AutoPaint: A Self-Inpainting Method for Unsupervised Anomaly Detection
Mehdi Astaraki Francesca De Benetti Yousef Yeganeh Iuliana Toma-Dasu Örjan Smedby Chunliang Wang Nassir Navab Thomas Wendler
Robust and accurate detection and segmentation of heterogenous tumors
appearing in different anatomical organs with supervised methods require
large-scale labeled datasets covering all possible types of diseases. Due to
the unavailability of such rich datasets and the high cost of annotations,
unsupervised anomaly detection (UAD) methods have been developed aiming to
detect the pathologies as deviation from the normality by utilizing the
unlabeled healthy image data. However, developed UAD models are often trained
with an incomplete distribution of healthy anatomies and have difficulties in
preserving anatomical constraints. This work intends to, first, propose a
robust inpainting model to learn the details of healthy anatomies and
reconstruct high-resolution images by preserving anatomical constraints.
Second, we propose an autoinpainting pipeline to automatically detect tumors,
replace their appearance with the learned healthy anatomies, and based on that
segment the tumoral volumes in a purely unsupervised fashion. Three imaging
datasets, including PET, CT, and PET-CT scans of lung tumors and head and neck
tumors, are studied as benchmarks for evaluation. Experimental results
demonstrate the significant superiority of the proposed method over a wide
range of state-of-the-art UAD methods. Moreover, the unsupervised method we
propose produces comparable results to a robust supervised segmentation method
when applied to multimodal images.