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.

翻译：精准的脑肿瘤检测与分割对医学诊断至关重要。然而，当前监督学习方法需要大量标注图像，而无监督方法中使用的先进生成模型常受限于无法覆盖完整数据分布。本文提出一种新颖的两阶段生成模型框架（TSGM），该模型结合了循环生成对抗网络（CycleGAN）和基于联合概率的方差爆炸随机微分方程（VE-JP），用以提升脑肿瘤检测与分割性能。CycleGAN通过在无配对数据上训练，从健康图像生成异常图像作为数据先验。随后，VE-JP以合成配对异常图像为引导重建健康图像，仅改变病理区域而不影响健康区域。值得注意的是，本方法直接学习了用于条件生成的联合概率分布。输入图像与重建图像间的残差指示异常区域，并随后采用阈值法获取分割结果。此外，通过为多模态结果赋予不同权重进一步优化分割精度。我们在三个数据集上验证所提方法，并与现有无监督异常检测与分割方法进行对比。在BraTs2020数据集、ITCS数据集及内部数据集上的DSC评分分别为0.8590、0.6226和0.7403，表明本方法在分割性能及泛化能力上均更优。