Le Quoc Anh, Pham Xuan Loc, van Walsum Theo, Dao Viet Hang, Le Tuan Linh, Franklin Daniel, Moelker Adriaan, Le Vu Ha, Trung Nguyen Linh, Luu Manh Ha
AVITECH, VNU University of Engineering and Technology, Hanoi, Vietnam.
FET, VNU University of Engineering and Technology, Hanoi, Vietnam.
Med Phys. 2024 Dec;51(12):8882-8899. doi: 10.1002/mp.17373. Epub 2024 Sep 9.
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.
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.
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 ( = 22, = 145; 62 patients in total) and then further tested using an external benchmark dataset ( = 12; 10 patients).
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 ( = 0.55, -test).
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.
在对比增强计算机断层扫描（CECT）图像中进行消融区分割，能够对肝脏病变消融治疗的成功与否进行定量评估。然而，在CT图像中实现全自动肝脏消融区分割仍具有挑战性，比如准确率低，以及对错误区域进行人工细化耗时较长。
因此，在本研究中，我们开发了一种半自动技术，以解决现存的缺点，并提高CT图像中肝脏消融区分割的准确性。
我们的方法结合了基于卷积神经网络（CNN）的自动分割方法和基于交互式CNN的分割方法。首先，应用自动分割对整个CT图像进行粗略的消融区分割。然后，医学专家对分割结果进行视觉验证。如果在粗略分割中存在错误，可以通过基于交互式CNN的分割方法对每个切片进行局部校正。使用两个介入后CECT图像的内部数据集（ = 22， = 145；共62例患者）对模型进行训练，并对所提出的方法进行评估，然后使用外部基准数据集（ = 12；10例患者）进行进一步测试。
为了评估所提出方法的准确性，我们使用了骰子相似系数（DSC）、平均对称表面距离（ASSD）、豪斯多夫距离（HD）和体积差异（VD）。定量评估结果表明，所提出的方法在内部数据集上的平均DSC、ASSD、HD和VD分数分别为94.0%、0.4毫米、8.4毫米、0.02，在基准数据集上分别为87.8%、0.9毫米、9.5毫米和 -0.03。我们还将所提出方法的性能与五种著名的分割方法进行了比较；所提出的半自动方法在消融分割准确性方面达到了当前的先进性能，平均校正分割需要2分钟。此外，我们发现所提出方法在基准数据集上的准确性与医学专家的手动分割相当（ = 0.55，t检验）。
所提出的基于CNN的半自动分割方法可用于有效分割消融区，提高CECT在评估治疗成功方面的价值。为实现可重复性，经过训练的模型、源代码和演示工具可在https://github.com/lqanh11/Interactive_AblationZone_Segmentation上公开获取。
