Paper Info
Title
Accurate Detection of Inner Ears in Head CTs Using a Deep Volume-to-Volume Regression Network with False Positive Suppression and a Shape-Based Constraint.
Abstract
Cochlear implants (CIs) are neural prosthetics which are used to treat patients with hearing loss. CIs use an array of electrodes which are surgically inserted into the cochlea to stimulate the auditory nerve endings. After surgery, CIs need to be programmed. Studies have shown that the spatial relationship between the intra-cochlear anatomy and electrodes derived from medical images can guide CI programming and lead to significant improvement in hearing outcomes. However, clinical head CT images are usually obtained from scanners of different brands with different protocols. The field of view thus varies greatly and visual inspection is needed to document their content prior to applying algorithms for electrode localization and intra-cochlear anatomy segmentation. In this work, to determine the presence/ absence of inner ears and to accurately localize them in head CTs, we use a volume-to-volume convolutional neural network which can be trained end-to-end to map a raw CT volume to probability maps which indicate inner ear positions. We incorporate a false positive suppression strategy in training and apply a shape-based constraint. We achieve a labeling accuracy of 98.59% and a localization error of 2.45 mm. The localization error is significantly smaller than a random forest-based approach that has been proposed recently to perform the same task.
Year
DOI
Venue
2018
10.1007/978-3-030-00937-3_80
Lecture Notes in Computer Science
Keywords
DocType
Volume
Cochlear implants,Landmark localization,3D U-Net
Conference
11073
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
5
4
Name
Order
Citations
PageRank
Dong-Qing Zhang147940.49
Jianing Wang283.59
Jack H. Noble313930.87
Benoit M. Dawant41388223.11