A Self-Configuring Deep Learning Network for Segmentation of Temporal Bone Anatomy in Cone-Beam CT Imaging.
Andy S DingAlexander LuZhaoshuo LiManish SahuDeepa GalaiyaJeffrey H SiewerdsenMathias UnberathRussell H TaylorFrancis X CreightonPublished in: Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery (2023)
Using an open-source deep learning pipeline, we demonstrate consistently submillimeter accuracy for semantic CT segmentation of temporal bone anatomy compared to hand-segmented labels. This pipeline has the potential to greatly improve preoperative planning workflows for a variety of otologic and neurotologic procedures and augment existing image guidance and robot-assisted systems for the temporal bone.
Keyphrases
- deep learning
- robot assisted
- convolutional neural network
- bone mineral density
- artificial intelligence
- computed tomography
- cone beam
- machine learning
- bone loss
- image quality
- soft tissue
- minimally invasive
- contrast enhanced
- dual energy
- high resolution
- postmenopausal women
- patients undergoing
- magnetic resonance imaging
- positron emission tomography
- magnetic resonance
- risk assessment
- fluorescence imaging
- data analysis