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Discrete light sheet microscopic segmentation of left ventricle using morphological tuning and active contours
Microscopy Research and Technique ( IF 2.5 ) Pub Date : 2021-08-21 , DOI: 10.1002/jemt.23906
Mehreen Irshad 1 , Muhammad Sharif 1 , Mussarat Yasmin 1 , Amjad Rehman 2 , Muhammad Attique Khan 3
Affiliation  

Left ventricular segmentation using cardiovascular MR scan is required for the diagnosis and further cure of cardiac diseases. Automatic systems for left ventricle segmentation are being studied for attaining more accurate results in a shorter period of time. A novel algorithm introducing discrete segmentation of left ventricle achieves an independent processing of images swiftly. The workflow consists of four segments; first, automated localization is performed on the MR image. Second, performing preprocessing intimately improves and enhances the quality of image using mean contrast adjustment. Central segmentation of endocardium and epicardium layers includes novel MTAC (Morphological tuning using active contours) segmentation algorithm that provides a perfect combination of active contours and morphological tuning to bring an adequate and desirable segmentation. The prospective snake model is a restrained progression, which takes iterations for an impulse throughout the left ventricle contours. At the end, contrast based refining overcomes minor edge problems for both outer and inner boundaries. Proposed algorithm is evaluated via Sunnybrook cardiac MR images by producing an overall average perpendicular distance 2.45 mm, an average dice matrix (endo: 91.3%; epi: 92.16%) and 91.7% dice matrix of overall endocardium and epicardium contours from ground truth contours.

中文翻译:

使用形态调整和活动轮廓的左心室离散光片显微分割

心脏疾病的诊断和进一步治疗需要使用心血管 MR 扫描进行左心室分割。正在研究用于左心室分割的自动系统,以便在更短的时间内获得更准确的结果。一种引入左心室离散分割的新算法快速实现了图像的独立处理。工作流由四个部分组成;首先,对 MR 图像执行自动定位。其次,使用平均对比度调整进行预处理可以密切改善和增强图像质量。心内膜和心外膜层的中央分割包括新颖的 MTAC(使用活动轮廓的形态调整)分割算法,该算法提供活动轮廓和形态调整的完美组合,以带来充分和理想的分割。预期的蛇模型是一个受约束的进程,它需要迭代整个左心室轮廓的脉冲。最后,基于对比度的细化克服了外边界和内边界的小边缘问题。提议的算法通过 Sunnybrook 心脏 MR 图像进行评估,方法是生成整体平均垂直距离 2.45 毫米、平均骰子矩阵(内膜:91.3%;外延:92.16%)和 91.7% 的整体心内膜和心外膜轮廓从地面的骰子矩阵真相轮廓。预期的蛇模型是一个受约束的进程,它需要迭代整个左心室轮廓的脉冲。最后,基于对比度的细化克服了外边界和内边界的小边缘问题。提议的算法通过 Sunnybrook 心脏 MR 图像进行评估,方法是生成整体平均垂直距离 2.45 毫米、平均骰子矩阵(内膜:91.3%;外延:92.16%)和 91.7% 的整体心内膜和心外膜轮廓从地面的骰子矩阵真相轮廓。预期的蛇模型是一个受约束的进程,它需要迭代整个左心室轮廓的脉冲。最后,基于对比度的细化克服了外边界和内边界的小边缘问题。提议的算法通过 Sunnybrook 心脏 MR 图像进行评估,方法是生成整体平均垂直距离 2.45 毫米、平均骰子矩阵(内膜:91.3%;外延:92.16%)和 91.7% 的整体心内膜和心外膜轮廓从地面的骰子矩阵真相轮廓。
更新日期:2021-08-21
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