  Brain tumor analysis in Magnetic Resonance Imaging (MRI) is crucial for
accurate diagnosis and treatment planning. However, the task remains
challenging due to the complexity and variability of tumor appearances, as well
as the scarcity of labeled data. Traditional approaches often address tumor
segmentation and image generation separately, limiting their effectiveness in
capturing the intricate relationships between healthy and pathological tissue
structures. We introduce a novel promptable counterfactual diffusion model as a
unified solution for brain tumor segmentation and generation in MRI. The key
innovation lies in our mask-level prompting mechanism at the sampling stage,
which enables guided generation and manipulation of specific healthy or
unhealthy regions in MRI images. Specifically, the model's architecture allows
for bidirectional inference, which can segment tumors in existing images and
generate realistic tumor structures in healthy brain scans. Furthermore, we
present a two-step approach for tumor generation and position transfer,
showcasing the model's versatility in synthesizing realistic tumor structures.
Experiments on the BRATS2021 dataset demonstrate that our method outperforms
traditional counterfactual diffusion approaches, achieving a mean IoU of 0.653
and mean Dice score of 0.785 for tumor segmentation, outperforming the 0.344
and 0.475 of conventional counterfactual diffusion model. Our work contributes
to improving brain tumor detection and segmentation accuracy, with potential
implications for data augmentation and clinical decision support in
neuro-oncology. The code is available at
https://github.com/arcadelab/counterfactual_diffusion.
