[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.
@article{arxiv.2312.11580,
title = {PlaNet-S: Automatic Semantic Segmentation of Placenta},
author = {Shinnosuke Yamamoto and Isso Saito and Eichi Takaya and Ayaka Harigai and Tomomi Sato and Tomoya Kobayashi and Kei Takase and Takuya Ueda},
journal= {arXiv preprint arXiv:2312.11580},
year = {2025}
}
Comments
11 pages, 5 figures, Shinnosuke Yamamoto and Isso Saito equally contributed to this work. In the original submission, there was a typographical error in the reported standard deviation for the Intersection over Union (IoU) values of the PlaNet-S model. The standard deviation was incorrectly listed as 0.01 instead of the correct value of 0.1. This has been corrected in the revised version. J Digit Imaging. Inform. med. (2025)