Purpose

This paper addresses the detection of the clinical target volume (CTV) in transrectal ultrasound (TRUS) image-guided intraoperative for permanent prostate brachytherapy. Developing a robust and automatic method to detect the CTV on intraoperative TRUS images is clinically important to have faster and reproducible interventions that can benefit both the clinical workflow and patient health.

Methods

We present a multi-task deep learning method for an automatic prostate CTV boundary detection in intraoperative TRUS images by leveraging both the low-level and high-level (prior shape) information. Our method includes a channel-wise feature calibration strategy for low-level feature extraction and learning-based prior knowledge modeling for prostate CTV shape reconstruction. It employs CTV shape reconstruction from automatically sampled boundary surface coordinates (pseudo-landmarks) to detect the low-contrast and noisy regions across the prostate boundary, while being less biased from shadowing, inherent speckles, and artifact signals from the needle and implanted radioactive seeds.

Results

The proposed method was evaluated on a clinical database of 145 patients who underwent permanent prostate brachytherapy under TRUS guidance. Our method achieved a mean accuracy of \( 0.96 \pm 0.01\) and a mean surface distance error of \(0.10 \pm 0.06 \, \hbox {mm}\). Extensive ablation and comparison studies show that our method outperformed previous deep learning-based methods by more than 7% for the Dice similarity coefficient and 6.9 mm reduced 3D Hausdorff distance error.

Conclusion

Our study demonstrates the potential of shape model-based deep learning methods for an efficient and accurate CTV segmentation in an ultrasound-guided intervention. Moreover, learning both low-level features and prior shape knowledge with channel-wise feature calibration can significantly improve the performance of deep learning methods in medical image segmentation.

中文翻译：

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一种用于前列腺近距离放射治疗中实时术中超声图像分割的深度学习方法。

目的

本文讨论了经直肠超声 (TRUS) 图像引导术中永久前列腺近距离放射治疗中临床目标体积 (CTV) 的检测。开发一种强大且自动的方法来检测术中 TRUS 图像上的 CTV，对于具有更快和可重复的干预措施具有临床重要意义，这些干预措施可以有益于临床工作流程和患者健康。

方法

我们通过利用低级和高级（先验形状）信息，提出了一种多任务深度学习方法，用于术中 TRUS 图像中的自动前列腺 CTV 边界检测。我们的方法包括用于低级特征提取的通道特征校准策略和用于前列腺 CTV 形状重建的基于学习的先验知识建模。它采用自动采样的边界表面坐标（伪界标）的 CTV 形状重建来检测跨前列腺边界的低对比度和噪声区域，同时减少来自针和植入的放射性种子的阴影、固有斑点和伪影信号的偏差.

结果

所提出的方法在 145 名在 TRUS 指导下接受永久性前列腺近距离放射治疗的患者的临床数据库中进行了评估。我们的方法实现了\( 0.96 \pm 0.01\)的平均精度和\(0.10 \pm 0.06 \, \hbox {mm}\)的平均表面距离误差。广泛的消融和比较研究表明，我们的方法在骰子相似系数方面比以前基于深度学习的方法高出 7% 以上，并且 6.9 毫米减少了 3D Hausdorff 距离误差。

结论

我们的研究证明了基于形状模型的深度学习方法在超声引导干预中有效和准确的 CTV 分割的潜力。此外，通过通道特征校准学习低级特征和先验形状知识可以显着提高深度学习方法在医学图像分割中的性能。