A Segmentation Method of Foramen Ovale Based on Multiatlas.
Jiashi ZhaoHuatao GeWei HeYanfang LiWeili ShiZhengang JiangYonghui LiXingzhi LiPublished in: Computational and mathematical methods in medicine (2021)
Trigeminal neuralgia is a neurological disease. It is often treated by puncturing the trigeminal nerve through the skin and the oval foramen of the skull to selectively destroy the pain nerve. The process of puncture operation is difficult because the morphology of the foramen ovale in the skull base is varied and the surrounding anatomical structure is complex. Computer-aided puncture guidance technology is extremely valuable for the treatment of trigeminal neuralgia. Computer-aided guidance can help doctors determine the puncture target by accurately locating the foramen ovale in the skull base. Foramen ovale segmentation is a prerequisite for locating but is a tedious and error-prone task if done manually. In this paper, we present an image segmentation solution based on the multiatlas method that automatically segments the foramen ovale. We developed a data set of 30 CT scans containing 20 foramen ovale atlas and 10 CT scans for testing. Our approach can perform foramen ovale segmentation in puncture operation scenarios based solely on limited data. We propose to utilize this method as an enabler in clinical work.
Keyphrases
- magnetic resonance
- deep learning
- computed tomography
- convolutional neural network
- neuropathic pain
- dual energy
- electronic health record
- chronic pain
- artificial intelligence
- climate change
- image quality
- positron emission tomography
- spinal cord
- big data
- machine learning
- pain management
- single cell
- newly diagnosed
- pet ct
- blood brain barrier
- soft tissue
- replacement therapy