Xie Xin, Huang Peng, Hu Zhihui, Fan Yuhan, Shang Jiawen, Zhang Ke, Yan Hui
Department of Radiation Oncology, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, Fuzhou, 350014, China.
Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
BMC Med Imaging. 2025 Mar 21;25(1):95. doi: 10.1186/s12880-025-01636-x.
To develop an automatic segmentation model for surgical marks, titanium clips, in target volume delineation of breast cancer radiotherapy after lumpectomy.
A two-stage deep-learning model is used to segment the titanium clips from CT image. The first network, Location Net, is designed to search the region containing all clips from CT. Then the second network, Segmentation Net, is designed to search the locations of clips from the previously detected region. Ablation studies are performed to evaluate the impact of various inputs for both networks. The two-stage deep-learning model is also compared with the other existing deep-learning methods including U-Net, V-Net and UNETR. The segmentation accuracy of these models is evaluated by three metrics: Dice Similarity Coefficient (DSC), 95% Hausdorff Distance (HD95), and Average Surface Distance (ASD).
The DSC, HD95 and ASD of the two-stage model are 0.844, 2.008 mm and 0.333 mm, while their values are 0.681, 2.494 mm and 0.785 mm for U-Net, 0.767, 2.331 mm and 0.497 mm for V-Net, 0.714, 2.660 mm and 0.772 mm for UNETR. The proposed 2-stage model achieved the best performance among the four models.
With the two-stage searching strategy the accuracy to detect titanium clips can be improved comparing to those existing deep-learning models with one-stage searching strategy. The proposed segmentation model can facilitate the delineation of tumor bed and subsequent target volume for breast cancer radiotherapy after lumpectomy.
开发一种用于保乳手术后乳腺癌放疗靶区勾画中手术标记物（钛夹）的自动分割模型。
采用两阶段深度学习模型从CT图像中分割钛夹。第一个网络，即定位网络，旨在从CT图像中搜索包含所有钛夹的区域。然后，第二个网络，即分割网络，用于从先前检测到的区域中搜索钛夹的位置。进行消融研究以评估两个网络各种输入的影响。还将两阶段深度学习模型与其他现有的深度学习方法（包括U-Net、V-Net和UNETR）进行比较。这些模型的分割准确性通过三个指标进行评估：骰子相似系数（DSC）、95%豪斯多夫距离（HD95）和平均表面距离（ASD）。
两阶段模型的DSC、HD95和ASD分别为0.844、2.008毫米和0.333毫米，而U-Net的值分别为0.681、2.494毫米和0.785毫米，V-Net的值分别为0.767、2.331毫米和0.497毫米，UNETR的值分别为0.714、2.660毫米和0.772毫米。所提出的两阶段模型在四个模型中表现最佳。
与现有的采用单阶段搜索策略的深度学习模型相比，采用两阶段搜索策略可提高检测钛夹的准确性。所提出的分割模型有助于保乳手术后乳腺癌放疗中肿瘤床和后续靶区的勾画。
