Li Jun, Choi Wookjin, Anne Rani
Department of Radiation Oncology, Thomas Jefferson University, Philadelphia, PA, USA.
Technol Cancer Res Treat. 2025 Jan-Dec;24:15330338251327081. doi: 10.1177/15330338251327081. Epub 2025 Mar 28.
The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A deep learning (DL)-based liver segmentation model using the U-Net3D architecture was built. Auto-segmentation of the liver was tested in CT images of SIRT patients. DL auto-segmented liver contours were evaluated against physician manually-delineated contours. Dice similarity coefficient (DSC) and mean distance to agreement (MDA) were calculated. The DL-model-generated contours were compared with the contours generated using an Atlas-based method. Ratio of volume (RV, the ratio of DL-model auto-segmented liver volume to manually-delineated liver volume), and ratio of activity (RA, the ratio of Y-90 activity calculated using a DL-model auto-segmented liver volume to Y-90 activity calculated using a manually-delineated liver volume), were assessed. Compared with the contours generated with the Atlas method, the contours generated with the DL model had better agreement with the manually-delineated contours, which had larger DSCs (average: 0.94 ± 0.01 vs 0.83 ± 0.10) and smaller MDAs (average: 1.8 ± 0.4 mm vs 7.1 ± 5.1 mm). The average RV and average RA calculated using the DL-model-generated volumes are 0.99 ± 0.03 and 1.00 ± 0.00, respectively. The DL segmentation model was able to identify and segment livers in the CT images and provide reliable results. It outperformed the Atlas method. The model can be applied for SIRT procedures.
目的是评估一种基于深度学习的自动分割方法，用于钇-90 选择性内放射治疗（SIRT）中的肝脏轮廓描绘。构建了一种基于深度学习（DL）的使用 U-Net3D 架构的肝脏分割模型。在 SIRT 患者的 CT 图像中对肝脏自动分割进行了测试。将 DL 自动分割的肝脏轮廓与医生手动描绘的轮廓进行评估。计算了骰子相似系数（DSC）和平均一致距离（MDA）。将 DL 模型生成的轮廓与使用基于图谱的方法生成的轮廓进行比较。评估了体积比（RV，DL 模型自动分割的肝脏体积与手动描绘的肝脏体积之比）和活度比（RA，使用 DL 模型自动分割的肝脏体积计算的钇-90 活度与使用手动描绘的肝脏体积计算的钇-90 活度之比）。与使用图谱法生成的轮廓相比，DL 模型生成的轮廓与手动描绘的轮廓具有更好的一致性，DSC 更大（平均值：0.94±0.01 对 0.83±0.10），MDA 更小（平均值：1.8±0.4 mm 对 7.1±5.1 mm）。使用 DL 模型生成的体积计算的平均 RV 和平均 RA 分别为 0.99±0.03 和 1.00±0.00。DL 分割模型能够在 CT 图像中识别和分割肝脏并提供可靠结果。它优于图谱法。该模型可应用于 SIRT 程序。
