement segmentation module (RSM) is designed to provide information about the position of the placenta to solve the problem of variable placental position, which effectively improves the segmentation of the placenta. The mean intersection over union (MIoU), hausdorff distance (HD), dice coefficient (Dice) and accuracy (Acc) are 0.8620, 4.3142, 0.9314 and 0.9987, respectively. Extensive experiments show that RFU-Net effectively improves the segmentation accuracy of the placenta. Boundary de-precision resolves the blurring of placental boundaries, effectively highlighting areas of the placental boundary that may contain blood vessels to aid in the physician's diagnosis.