ArticleCells2022
Deep Learning Based Real-Time Semantic Segmentation of Cerebral Vessels and Cranial Nerves in Microvascular Decompression Scenes.
Article in Cells, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Microvascular decompression: a contemporary update.BMC surgery · 2025Review
- Application research on the diagnosis of classic trigeminal neuralgia based on VB-Net technology and radiomics.BMC medical imaging · 2024Article
- The role of artificial intelligence in the management of trigeminal neuralgia.Frontiers in surgery · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.