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PlaNet-S: Automatic Semantic Segmentation of Placenta
June 28, 2024, 4:47 a.m. | Shinnosuke Yamamoto, Isso Saito, Eichi Takaya, Ayaka Harigai, Tomomi Sato, Tomoya Kobayashi, Kei Takase, Takuya Ueda
cs.CV updates on arXiv.org arxiv.org
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 …
abstract architectures arxiv automated cs.cv deep learning eess.iv ensemble images imaging mri planet replace segmentation semantic through total type women
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