  We propose a scalable and data-driven approach to learn shape distributions
from large databases of healthy organs. To do so, volumetric segmentation masks
are embedded into a common probabilistic shape space that is learned with a
variational auto-encoding network. The resulting latent shape representations
are leveraged to derive zeroshot and few-shot methods for abnormal shape
detection. The proposed distribution learning approach is illustrated on a
large database of 1200 healthy pancreas shapes. Downstream qualitative and
quantitative experiments are conducted on a separate test set of 224 pancreas
from patients with mixed conditions. The abnormal pancreas detection AUC
reached up to 65.41% in the zero-shot configuration, and 78.97% in the few-shot
configuration with as few as 15 abnormal examples, outperforming a baseline
approach based on the sole volume.
