Ultrasound is a commonly used modality for medical imaging. While this modality has great advantages in terms of safety and cost relative to other imaging modalities, it also has several limitations. Signal-to-noise ratio varies greatly depending on the acoustic properties of the tissue being imaged and the depth of the target structures. In this work, we evaluate the use of deep learning based methods to reconstruct 3D surfaces of general objects imaged with ultrasound. We evaluate three variants of the 3D U-Net with different training scenarios. We were able to train networks to reconstruct three distinct categories of objects relatively well when trained on limited data from each category. However, the performance of the networks did not generalize well when testing on categories of objects not included in the training. We also investigated the effects of employing dual-task autoencoding on generalizability. These results provide a baseline for exploring modifications to the U-Net framework to improve generalizability. A generalizable method could improve visualization for a number of ultrasound imaging tasks.
Cochlear implants (CIs) are neural prosthetics that can improve hearing in patients with severe-to-profound hearing loss. CIs induce hearing sensation by stimulating auditory nerve fibers (ANFs) using an electrode array that is surgically implanted into the cochlea. After the device is implanted, an audiologist programs the CI processor to optimize hearing performance. However, without knowing which ANFs are being stimulated by each electrode, audiologists must rely solely on patient performance to inform the programming adjustments. Patient-specific neural stimulation modeling has been proposed to provide objective information to assist audiologists with programming, but this approach requires accurate localization of ANFs in patient CT images. In this paper, we propose an automatic neural-network-based method for atlas-based localization of the ANFs. Our results show that our method is able to produce smooth ANF predictions that are more realistic than those produced by a previously proposed semi-manual localization method. Accurate and realistic ANF localizations are critical for constructing patient-specific ANF stimulation models for model guided CI programming.
Cochlear Implants (CIs) are neural prosthetics which use an array of implanted electrodes to improve hearing in patients with severe-to-profound hearing loss. After implantation, the CI is programmed by audiologists who adjust various parameters to optimize hearing performance for the patient. Without knowing which Auditory Nerve Fibers (ANFs) are being stimulated by each electrode, this process can require dozens of programming sessions and often does not lead to optimal programming. The Internal Auditory Canal (IAC) houses the ANFs as they travel from the implantation site, the cochlea, to the brain. In this paper, we present a method for localizing the IAC in a CT image by deforming an atlas IAC mesh to a CT image using a 3D U-Net. Our results suggest this method is more accurate than an active shape model-based method when tested on a test set of 20 images with ground truth. This IAC segmentation can be used to infer the position of the invisible ANFs to assist with patient-specific CI programming.
Cochlear implants (CIs) are neural prosthetics used to improve hearing in patients with severe-to-profound hearing loss. After implantation, the process of fine-tuning the implant for a specific patient is expedited if the audiologist has tools to approximate which auditory nerve fiber regions are being stimulated by the implant’s electrode array. Auditory nerves travel from the cochlea where the prosthetic is implanted to the brain via the internal auditory canal (IAC). In this paper, we present a method for segmenting the IAC in a CT image using weakly supervised 3D UNets. Our approach is to train a U-Net with a custom loss function to refine a localization provided by a previously proposed active-shape-model-based IAC segmentation method. Preliminary results indicate that our proposed approach is successful in refining IAC localization, which is an important step towards accurate auditory nerve fiber localization for neural activation modeling.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.