Unsupervised anomaly segmentation aims to detect patterns that are distinct
from any patterns processed during training, commonly called abnormal or
out-of-distribution patterns, without providing any associated manual
segmentations. Since anomalies during deployment can lead to model failure,
detecting the anomaly can enhance the reliability of models, which is valuable
in high-risk domains like medical imaging. This paper introduces Masked
Modality Cycles with Conditional Diffusion (MMCCD), a method that enables
segmentation of anomalies across diverse patterns in multimodal MRI. The method
is based on two fundamental ideas. First, we propose the use of cyclic modality
translation as a mechanism for enabling abnormality detection.
Image-translation models learn tissue-specific modality mappings, which are
characteristic of tissue physiology. Thus, these learned mappings fail to
translate tissues or image patterns that have never been encountered during
training, and the error enables their segmentation. Furthermore, we combine
image translation with a masked conditional diffusion model, which attempts to
`imagine' what tissue exists under a masked area, further exposing unknown
patterns as the generative model fails to recreate them. We evaluate our method
on a proxy task by training on healthy-looking slices of BraTS2021
multi-modality MRIs and testing on slices with tumors. We show that our method
compares favorably to previous unsupervised approaches based on image
reconstruction and denoising with autoencoders and diffusion models.Comment: Accepted in Multiscale Multimodal Medical Imaging workshop in MICCAI
202