PlaNet-S: Automatic Semantic Segmentation of Placenta
Shinnosuke Yamamoto Isso Saito Eichi Takaya Ayaka Harigai Tomomi Sato Tomoya Kobayashi Kei Takase Takuya Ueda
[Purpose] To develop a fully automated semantic placenta segmentation model
that integrates the U-Net and SegNeXt architectures through ensemble learning.
[Methods] A total of 218 pregnant women with suspected placental anomalies who
underwent magnetic resonance imaging (MRI) were enrolled, yielding 1090
annotated images for developing a deep learning model for placental
segmentation. The images were standardized and divided into training and test
sets. The performance of PlaNet-S, which integrates U-Net and SegNeXt within an
ensemble framework, was assessed using Intersection over Union (IoU) and
counting connected components (CCC) against the U-Net model. [Results] PlaNet-S
had significantly higher IoU (0.73 +/- 0.13) than that of U-Net (0.78 +/-
0.010) (p<0.01). The CCC for PlaNet-S was significantly higher than that for
U-Net (p<0.01), matching the ground truth in 86.0\% and 56.7\% of the cases,
respectively. [Conclusion]PlaNet-S performed better than the traditional U-Net
in placental segmentation tasks. This model addresses the challenges of
time-consuming physician-assisted manual segmentation and offers the potential
for diverse applications in placental imaging analyses.