3D Reasoning for Unsupervised Anomaly Detection in Pediatric WbMRI

• Main Authors: Chang, Alex, Suriyakumar, Vinith, Moturu, Abhishek, Tu, James, Tewattanarat, Nipaporn, Joshi, Sayali, Doria, Andrea, Goldenberg, Anna
• Format: Journal Article
• Language: English
• Published: 24-03-2021
• Subjects: Computer Science - Computer Vision and Pattern Recognition
• DOI: 10.48550/arxiv.2103.13497

Modern deep unsupervised learning methods have shown great promise for
detecting diseases across a variety of medical imaging modalities. While
previous generative modeling approaches successfully perform anomaly detection
by learning the distribution of healthy 2D image slices, they process such
slices independently and ignore the fact that they are correlated, all being
sampled from a 3D volume. We show that incorporating the 3D context and
processing whole-body MRI volumes is beneficial to distinguishing anomalies
from their benign counterparts. In our work, we introduce a multi-channel
sliding window generative model to perform lesion detection in whole-body MRI
(wbMRI). Our experiments demonstrate that our proposed method significantly
outperforms processing individual images in isolation and our ablations clearly
show the importance of 3D reasoning. Moreover, our work also shows that it is
beneficial to include additional patient-specific features to further improve
anomaly detection in pediatric scans.