Anatomy-informed Data Augmentation for Enhanced Prostate Cancer
  Detection
Balint Kovacs Nils Netzer Michael Baumgartner Carolin Eith Dimitrios Bounias Clara Meinzer Paul F. Jaeger Kevin S. Zhang Ralf Floca Adrian Schrader Fabian Isensee Regula Gnirs Magdalena Goertz Viktoria Schuetz Albrecht Stenzinger Markus Hohenfellner Heinz-Peter Schlemmer Ivo Wolf David Bonekamp Klaus H. Maier-Hein
Data augmentation (DA) is a key factor in medical image analysis, such as in
prostate cancer (PCa) detection on magnetic resonance images. State-of-the-art
computer-aided diagnosis systems still rely on simplistic spatial
transformations to preserve the pathological label post transformation.
However, such augmentations do not substantially increase the organ as well as
tumor shape variability in the training set, limiting the model's ability to
generalize to unseen cases with more diverse localized soft-tissue
deformations. We propose a new anatomy-informed transformation that leverages
information from adjacent organs to simulate typical physiological deformations
of the prostate and generates unique lesion shapes without altering their
label. Due to its lightweight computational requirements, it can be easily
integrated into common DA frameworks. We demonstrate the effectiveness of our
augmentation on a dataset of 774 biopsy-confirmed examinations, by evaluating a
state-of-the-art method for PCa detection with different augmentation settings.