# PlaNet-S: Automatic Semantic Segmentation of Placenta Source: [PlaNet-S: Automatic Semantic Segmentation of Placenta]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:57:17.532Z Authors: Shinnosuke Yamamoto, Isso Saito, Eichi Takaya, Ayaka Harigai, Tomomi Sato, Tomoya Kobayashi, Kei Takase, Takuya Ueda Published: 2023-12-18T10:55:11.000Z ## Abstract \[Purpose\] 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()