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Few-shot Meta-learning with Adversarial Shape Prior for Zonal Prostate Segmentation on T2 Weighted MRI

Han Yu¹, Varut Vardhanabhuti¹, and Peng Cao¹
¹The University of Hong Kong, Hong Kong, Hong Kong

We propose a novel gradient-based meta-learning scheme to tackle the challenges when deploying the model to a different medical center with the lack of labeled data. Evaluation results show that our approach outperformed the existing naive U-Net methods.

Figure 1. The schematic illustration of the meta-learning-based zonal segmentation network combines a 2D U-Net and an adversarial network for determining the shape prior.

Figure 2. Validation result of meta-learning based zonal segmentation model with the adversarial network, where (a) presents the T2w images, (b) shows the ground truth of zonal label, and (c) states the results of our approach. The CZ is masked in green, and PZ is in red.