S127 - Semi-supervised learning in perivascular space segmentation using MRI images

Yaqiong Chai, Hedong Zhang, Gilsoon Park, Erika Lopez, Cong Zang, Jongmok Ha, Hosung Kim

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Accurate segmentation of perivascular space (PVS) is essential for its quantitative analysis and clinical applications. Various segmentation methods have been proposed, but semi-supervised learning methods have never been attempted. Here, a 3D multi-channel, multi-scale semi-supervised PVS segmentation (M2SS-PVS) network is proposed. The proposed network incorporated multi-scale image features in the encoder and applied a few strategies to mitigate class imbalance issue. The proposed M2SS-PVS network segmented PVS with the highest accuracy and high sensitivity among all the tested supervised and semi-supervised methods.
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Schedule: Monday, July 10: Posters — 11:00–12:00 & 15:00–16:00
Poster location: M53