Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and normal breast tissue is relatively low in CT images. In this study, we propose to mimic the marker-guidance procedure in manual target delineation. We developed a saliency-based deep learning segmentation (SDL-Seg) algorithm for accurate TBV segmentation in post-operative breast irradiation. The SDL-Seg algorithm incorporates saliency information in the form of markers' location cues into a U-Net model. The design forces the model to encode the location-related features, which underscores regions with high saliency levels and suppresses low saliency regions. The saliency maps were generated by identifying markers on CT images. Markers' locations were then converted to probability maps using a distance-transformation coupled with a Gaussian filter. Subsequently, the CT images and the corresponding saliency maps formed a multi-channel input for the SDL-Seg network. Our in-house dataset was comprised of 145 prone CT images from 29 post-operative breast cancer patients, who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod. The performance of the proposed method was compared against basic U-Net. Our model achieved mean (standard deviation) of 76.4 %, 6.76 mm, and 1.9 mm for DSC, HD95, and ASD respectively on the test set with computation time of below 11 seconds per one CT volume. SDL-Seg showed superior performance relative to basic U-Net for all the evaluation metrics while preserving low computation cost. The findings demonstrate that SDL-Seg is a promising approach for improving the efficiency and accuracy of the on-line treatment planning procedure of PBI, such as GammaPod based PBI.

中文翻译：

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显着性指导的深度学习网络，可在术后乳房照射中自动确定肿瘤床的体积

有效，可靠和可重复的目标体积勾画是有效规划乳房放疗的关键步骤。但是，由于CT图像中的肿瘤床体积（TBV）与正常乳腺组织之间的对比相对较低，因此，术后乳房目标的界定是具有挑战性的。在这项研究中，我们建议在手动目标描绘中模仿标记引导程序。我们开发了基于显着性的深度学习细分（SDL-Seg）算法，用于在术后乳房照射中进行精确的TBV细分。SDL-Seg算法将标记位置提示形式的显着性信息合并到U-Net模型中。设计迫使模型对与位置相关的特征进行编码，从而强调具有高显着性水平的区域并抑制具有低显着性的区域。显着性图是通过在CT图像上标识标记生成的。然后使用距离变换和高斯滤波器将标记的位置转换为概率图。随后，CT图像和相应的显着性图形成了SDL-Seg网络的多通道输入。我们的内部数据集包含来自29位术后乳腺癌患者的145张俯卧CT图像，这些患者在GammaPod上接受了5级局部乳房照射（PBI）方案。将该方法的性能与基本U-Net进行了比较。我们的模型在测试集上的DSC，HD95和ASD的平均（标准偏差）分别为76.4％，6.76 mm和1.9 mm，每1个CT体积的计算时间不到11秒。SDL-Seg在所有评估指标上均表现出优于基本U-Net的性能，同时保持了较低的计算成本。研究结果表明，SDL-Seg是提高PBI在线治疗计划程序（例如基于GammaPod的PBI）的效率和准确性的一种有前途的方法。