  Accurate detection and segmentation of brain tumors is critical for medical
diagnosis. However, current supervised learning methods require extensively
annotated images and the state-of-the-art generative models used in
unsupervised methods often have limitations in covering the whole data
distribution. In this paper, we propose a novel framework Two-Stage Generative
Model (TSGM) that combines Cycle Generative Adversarial Network (CycleGAN) and
Variance Exploding stochastic differential equation using joint probability
(VE-JP) to improve brain tumor detection and segmentation. The CycleGAN is
trained on unpaired data to generate abnormal images from healthy images as
data prior. Then VE-JP is implemented to reconstruct healthy images using
synthetic paired abnormal images as a guide, which alters only pathological
regions but not regions of healthy. Notably, our method directly learned the
joint probability distribution for conditional generation. The residual between
input and reconstructed images suggests the abnormalities and a thresholding
method is subsequently applied to obtain segmentation results. Furthermore, the
multimodal results are weighted with different weights to improve the
segmentation accuracy further. We validated our method on three datasets, and
compared with other unsupervised methods for anomaly detection and
segmentation. The DSC score of 0.8590 in BraTs2020 dataset, 0.6226 in ITCS
dataset and 0.7403 in In-house dataset show that our method achieves better
segmentation performance and has better generalization.
