Feature-based automated segmentation of ablation zones by fuzzy c-mean clustering during low-dose computed tomography.
Feature-based automated segmentation of ablation zones by fuzzy c-mean clustering during low-dose computed tomography.
复制标题
DOI：
10.1002/mp.14623
复制
发表时间：
2021-03
期刊：
影响因子：
3.8
通讯作者：
Brace CL
中科院分区：
文献类型：
--
作者：
Wu PH;Bedoya M;White J;Brace CL
术中监测和术后随访是成功消融治疗的必要条件。能够准确评估消融几何形状的成像技术有利于监测和评估治疗。在这项研究中，我们开发了一种自动消融分割技术，用于连续低剂量，噪声消融计算机断层扫描（CT）或对比增强CT（CECT）。在微波消融正常猪肝期间采集低剂量、有噪声的颞叶CT和CECT体积（4个使用非造影CT，8个使用CECT）。高度约束的反投影（HYPR）处理被用来恢复消融区的信息受到低剂量噪声。利用一阶统计量特征和归一化分数布朗特征（NBF），通过模糊c均值聚类对消融区进行分割。聚类后，通过循环形态学处理细化分割的消融区。自动和手动分割与大体病理学进行了比较，具有Dice系数（形态相似性），而横截面尺寸通过百分比差异进行比较。消融区的自动和手动分割与大体病理学非常相似（骰子系数：自动。-路径= 0.84 ± 0.02;手动- Path. = 0.76 ± 0.03，P = 0.11）。自动分割与大体病理比较时，消融面积、大直径和小直径的差异分别为17.9 ± 3.2%、11.1 ± 3.2%和16.2 ± 3.4%，均低于32.9 ± 16.8%的差异，手动分割与大体病理学比较时为13.0 ± 9.8%和21.8 ± 5.8%。当消融面积小于15 cm 2时，手动分割倾向于高估大体病理学，但当消融区域大于20 cm 2时，自动分割倾向于低估大体病理学。模糊c均值聚类可用于辅助消融区域的自动分割，而无需先验信息或用户输入，使得连续CT/CECT更有可能在术中评估治疗。
Intra-procedural monitoring and post-procedural follow-up is necessary for a successful ablation treatment. An imaging technique which can assess the ablation geometry accurately is beneficial to monitor and evaluate treatment. In this study, we developed an automated ablation segmentation technique for serial low-dose, noisy ablation computed tomography (CT) or contrast-enhanced CT (CECT). Low-dose, noisy temporal CT and CECT volumes were acquired during microwave ablation on normal porcine liver (four with non-contrast CT and eight with CECT). Highly constrained backprojection (HYPR) processing was used to recover ablation zone information compromised by low-dose noise. First-order statistic features and normalized fractional Brownian features (NBF) were used to segment ablation zones by fuzzy c-mean clustering. After clustering, the segmented ablation zone was refined by cyclic morphological processing. Automatic and manual segmentations were compared to gross pathology with Dice’s coefficient (morphological similarity), while cross-sectional dimensions were compared by percent difference. Automatic and manual segmentations of the ablation zone were very similar to gross pathology (Dice Coefficients: Auto.-Path. = 0.84 ± 0.02; Manu.-Path. = 0.76 ± 0.03, P = 0.11). The differences in ablation area, major diameter and minor diameter were 17.9 ± 3.2%, 11.1 ± 3.2% and 16.2 ± 3.4%, respectively, when comparing automatic segmentation to gross pathology, which were lower than the differences of 32.9 ± 16.8%, 13.0 ± 9.8% and 21.8 ± 5.8% when comparing manual segmentation to gross pathology. Manual segmentations tended to overestimate gross pathology when ablation area was less than 15 cm2, but the automated segmentation tended to underestimate gross pathology when ablation zone is larger than 20 cm2. Fuzzy c-means clustering may be used to aid automatic segmentation of ablation zones without prior information or user input, making serial CT/CECT has more potential to assess treatments intra-procedurally.
登录
查看更多内容
DOI：
10.1053/j.tvir.2013.08.002
发表时间：
2013-12
影响因子：
1.7
作者：
Knavel EM;Brace CL
通讯作者：
Brace CL
DOI：
10.1109/iembs.2009.5333500
发表时间：
2009
期刊：
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
影响因子：
--
作者：
Brace CL;Mistretta CA;Hinshaw JL;Lee FT Jr
通讯作者：
Lee FT Jr
影响因子：
5
作者：
Chopra, S;Dodd, GD;Rhim, H
通讯作者：
Rhim, H
影响因子：
--
作者：
Brace CL
通讯作者：
Brace CL
影响因子：
40.5
作者：
Leung, Angela M.;Braverman, Lewis E.
通讯作者：
Braverman, Lewis E.