Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes

Abstract

Automatic extraction of cerebral vessels and cranial nerves has important clinical value in the treatment of trigeminal neuralgia (TGN) and hemifacial spasm (HFS). However, because of the great similarity between different cerebral vessels and between different cranial nerves, it is challenging to segment cerebral vessels and cranial nerves in real time on the basis of true-color microvascular decompression (MVD) images. In this paper, we propose a lightweight, fast semantic segmentation Microvascular Decompression Network (MVDNet) for MVD scenarios which achieves a good trade-off between segmentation accuracy and speed. Specifically, we designed a Light Asymmetric Bottleneck (LAB) module in the encoder to encode context features. A Feature Fusion Module (FFM) was introduced into the decoder to effectively combine high-level semantic features and underlying spatial details. The proposed network has no pretrained model, fewer parameters, and a fast inference speed. Specifically, MVDNet achieved 76.59% mIoU on the MVD test set, has 0.72 M parameters, and has a 137 FPS speed using a single GTX 2080Ti card.

The role of artificial intelligence in the management of trigeminal neuralgia

“…This is enhanced by 3D rendering, enabling direct visualization of NVC from various perspectives and zoom levels. With advancements in neuronavigation reducing bone flaps during MVD ( 28 ), AI intraoperative segmentation protocols, like Bai et al innovative encoder-decoder structure, become invaluable for identifying vessels and nerves in tight surgical spaces ( 17 ). It is worth noting the protocol's impressive speed and accuracy, providing real-time assistance to surgeons during procedures.…”

“…In contrast to Lin et al, Bai et al introduced MVDNet, a deep learning network focused on real-time blood vessel and cranial nerve segmentation during MVD procedures for facial and trigeminal nerve disorders. MVDNet achieved impressive precision, with a 76.59% Intersection-over-Union (mIoU) accuracy and a rapid 137.6 fps speed, surpassing other real-time models ( 17 ). AI has brought about predictive models for postoperative outcomes.…”

Using Novel Segmentation Technology to Define Safe Corridors for Minimally Invasive Posterior Lumbar Interbody Fusion

“…thalamic nuclei, brain tumors, and cerebral arteries. [1][2][3][4][5][6][7] Most notably, there has been a surge of research interest surrounding deep segmentation for brain tumors that has driven significant technological advancement and incorporation of segmentation within the tumor and vascular fields. For example, preoperative planning in conjunction with 3-dimensional (3D) segmentation has allowed surgeons to better visualize the topographical relation of tumors to important vessels and improve the delineation between normal and abnormal tissues.…”