PlaNet-S: an Automatic Semantic Segmentation Model for Placenta Using U-Net and SegNeXt.

Journal: Journal of imaging informatics in medicine
Published Date: May 27, 2025

Abstract

This study aimed to develop a fully automated semantic placenta segmentation model that integrates the U-Net and SegNeXt architectures through ensemble learning. A total of 218 pregnant women with suspected placental abnormalities 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 Placental Segmentation Network (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, U-Net + + , and DS-transUNet. PlaNet-S had significantly higher IoU (0.78, SD = 0.10) than that of U-Net (0.73, SD = 0.13) (p < 0.005) and DS-transUNet (0.64, SD = 0.16) (p < 0.005), while the difference with U-Net + + (0.77, SD = 0.12) was not statistically significant. The CCC for PlaNet-S was significantly higher than that for U-Net (p < 0.005), U-Net + + (p < 0.005), and DS-transUNet (p < 0.005), matching the ground truth in 86.0%, 56.7%, 67.9%, and 20.9% of the cases, respectively. PlaNet-S achieved higher IoU than U-Net and DS-transUNet, and comparable IoU to U-Net + + . Moreover, PlaNet-S significantly outperformed all three models in CCC, indicating better agreement with the ground truth. This model addresses the challenges of time-consuming physician-assisted manual segmentation and offers the potential for diverse applications in placental imaging analyses.

Authors

  • Isso Saito
    Department of Clinical Imaging, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
  • Shinnosuke Yamamoto
    Department of Clinical Imaging, Tohoku University Graduate School of Medicine, Sendai, Miyagi, Japan.
  • Eichi Takaya
    Graduate School of Science and Technology, Keio University.
  • Ayaka Harigai
    Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
  • Tomomi Sato
    Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8574, Japan.
  • Tomoya Kobayashi
    Department of Clinical Imaging, Graduate School of Medicine, Tohoku University, Miyagi, Japan.
  • Kei Takase
    Department of Diagnostic Radiology, Tohoku University Hospital, 1-1 Seiryo-machi, Aoba-ku, Sendai, 980-8574, Japan.
  • Takuya Ueda
    Department of Clinical Imaging, Tohoku University Graduate School of Medicine, 2-1 Seiryo-Machi, Aoba-Ku, Sendai, Miyagi, 980-8575, Japan. takuya.ueda.d3@tohoku.ac.jp.

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