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 zero-shot 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.
CITATION STYLE
Vétil, R., Abi-Nader, C., Bône, A., Vullierme, M. P., Rohé, M. M., Gori, P., & Bloch, I. (2022). Learning Shape Distributions from Large Databases of Healthy Organs: Applications to Zero-Shot and Few-Shot Abnormal Pancreas Detection. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13432 LNCS, pp. 464–473). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16434-7_45
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