This paper explores ear biometrics using a mixture of feature extraction techniques and
classifies this feature vector using deep learning with convolutional neural network. This …

The robust delineation of the cochlea and its inner structures combined with the detection of
the electrode of a cochlear implant within these structures is essential for envisaging a safer …

The recent literature on AI in otological imaging is promising and demonstrates the future
potential of this technology in automating certain imaging tasks in a healthcare environment …

Objective: To investigate key morphometric features identifiable on routine preoperative
computed tomography (CT) imaging indicative of incisional hernia (IH) formation following …

Background Automatic segmentation of temporal bone computed tomography (CT) images
is fundamental to image-guided otologic surgery and the intelligent analysis of CT images in …

Background In-vivo MR-based high-resolution volumetric quantification methods of the
endolymphatic hydrops (ELH) are highly dependent on a reliable segmentation of the inner …

Objective To investigate the feasibility of a deep learning method based on a UNETR model
for fully automatic segmentation of the cochlea in temporal bone CT images. Methods The …

Acute kidney injury (AKI) is a frequent pathology with a high mortality rate after even a single
AKI episode and a great risk of chronic kidney disease (CKD) development. To get insight …

Automated analysis of the inner ear anatomy in radiological data instead of time-consuming
manual assessment is a worthwhile goal that could facilitate preoperative planning and …

Incorporating shape information is essential for the delineation of many organs and
anatomical structures in medical images. While previous work has mainly focused on …