Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis.
Algorithm-based method for detection of blood vessels in breast MRI for development of computer-aided diagnosis.
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DOI：
10.1002/jmri.21915
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发表时间：
2009-10
影响因子：
4.4
通讯作者：
Su, Min-Ying
中科院分区：
文献类型：
--
作者：
Lin, Muqing;Chen, Jeon-Hor;Nie, Ke;Chang, Daniel;Nalcioglu, Orhan;Su, Min-Ying
关键词：
开发一种基于计算机的算法来检测乳腺动态对比增强(DCE)MRI中出现的血管，并评估其在减少计算机辅助诊断(CAD)系统标记为可疑恶性肿瘤的血管像素数量方面的改进。对34例患者进行了分析。该算法采用基于小波变换和海森矩阵的滤波器组来检测二维最大强度投影(MIP)上的血管等线性结构。然后，基于高于阈值的增强像素的连通性来检测垂直于MIP平面运行的血管。根据其形态特征确定和排除非血管强化，包括那些显示散在小段强化或结节状或平面团的强化。检测到的血管首先通过变薄转换为血管骨架，然后与放射科医生手动绘制的血管轨迹进行比较。在评估算法在识别血管组织方面的性能时，正确检测率是指由算法和放射科医生两者识别的像素，而误检率是指仅由算法识别的像素，而漏检率是指仅由放射科医生识别的像素。34例分析病例中，正确率为85.6%(平均84.9%±7.8%)，误检率为13.1%(平均15.1%±7.8%)，漏检率为19.2%(平均21.3%±12.8%)。当检测到的血管被排除在CAD系统的热点颜色编码中时，可以减少2.6%~68.6%的热点像素的血管标记(平均16.6%±15.9%)。这种基于计算机算法的方法可以检测出大多数大血管，为减少DCE-MRI CAD系统中可疑血管像素的标记提供了有效的手段。这种算法可能会改善放射科医生使用CAD进行图像显示的工作流程，但对于提供诊断印象的自动化CAD的开发将特别有用。
To develop a computer-based algorithm for detecting blood vessels that appear in breast dynamic contrast enhanced (DCE) MRI, and to evaluate the improvement in reducing the number of vascular pixels that are labeled by computer-aided diagnosis (CAD) systems as being suspicious of malignancy. The analysis was performed in 34 cases. The algorithm applied a filter bank based on wavelet transform and the Hessian matrix to detect linear structures as blood vessels on a 2-dimensional maximum intensity projection (MIP). The vessels running perpendicular to the MIP plane were then detected based on the connectivity of enhanced pixels above a threshold. The non-vessel enhancements were determined and excluded based their morphological properties, including those showing scattered small segment enhancements or nodular or planar clusters. The detected vessels were first converted to a vasculature skeleton by thinning and subsequently compared to the vascular track manually drawn by a radiologist. When evaluating the performance of the algorithm in identifying vascular tissue, the correct-detection rate refers to pixels identified by both the algorithm and radiologist, while the incorrect-detection rate refers to pixels identified by only the algorithm, and the missed-detection rate refers to pixels identified only by the radiologist. From 34 analyzed cases the median correct-detection rate was 85.6% (mean 84.9% ± 7.8%), the incorrect-detection rate was 13.1% (mean 15.1% ± 7.8%), and the missed-detection rate was 19.2% (mean 21.3% ± 12.8%). When detected vessels were excluded in the hot spot color-coding of the CAD system, they could reduce the labeling of vascular vessels in 2.6% to 68.6% of hot spot pixels (mean 16.6% ± 15.9%). The computer-algorithm based method can detect most large vessels, and provide an effective means in reducing the labeling of vascular pixels as suspicious on DCE-MRI CAD system. This algorithm may improve the workflow of radiologists using CAD for image display, but will be particularly useful for development of automated CAD that gives diagnostic impression.
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