Sakly Houneida, Said Mourad, Tagina Moncef
COSMOS Laboratory, National School of Computer Sciences (ENSI), University of Manouba, Tunisia.
Radiology and Medical Imaging Unit, International Center Carthage Medical, Tourist Area "JINEN EL OUEST" 5000 Monastir, Tunisia.
Heliyon. 2020 Nov 20;6(11):e05547. doi: 10.1016/j.heliyon.2020.e05547. eCollection 2020 Nov.
A comparative study has been depicted between the contour and topographic watershed segmentation approach for short-axis 5D cardiac sequences with MRI for medical decision. The fifth dimension has been defined as the excitation of pixels based on the gray scale around the myocardium without consideration of the morphological structure of the heart in 3D and fourth dimension (time). Three patients were performed the first is healthy, the second has a genetic disease, and the third had a heart failure syndrome for a dimension ROI = 150mm, average age is 54 years old, and mean of weight = 86 kg. A contouring and watershed segmentation algorithm for a sample of 63 Cine Fiesta MRI sequences for short-axis cuts with Matlab and its in-box toolbox complements was implemented. For a healthy patient 13.4% tolerance rate for the estimation of the stroke fraction, 6.4% for a patient with genetic disease, 8.7% error rate for a patient with heart failure symptom. The results show that the regurgitation fraction by the contour approach for a patient case with symptom of the presence of a genetic disease is 0.0335% for an aortic valve, 0.248% for a mitral valve, an error rate 0.16% for estimating this parameter for the aortic orifice with the watershed segmentation approach. In return, for a patient with suspected heart failure (stenosis or regurgitation) the regurgitation fraction is estimated by 0% for aortic valve, 1.49 e % for a mitral valve, an error rate 11.76% compared to the watershed segmentation approach. The results are validated clinically. The Optimization of the topographic watershed approach with mutual information was simulated for the extraction of measurements (ejection fraction, regurgitation rate) within the left ventricle for three patient types (healthy, genetic pathology and heart failure). The results are considered interesting compared to the clinical assessment.
为辅助医学决策,开展了一项针对短轴5D心脏序列的轮廓分割与地形流域分割方法的比较研究,该序列由MRI获取。第五维被定义为基于心肌周围灰度对像素的激发,而不考虑心脏在三维和第四维(时间)上的形态结构。对三名患者进行了研究,第一名健康,第二名患有遗传疾病,第三名患有心力衰竭综合征,感兴趣区域(ROI)维度为150mm,平均年龄54岁,平均体重86kg。利用Matlab及其内置工具箱,针对63个短轴切面的电影稳态自由进动(Cine Fiesta)MRI序列样本,实现了轮廓分割和流域分割算法。对于健康患者,每搏输出量估计的容差率为13.4%,患有遗传疾病的患者为6.4%,有心力衰竭症状的患者错误率为8.7%。结果表明,对于患有遗传疾病症状的患者病例,采用轮廓法时主动脉瓣反流分数为0.0335%,二尖瓣为0.248%,采用流域分割法估计主动脉口该参数的错误率为0.16%。反过来,对于疑似心力衰竭(狭窄或反流)的患者,采用轮廓法时主动脉瓣反流分数估计为0%,二尖瓣为1.49e%,与流域分割法相比错误率为11.76%。结果得到了临床验证。针对三种患者类型(健康、遗传病理和心力衰竭),模拟了利用互信息对地形流域法进行优化,以提取左心室内的测量值(射血分数、反流率)。与临床评估相比,结果令人关注。