Purpose

Neurosurgeons can have a better understanding of surgical procedures by comparing ultrasound images obtained at different phases of the tumor resection. However, establishing a direct mapping between subsequent acquisitions is challenging due to the anatomical changes happening during surgery. We propose here a method to improve the registration of ultrasound volumes, by excluding the resection cavity from the registration process.

Methods

The first step of our approach includes the automatic segmentation of the resection cavities in ultrasound volumes, acquired during and after resection. We used a convolution neural network inspired by the 3D U-Net. Then, subsequent ultrasound volumes are registered by excluding the contribution of resection cavity.

Results

Regarding the segmentation of the resection cavity, the proposed method achieved a mean DICE index of 0.84 on 27 volumes. Concerning the registration of the subsequent ultrasound acquisitions, we reduced the mTRE of the volumes acquired before and during resection from 3.49 to 1.22 mm. For the set of volumes acquired before and after removal, the mTRE improved from 3.55 to 1.21 mm.

Conclusions

We proposed an innovative registration algorithm to compensate the brain shift affecting ultrasound volumes obtained at subsequent phases of neurosurgical procedures. To the best of our knowledge, our method is the first to exclude automatically segmented resection cavities in the registration of ultrasound volumes in neurosurgery.

中文翻译：

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通过在神经外科手术中分割切除腔来增强超声体积的配准

目的

通过比较在肿瘤切除不同阶段获得的超声图像，神经外科医生可以更好地了解手术程序。然而，由于手术期间发生的解剖学变化，因此在后续采集之间建立直接映射是一项挑战。我们在这里提出一种方法，通过从注册过程中排除切除腔来改善超声体积的注册。

方法

我们的方法的第一步包括在切除过程中和切除后自动对超声腔中的切除腔进行分割。我们使用了受3D U-Net启发的卷积神经网络。然后，通过排除切除腔的贡献来记录随后的超声体积。

结果

关于切除腔的分割，所提出的方法在27个体积上实现了0.84的平均DICE指数。关于后续超声采集的配准，我们将术前和术中采集的体积的mTRE从3.49毫米降低到了1.22毫米。对于移除前后采集的体积，mTRE从3.55毫米提高到1.21毫米。

结论

我们提出了一种创新的注册算法，以补偿影响在神经外科手术后续阶段获得的超声体积的脑移位。据我们所知，我们的方法是第一个在神经外科超声体积记录中排除自动分段切除腔的方法。