PDF(Pubmed)
Abstract：
BACKGROUND: The Otsu method and the Chan-Vese model are two methods proven to perform well in determining volumes of different organs and specific tissue fractions. This study aimed to compare the performance of the two methods regarding segmentation of active thyroid gland volumes, reflecting different clinical settings by varying the parameters: gland size, gland activity concentration, background activity concentration and gland activity concentration heterogeneity.
METHODS: A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu\'s threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient.
RESULTS: Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]).
CONCLUSIONS: The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.
METHODS: A computed tomography was performed on three playdough thyroid phantoms with volumes 20, 35 and 50 ml. The image data were separated into playdough and water based on Hounsfield values. Sixty single photon emission computed tomography (SPECT) projections were simulated by Monte Carlo method with isotope Technetium-99 m ([Formula: see text]Tc). Linear combinations of SPECT images were made, generating 12 different combinations of volume and background: each with both homogeneous thyroid activity concentration and three hotspots of different relative activity concentrations (48 SPECT images in total). The relative background levels chosen were 5 %, 10 %, 15 % and 20 % of the phantom activity concentration and the hotspot activities were 100 % (homogeneous case) 150 %, 200 % and 250 %. Poisson noise, (coefficient of variation of 0.8 at a 20 % background level, scattering excluded), was added before reconstruction was done with the Monte Carlo-based SPECT reconstruction algorithm Sahlgrenska Academy reconstruction code (SARec). Two different segmentation algorithms were applied: Otsu\'s threshold selection method and an adaptation of the Chan-Vese model for active contours without edges; the results were evaluated concerning relative volume, mean absolute error and standard deviation per thyroid volume, as well as dice similarity coefficient.
RESULTS: Both methods segment the images well and deviate similarly from the true volumes. They seem to slightly overestimate small volumes and underestimate large ones. Different background levels affect the two methods similarly as well. However, the Chan-Vese model deviates less and paired t-testing showed significant difference between distributions of dice similarity coefficients (p-value [Formula: see text]).
CONCLUSIONS: The investigations indicate that the Chan-Vese model performs better and is slightly more robust, while being more challenging to implement and use clinically. There is a trade-off between performance and user-friendliness.
摘要：
背景：Otsu方法和Chan-Vese模型是两种被证明在确定不同器官和特定组织分数的体积方面表现良好的方法。本研究旨在比较两种方法的性能有关活动甲状腺体积的分割，通过改变参数来反映不同的临床设置：腺体大小，腺体活动浓度，背景活动浓度和腺体活动浓度异质性。
方法：对三个体积分别为20、35和50ml的小面团甲状腺体模进行了计算机断层扫描。根据Hounsfield值将图像数据分为面团和水。通过蒙特卡洛方法使用同位素Tech-99m（[公式：参见文本]Tc）模拟了60个单光子发射计算机断层扫描（SPECT）投影。SPECT图像的线性组合，生成12种不同的体积和背景组合：每种组合均具有均匀的甲状腺活动浓度和不同相对活动浓度的三个热点（总共48张SPECT图像）。选择的相对背景水平为5%，10%,15％和20％的体模活动浓度和热点活动分别为100％（均一情况）150％，200%和250%。泊松噪声,(在20%的背景水平下变异系数为0.8，排除散射)，在使用基于蒙特卡洛的SPECT重建算法Sahlgrenska学院重建代码（SARec）进行重建之前添加。应用了两种不同的分割算法：Otsu的阈值选择方法和Chan-Vese模型对没有边缘的活动轮廓的适应；评估了有关相对体积的结果，每个甲状腺体积的平均绝对误差和标准偏差，以及骰子相似系数。
结果：两种方法都很好地分割图像，并类似地偏离真实体积。他们似乎稍微高估了小体积，而低估了大体积。不同的背景水平也类似地影响两种方法。然而,Chan-Vese模型偏差较小，配对t检验显示骰子相似系数分布之间存在显着差异（p值[公式：见正文]）。
结论：调查表明，Chan-Vese模型表现更好，并且更加稳健，同时在临床上实施和使用更具挑战性。在性能和用户友好性之间存在权衡。
方法：对三个体积分别为20、35和50ml的小面团甲状腺体模进行了计算机断层扫描。根据Hounsfield值将图像数据分为面团和水。通过蒙特卡洛方法使用同位素Tech-99m（[公式：参见文本]Tc）模拟了60个单光子发射计算机断层扫描（SPECT）投影。SPECT图像的线性组合，生成12种不同的体积和背景组合：每种组合均具有均匀的甲状腺活动浓度和不同相对活动浓度的三个热点（总共48张SPECT图像）。选择的相对背景水平为5%，10%,15％和20％的体模活动浓度和热点活动分别为100％（均一情况）150％，200%和250%。泊松噪声,(在20%的背景水平下变异系数为0.8，排除散射)，在使用基于蒙特卡洛的SPECT重建算法Sahlgrenska学院重建代码（SARec）进行重建之前添加。应用了两种不同的分割算法：Otsu的阈值选择方法和Chan-Vese模型对没有边缘的活动轮廓的适应；评估了有关相对体积的结果，每个甲状腺体积的平均绝对误差和标准偏差，以及骰子相似系数。
结果：两种方法都很好地分割图像，并类似地偏离真实体积。他们似乎稍微高估了小体积，而低估了大体积。不同的背景水平也类似地影响两种方法。然而,Chan-Vese模型偏差较小，配对t检验显示骰子相似系数分布之间存在显着差异（p值[公式：见正文]）。
结论：调查表明，Chan-Vese模型表现更好，并且更加稳健，同时在临床上实施和使用更具挑战性。在性能和用户友好性之间存在权衡。
