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
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.
Schedule: Monday, July 10: Posters — 11:00–12:00 & 15:00–16:00
Poster location: M53