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- W4385545710 abstract "Quantification of 129 Xe MRI relies on accurate segmentation of the thoracic cavity. This segmentation could potentially be performed directly on the 129 Xe ventilation image using an automated convolutional neural network, but this task is challenging, especially in cases where peripheral ventilation defects obscure the lung boundary. Currently, overcoming this obstacle requires large, diverse training datasets created by time-consuming manual segmentation. Here, we demonstrate the use of a generative Pix2Pix model to synthesize both 129 Xe images with defects, and corresponding segmentation masks. We then test the effects of this additional training data on the performance of an existing U-net segmentation algorithm." @default.
- W4385545710 created "2023-08-04" @default.
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- W4385545710 date "2023-08-03" @default.
- W4385545710 modified "2023-09-27" @default.
- W4385545710 title "Ventilation Defect Synthesis in Hyperpolarized 129Xe Ventilation MRI to Accelerate Training of Segmentation Models" @default.
- W4385545710 doi "https://doi.org/10.58530/2022/1178" @default.
- W4385545710 hasPublicationYear "2023" @default.
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