分两阶段合成缺失脑部MRI，提升肿瘤分割效果
Two-Stage Approach for Brain MR Image Synthesis: 2D Image Synthesis and 3D Refinement
- 先用2D切片合成，引入新强度编码减少伪影
- 再用3D全体积信息精修，显著提升图像质量
- 适合缺序列的脑肿瘤分割任务，实用性强
尽管自动脑肿瘤分割方法已取得显著进展，但在某些MR序列缺失时性能难以保证。为此，需合成反映缺失模态特性的脑部MRI，并精确呈现肿瘤特征。传统方法受计算限制，通常仅生成部分图像而非完整体数据，导致三维信息不全且拼接时出现伪影。本文提出一种两阶段方法：首先基于2D切片，利用新型强度编码方法合成MRI；随后通过依赖完整3D体积信息的精修模块（Refiner）进一步优化合成图像。实验表明，该强度编码有效降低伪影并提升感知质量，使用Refiner后显著改善脑肿瘤分割性能，凸显该方法在实际应用中的潜力。
原文摘要 · Abstract (English)
Despite significant advancements in automatic brain tumor segmentation methods, their performance is not guaranteed when certain MR sequences are missing. Addressing this issue, it is crucial to synthesize the missing MR images that reflect the unique characteristics of the absent modality with precise tumor representation. Typically, MRI synthesis methods generate partial images rather than full-sized volumes due to computational constraints. This limitation can lead to a lack of comprehensive 3D volumetric information and result in image artifacts during the merging process. In this paper, we propose a two-stage approach that first synthesizes MR images from 2D slices using a novel intensity encoding method and then refines the synthesized MRI. The proposed intensity encoding reduces artifacts when synthesizing MRI on a 2D slice basis. Then, the \textit{Refiner}, which leverages complete 3D volume information, further improves the quality of the synthesized images and enhances their applicability to segmentation methods. Experimental results demonstrate that the intensity encoding effectively minimizes artifacts in the synthesized MRI and improves perceptual quality. Furthermore, using the \textit{Refiner} on synthesized MRI significantly improves brain tumor segmentation results, highlighting the potential of our approach in practical applications.
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