Gubern-Mérida Albert, Kallenberg Michiel, Martí Robert, Karssemeijer Nico
University of Girona, Spain.
Med Image Comput Comput Assist Interv. 2012;15(Pt 2):371-8. doi: 10.1007/978-3-642-33418-4_46.
Pectoral muscle segmentation is an important step in automatic breast image analysis methods and crucial for multi-modal image registration. In breast MRI, accurate delineation of the pectoral is important for volumetric breast density estimation and for pharmacokinetic analysis of dynamic contrast enhancement. In this paper we propose and study the performance of atlas-based segmentation methods evaluating two fully automatic breast MRI dedicated strategies on a set of 27 manually segmented MR volumes. One uses a probabilistic model and the other is a multi-atlas registration based approach. The multi-atlas approach performed slightly better, with an average Dice coefficient (DSC) of 0.74, while with the much faster probabilistic method a DSC of 0.72 was obtained.
胸肌分割是自动乳腺图像分析方法中的重要步骤,对于多模态图像配准至关重要。在乳腺磁共振成像(MRI)中,准确勾勒胸肌对于乳腺体积密度估计和动态对比增强的药代动力学分析很重要。在本文中,我们提出并研究了基于图谱的分割方法的性能,在一组27个手动分割的MR体积上评估了两种全自动乳腺MRI专用策略。一种使用概率模型,另一种是基于多图谱配准的方法。多图谱方法表现稍好,平均骰子系数(DSC)为0.74,而速度快得多的概率方法获得的DSC为0.72。