Combining Concatenated Random Forests and Active Contour for the 3D MR Images Segmentation
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摘要: 针对医学磁共振（Magnetic resonance，MR）图像三维分割中随机森林（Random forest，RF）方法难以获得具有几何约束的结果以及活动轮廓模型（Active contour model，ACM）不能自动分割发生信号混叠的组织结构的问题，提出了一种整合了级联随机森林与活动轮廓模型的磁共振图像三维分割方法.该方法首先从多模态磁共振体数据中提取图像多尺度局部鲁棒统计信息，以此驱动级联随机森林对磁共振图像进行迭代的体素分类，从而获得对组织结构的初步分割结果，进一步将此结果作为初始轮廓与形状先验，整合进一个尺度可调的活动轮廓模型中，将独立的体素分类转化为轮廓曲线演化，最终得到具有几何约束的精确分割结果.在公开数据集上的实验结果表明，本文的自动化分割方法在分割精度和鲁棒性等方面，相比其他同类方法具有较大的性能提升.Abstract: Since it is difficult for the random forest (RF) method to achieve geometrically constrained result and the active contour model (ACM) can not segment tissue structures with overlapped signals automatically, when segmenting medical magnetic resonance (MR) images in three dimensions, a combined concatenated random forests and active contour model approach is proposed in this work for the 3D segmentation of medical magnetic resonance images. The multiscale local robust statistics image information is extracted from the multimodal magnetic resonance volumetric data, and then is used to drive the random forest to perform voxel classification iteratively. As a consequence, the initial segmentation result for the tissue structure is achieved. Furthermore, the initial result is integrated into a scale scalable active contour model as the initial contour and shape prior. In this way, the independent voxel classification is reformulated as contour evolution, and the final accurate and geometrically constrained segmentation result is achieved. Experimental results on publicly available datasets demonstrate that, compared to several related methods, the proposed automated segmentation method has considerable improvement in terms of segmentation accuracy and robustness.
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Key words:
- Medical image segmentation /
- random forest (RF) /
- active contour model /
- 3D segmentation /
- shape prior
1) 本文责任编委 张道强 -
图 2 随机鲁棒统计特征学习方案二维示意图
Fig. 2 A two-dimensional illustration of the random robust statistics features learning scheme
图 3 级联架构下的体素分类流程图
Fig. 3 Overview of the voxel-wise classification within the proposed concatenated scheme
图 4 级联随机森林对多个目标图像在不同层级做出的组织概率图的估计
Fig. 4 The tissue probability maps estimated from different levels of the concatenated random forests for several target subjects
图 5 分割框架不同阶段的分割结果对比
Fig. 5 Comparison of different components in the proposed segmentation framework
图 6 低质量磁共振图像体数据三维分割结果在不同视角下的多个切片图像
Fig. 6 Multiple slices of the 3D segmentation results for low quality volumetric MR images in different views
图 7 级联随机森林不同参数对分割结果的影响
Fig. 7 Impact of different parameters in the concatenated random forests
表 1 不同分割方法在左心房数据集上的DC系数对比
Table 1 Dice coefficients (DC) of different methods on the left atrial dataset
数据集 Proposed1 Proposed2 ACM RF Competitive contours ${\rm{M^3AS}}$ CRF CRF-AC 训练集 0.78$\, \pm\, $0.006 0.94$\, \pm\, $0.04 0.72$\, \pm\, $0.20 0.69$\, \pm\, $0.12 --- --- --- --- 测试集 0.74$\, \pm\, $0.07 0.90$\, \pm\, $0.03 0.71$\, \pm\, $0.20 0.63$\, \pm\, $0.14 0.92 0.88$\, \pm\, $0.06 0.74$\, \pm\, $0.07 0.93$\, \pm\, $0.05 
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表 2 不同方法在BRATS15测试集上的对比
Table 2 Comparison of different methods on the BRATS15 test set
Methods DC PPV Sensitivity Complete Core Enh Complete Core Enh Complete Core Enh Proposed 2 0.87 0.74 0.64 0.85 0.81 0.61 0.91 0.74 0.73 ${\rm{I_{NPUT}C_{ASCADE}CNN}}$ 0.88 0.79 0.73 --- --- --- 0.87 0.79 0.80 CNNs 0.78 0.65 0.75 --- --- --- --- --- FCNNs-CRFs 0.84 0.73 0.62 0.89 0.76 0.63 0.82 0.76 0.67 DeepMedic 0.85 0.67 0.63 0.85 0.85 0.63 0.88 0.61 0.66
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