Fraunhofer MEVIS, Institute for Digital Medicine, Bremen, Germany.
Medical Imaging Computing, University of Bremen, Bremen, Germany.
Int J Comput Assist Radiol Surg. 2020 Dec;15(12):1963-1974. doi: 10.1007/s11548-020-02273-1. Epub 2020 Oct 7.
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.
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.
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.
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.
通过比较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过程。然而，由于手术过程中发生的解剖结构变化，建立后续采集之间的直接映射具有挑战性。我们在这里提出了一种通过从注册过程中排除切除腔来改进超声体积配准的方法。
我们的方法的第一步包括在切除过程中和切除后自动分割超声体积中的切除腔。我们使用了一种受 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注册后续的超声体积。
关于切除腔的分割，所提出的方法在 27 个体积上达到了 0.84 的平均 DICE 指数。关于后续超声采集的注册，我们将切除前和切除期间采集的体积的 mTRE 从 3.49 降低到 1.22 毫米。对于在切除前后采集的一组体积，mTRE 从 3.55 改善到 1.21 毫米。
我们提出了一种创新的配准算法，以补偿影响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中自动分割切除腔的超声体积配准方法。
