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

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.

Automated summary

Automatic segmentation of the placenta in MRI is challenging due to the variability of its position and shape, as well as the blurring caused by PAS. In this study, a refinement fusion based on the U-Net (RFU-Net) is proposed to address these issues and improve the segmentation accuracy. The RFU-Net utilizes ResNet34 as a feature extractor and incorporates a fusion multiscale feature (FMF) to handle the variable placental shape. A refinement segmentation module (RSM) is designed to provide information about the placental position, leading to improved segmentation results. Experimental results demonstrate the effectiveness of RFU-Net in achieving accurate placenta segmentation and highlighting potential areas of interest for diagnosis.