Medical image segmentation is crucial for enhancing diagnostic accuracy and treatment planning in Magnetic Resonance Imaging (MRI). However, acquiring precise lesion masks for segmentation model training demands specialized expertise and significant time investment, leading to a small dataset scale in clinical practice. In this paper, we present ZECO, a ZeroFusion guided 3D MRI conditional generation framework that extracts, compresses, and generates high-fidelity MRI images with corresponding 3D segmentation masks to mitigate data scarcity. To effectively capture inter-slice relationships within volumes, we introduce a Spatial Transformation Module that encodes MRI images into a compact latent space for the diffusion process. Moving beyond unconditional generation, our novel ZeroFusion method progressively maps 3D masks to MRI images in latent space, enabling robust training on limited datasets while avoiding overfitting. ZECO outperforms state-of-the-art models in both quantitative and qualitative evaluations on Brain MRI datasets across various modalities, showcasing its exceptional capability in synthesizing high-quality MRI images conditioned on segmentation masks.
View on arXiv@article{wang2025_2503.18246,
title={ ZECO: ZeroFusion Guided 3D MRI Conditional Generation },
author={ Feiran Wang and Bin Duan and Jiachen Tao and Nikhil Sharma and Dawen Cai and Yan Yan },
journal={arXiv preprint arXiv:2503.18246},
year={ 2025 }
}