Cortical Vessel Segmentation for Neuronavigation Using Vesselness-Enforced Deep Neural Networks

Abstract

We propose in this paper an efficient method to segment cortical vessels in craniotomy images acquired by the surgical microscope. Our method uses a vesselness-enforced convolutional neural network to classify each pixel of a craniotomy image as a vessel or surrounding tissue. This permits training the network not only on appearance-based features but also on geometrical-based constraints that will ensure the continuity of the vascular trees. Our solution uses neural style transfer to generate new instances of images from manually labeled data leading to augment the training dataset in an anatomically semantic manner. The generated images improve the generalization of our model to various types of cortical surface appearances and vascular geometries. We conducted experiments on real images from human patients that demonstrate that accurate intraoperative cortical vessel segmentation can be achieved.

Intraoperative tissue classification methods in orthopedic and neurological surgeries: A systematic review

“…Studies using surgical endoscopes focused on spinal endoscopic surgeries ( 62 ) and percutaneous transforaminal endoscopic discectomies ( 63 ), where the identification of neural structures is critical. A surgical microscope was used in Haouchine et al ( 64 ), Bai et al ( 65 ), and Nercessian et al ( 66 ) during craniotomy and microvascular decompression ( Table 8 ).…”

“…A surgical endoscope was used in the studies by Cui et al ( 62 , 63 ), whereas Haouchine et al ( 64 ), Bai et al ( 65 ), and Nercessian et al ( 66 ) relied on surgical microscopes. No detailed information about types and brands of the devices was given.…”

“…Studies reported in Cui et al ( 62 , 63 ) used YOLO-v3 as the basic tissue classification algorithm, whereas Haouchine et al ( 64 ) and Nercessian et al ( 66 ) applied CNN architectures for semantic segmentation such as U-Net and VGG-19. A similar deep learning-based approach was used in Bai et al ( 65 ), but the architecture was based on the semantic segmentation method known as DeepLabv3 + and was extended with the feature distillation block and the atrous spatial pyramid pooling modules.…”

“…Surgical endoscope-based studies ( 62 , 63 ) identified nerve and dura, while surgical microscope-based studies ( 64 , 66 ) classified vessel, parenchyma, and background to distinguish trigeminal nerve. Bai et al ( 65 ) classified the facial nerve, glossopharyngeal nerve, vagus nerve, anterior inferior cerebellar artery, posterior inferior cerebellar artery, petrosal vein vessel, parenchyma, and background from microvascular decompression recordings.…”

“…Among brain tumor detection-related studies, the technologies that were used to detect brain tumors ( 9 ) intraoperatively are mainly based on the optical properties of the tissue. These include technologies such as hyperspectral imaging (e.g., 9 ), Raman spectroscopy (e.g., 22 ), and optical coherence tomography [e.g., ( 64 )]. Unlike other studies concerning the application of the VNIR hyperspectral push-broom camera (e.g., 9 ), Urbanos et al ( 16 ) proposed the snapshot camera, which could be used in a surgical environment due to its small footprint.…”