AI Medical Compendium Topic

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Ear, Inner

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A simple standard technique for labyrinthectomy in the rat: A methodical communication with a detailed description of the surgical process.

Physiology international
Aims Labyrinthectomized rats are suitable models to test consequences of vestibular lesion and are widely used to study neural plasticity. We describe a combined microsurgical-chemical technique that can be routinely performed with minimum damage. Me...

HeadLocNet: Deep convolutional neural networks for accurate classification and multi-landmark localization of head CTs.

Medical image analysis
Cochlear implants (CIs) are used to treat subjects with hearing loss. In a CI surgery, an electrode array is inserted into the cochlea to stimulate auditory nerves. After surgery, CIs need to be programmed. Studies have shown that the cochlea-electro...

Artificial intelligence in detection and segmentation of internal auditory canal and its nerves using deep learning techniques.

International journal of computer assisted radiology and surgery
PURPOSE: Artificial intelligence (AI) in medical imaging is a burgeoning topic that involves the interpretation of complex image structures. The recent advancements in deep learning techniques increase the computational powers to extract vital featur...

Automatic segmentation of inner ear on CT-scan using auto-context convolutional neural network.

Scientific reports
Temporal bone CT-scan is a prerequisite in most surgical procedures concerning the ear such as cochlear implants. The 3D vision of inner ear structures is crucial for diagnostic and surgical preplanning purposes. Since clinical CT-scans are acquired ...

Deep learning for the fully automated segmentation of the inner ear on MRI.

Scientific reports
Segmentation of anatomical structures is valuable in a variety of tasks, including 3D visualization, surgical planning, and quantitative image analysis. Manual segmentation is time-consuming and deals with intra and inter-observer variability. To dev...

Utility of unsupervised deep learning using a 3D variational autoencoder in detecting inner ear abnormalities on CT images.

Computers in biology and medicine
BACKGROUND AND PURPOSE: To examine the diagnostic performance of unsupervised deep learning using a 3D variational autoencoder (VAE) for detecting and localizing inner ear abnormalities on CT images.