背景:对比增强计算机断层扫描(CECT)图像中的消融区分割可以定量评估肝脏病变消融的治疗成功率。然而,CT图像中的全自动肝脏消融区分割仍然具有挑战性,例如不正确区域的低精度和耗时的手动细化。
目标:因此,在这项研究中,我们开发了一种半自动技术来解决剩余的缺点,并提高CT图像中肝脏消融区分割的准确性。
方法:我们的方法结合了基于CNN的自动分割方法和基于CNN的交互式分割方法。首先,自动分割应用于整个CT图像的粗消融区分割。然后,人类专家在视觉上验证分割结果。如果粗分割有错误,可以通过基于CNN的交互式分割方法对每个切片执行局部校正。对模型进行了训练,并使用两个介入后CECT图像的内部数据集(n1$n_{1}$=22,n2$n_{2}$=145;总共62名患者)对所提出的方法进行了评估,然后使用外部基准数据集(n3$n_{3}$=12;10名患者)进行了进一步测试。
结果:为了评估拟议方法的准确性,我们使用骰子相似系数(DSC),平均对称表面距离(ASSD),Hausdorff距离(HD),和体积差(VD)。定量评价结果表明,该方法得到的平均DSC,ASSD,HD,VD评分为94.0%,0.4mm,8.4mm,0.02,分别在内部数据集上,87.8%,0.9mm,9.5mm,和-0.03,分别在基准数据集上。我们还将所提出的方法的性能与五种著名的分割方法的性能进行了比较;所提出的半自动方法在消融分割精度上实现了最先进的性能,平均而言,需要2分钟来校正分割。此外,我们发现,该方法在基准数据集上的准确性与人类专家手动分割的准确性相当(p$p$=0.55,t$t$-test)。
结论:提出的基于CNN的半自动分割方法可用于有效分割消融区,增加CECT对评估治疗成功的价值。为了重现性,经过训练的模型,源代码,和演示工具可在https://github.com/lqanh11/Interactive_AblationZone_Segmentation上公开获得。
BACKGROUND: Ablation zone segmentation in contrast-enhanced computed tomography (CECT) images enables the quantitative assessment of treatment success in the ablation of liver lesions. However, fully automatic liver ablation zone segmentation in CT images still remains challenging, such as low accuracy and time-consuming manual refinement of the incorrect regions.
OBJECTIVE: Therefore, in this study, we developed a semi-automatic technique to address the remaining drawbacks and improve the accuracy of the liver ablation zone segmentation in the CT images.
METHODS: Our approach uses a combination of a CNN-based automatic segmentation method and an interactive CNN-based segmentation method. First, automatic segmentation is applied for coarse ablation zone segmentation in the whole CT image. Human experts then visually validate the segmentation results. If there are errors in the coarse segmentation, local corrections can be performed on each slice via an interactive CNN-based segmentation method. The models were trained and the proposed method was evaluated using two internal datasets of post-interventional CECT images ( n 1 $n_{1}$ = 22, n 2 $n_{2}$ = 145; 62 patients in total) and then further tested using an external benchmark dataset ( n 3 $n_{3}$ = 12; 10 patients).
RESULTS: To evaluate the accuracy of the proposed approach, we used Dice similarity coefficient (DSC), average symmetric surface distance (ASSD), Hausdorff distance (HD), and volume difference (VD). The quantitative evaluation results show that the proposed approach obtained mean DSC, ASSD, HD, and VD scores of 94.0%, 0.4 mm, 8.4 mm, 0.02, respectively, on the internal dataset, and 87.8%, 0.9 mm, 9.5 mm, and -0.03, respectively, on the benchmark dataset. We also compared the performance of the proposed approach to that of five well-known segmentation methods; the proposed semi-automatic method achieved state-of-the-art performance on ablation segmentation accuracy, and on average, 2 min are required to correct the segmentation. Furthermore, we found that the accuracy of the proposed method on the benchmark dataset is comparable to that of manual segmentation by human experts ( p $p$ = 0.55, t $t$ -test).
CONCLUSIONS: The proposed semi-automatic CNN-based segmentation method can be used to effectively segment the ablation zones, increasing the value of CECT for an assessment of treatment success. For reproducibility, the trained models, source code, and demonstration tool are publicly available at https://github.com/lqanh11/Interactive_AblationZone_Segmentation.