多图谱与联合标签融合策略相结合的主动脉CT图像分割
Multi-atlas Based Segmentation of Aortic CT Scans with Joint Label Fusion

主动脉图像自动分割技术在主动脉疾病的早期诊断、风险评估及手术治疗中发挥重要作用。本文采用了基于多图谱的医学图像分割技术，并将之与联合标签融合（Joint label fusion，JLF）策略相结合应用于3D主动脉CT图像的自动分割问题中。联合标签融合策略考虑了各个图谱之间的相互关系，能够有效抑制图谱间冗余信息的干扰，进而提高标签融合精度。本文提出了一种图谱更新算法以应对图谱数量不足的问题，在提高分割精度的同时，保持了较低的计算复杂度。在15例主动脉CT图像数据上的分割结果表明，本文方法能有效地对3D主动脉图像进行分割，与3种基于传统融合方式的图谱分割法相比，本文方法具有更高的分割精度。
Automatic aortic image segmentation plays an important role in early aortic disease diagnosis, risk evaluation and treatment planning. In this paper, we use a multi-atlas based medical image segmentation method and first combine it with a joint label fusion(JLF) strategy to segment 3D aortic CT images automatically. Joint label fusion strategy takes the correlation of atlases into consideration and the effect of redundant information of atlases can be restrained. To handle the problem of insufficient atlases, we propose an atlas archive update method which can enhance the segmentation accuracy with relatively low computational complexity. We evaluate our method by using a data set with 15 aortic subjects and comparing with three widely used label fusion techniques (majority voting, local-weighted label fusion and STAPLE). Experimental results show superior performances of our method to state-of-the-art.