Purpose

Twin-to-twin transfusion syndrome (TTTS) is a serious condition that occurs in about 10–15% of monochorionic twin pregnancies. In most instances, the blood flow is unevenly distributed throughout the placenta anastomoses leading to the death of both fetuses if no surgical procedure is performed. Fetoscopic laser coagulation is the optimal therapy to considerably improve co-twin prognosis by clogging the abnormal anastomoses. Notwithstanding progress in recent years, TTTS surgery is highly risky. Computer-assisted planning of the intervention can thus improve the outcome.

Methods

In this work, we implement a GPU-accelerated random walker (RW) algorithm to detect the placenta, both umbilical cord insertions and the placental vasculature from Doppler ultrasound (US). Placenta and background seeds are manually initialized in 10–20 slices (out of 245). Vessels are automatically initialized in the same slices by means of Otsu thresholding. The RW finds the boundaries of the placenta and reconstructs the vasculature.

Results

We evaluate our semiautomatic method in 5 monochorionic and 24 singleton pregnancies. Although satisfactory performance is achieved on placenta segmentation (Dice ≥ 84.0%), some vascular connections are still neglected due to the presence of US reverberation artifacts (Dice ≥ 56.9%). We also compared inter-user variability and obtained Dice coefficients of ≥ 76.8% and ≥ 97.42% for placenta and vasculature, respectively. After a 3-min manual initialization, our GPU approach speeds the computation 10.6 times compared to the CPU.

Conclusions

Our semiautomatic method provides a near real-time user experience and requires short training without compromising the segmentation accuracy. A powerful approach is thus presented to rapidly plan the fetoscope insertion point ahead of TTTS surgery.

中文翻译：

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在多普勒超声中分割胎盘及其血管树，用于胎儿手术计划。

目的

双胎输血综合征 (TTTS) 是一种严重的疾病，发生在大约 10-15% 的单绒毛膜双胎妊娠中。在大多数情况下，如果不进行外科手术，整个胎盘吻合处的血流分布不均会导致两个胎儿死亡。胎儿镜激光凝固术是通过堵塞异常吻合来显着改善双胞胎预后的最佳疗法。尽管近年来取得了进展，但 TTTS 手术风险很高。因此，计算机辅助的干预计划可以改善结果。

方法

在这项工作中，我们实施了 GPU 加速的随机游走 (RW) 算法来检测胎盘，包括脐带插入和多普勒超声 (美国) 的胎盘血管系统。胎盘和背景种子在 10-20 个切片（共 245 个）中手动初始化。血管通过 Otsu 阈值在相同的切片中自动初始化。RW 找到胎盘的边界并重建脉管系统。

结果

我们在 5 次单绒毛膜妊娠和 24 次单胎妊娠中评估了我们的半自动方法。尽管在胎盘分割上取得了令人满意的性能 (Dice ≥ 84.0%)，但由于存在 US 混响伪影 (Dice ≥ 56.9%)，一些血管连接仍被忽略。我们还比较了用户间的可变性，并获得了胎盘和脉管系统的 Dice 系数分别为 ≥ 76.8% 和 ≥ 97.42%。经过 3 分钟的手动初始化，我们的 GPU 方法的计算速度是 CPU 的 10.6 倍。

结论

我们的半自动方法提供了近乎实时的用户体验，并且需要在不影响分割精度的情况下进行短期培训。因此，提出了一种强大的方法，可以在 TTTS 手术之前快速规划胎儿镜插入点。