Deep Learning-based Placental MRI Segmentation and Placental Boundary Vessel Recognition

Motivation: Accurate automatic segmentation of the placenta and identification of blood vessel distribution at the placental borders are crucial for diagnosing placental accrete spectrum (PAS) in MRI, yet effective methods are currently limited. Goal(s): Introduce a refinement fusion method based on U-Net (RFU-Net) for accurately segmenting the placenta. And propose a boundary de-precision (BD) technique to identify the blood vessel distribution around the placental boundary. Approach: MRI of 200 pregnant females were enrolled. RFU-Net and BD were conducted and evaluated. Results: RFU-Net improved the accuracy of placenta segmentation (Dice = 0.9314). The BD resolved the blurring of the placenta boundary. Impact: This study provided a novel method for the automatic identification of placental border vessel distribution.