Automated Registration-Based Temporal Bone Computed Tomography Segmentation for Applications in Neurotologic Surgery.
Andy S DingAlexander LuZhaoshuo LiDeepa GalaiyaJeffrey H SiewerdsenRussell H TaylorFrancis X CreightonPublished in: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery (2021)
We demonstrated submillimeter accuracy for automated segmentation of temporal bone anatomy when compared against hand-segmented ground truth using our template registration pipeline. This method is not dependent on the training data volume that plagues many complex deep learning models. Favorable runtime and low computational requirements underscore this method's translational potential.
Keyphrases
- deep learning
- convolutional neural network
- computed tomography
- artificial intelligence
- bone mineral density
- machine learning
- minimally invasive
- soft tissue
- big data
- bone loss
- positron emission tomography
- bone regeneration
- coronary artery bypass
- electronic health record
- postmenopausal women
- magnetic resonance imaging
- body composition
- surgical site infection
- coronary artery disease
- climate change
- high resolution
- data analysis
- image quality
- human health