Mr. Minh Q. Vu Profile

Minh Q. Vu

at Vanderbilt Univ

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Publications (2)

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Proceedings Article | 29 March 2024 Presentation + Paper

Segmentation of the facial nerve using a weakly supervised GAN

Minh Vu, Yubo Fan, Jack Noble

Proceedings Volume 12928, 1292818 (2024) https://doi.org/10.1117/12.3008656

KEYWORDS: Image segmentation, Nerve, Cochlea, Voxels, Network architectures, Computed tomography, Surgery, Image registration, Ear, Machine learning

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Cochlear implants (CI) are a highly successful neural-prosthetic device, recreating the sensation of hearing by directly stimulating the nerve fibers inside the cochlea for individuals experiencing severe to profound hearing loss. Implantation traditionally requires invasive procedures such as mastoidectomy, however minimally invasive techniques such as percutaneous cochlear access have also been investigated. This method involves drilling a single hole through the skull surface, granting direct access to the cochlea where the CI can be threaded. The trajectory of this insertion typically involves traversing the facial recess, a region approximately 1.0–3.5 mm in width bounded posteriorly by the facial nerve and anteriorly by the chorda tympani The determination of a safe drilling trajectory is highly crucial, as damage to these structures during surgery may result in a loss of taste (chorda) or facial paralysis (facial nerve). It is therefore very important that these clinical structures are segmented accurately for the drilling trajectory planning process. In this work, we propose the use of a conditional generative adversarial network (cGAN) to automatically segment the facial nerve. Our method can also make up for noisy and disconnected generated segmentations using a minimum cost path search function between the endpoints. Our network utilized weakly supervised approach, being trained on a small sample of 12 manually segmented image and supplemented with 120 automatically segmented image created through atlas-based image registration. Our method generated segmentations with an average mean surface error of only 0.24mm, reducing the mean error of the original method by ~50%.

Proceedings Article | 8 March 2019 Paper

Analysis of middle ear morphology for design of a transnasal endoscope

Minh Vu, Rueben Banalagay, Dongqing Zhang, Alejandro Rivas, Loris Fichera, Robert Webster, Robert Labadie, Jack Noble

Proceedings Volume 10951, 109512S (2019) https://doi.org/10.1117/12.2513841

KEYWORDS: Ear, Visualization, Endoscopes, Image segmentation, Statistical modeling, Principal component analysis, Statistical analysis

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Cholesteatomas are benign lesions that form in the middle ear (ME). They can cause debilitating side effects including hearing loss, recurrent ear infection and drainage, and balance disruption. The current approach for positively identifying cholesteatomas requires intraoperative visualization either by lifting the ear drum or transmitting an endoscope through the ear canal and tympanic membrane – procedures which are typically done in and operating room with the patient under general anesthesia. We are developing a novel endoscope that can be inserted trans-nasally and could potentially be used in an outpatient setting allowing clinicians to easily detect and visualize cholesteatomas and other middle ear conditions. A crucial part of designing this device is determining the degrees of freedom necessary to visualize the regions of interest in the middle ear space. To permit virtual evaluation of scope design, in this work we propose to create a library of models of the most difficult to visualize region of the middle ear, the retrotympanum (RT), which is located deep and posterior to the tympanic membrane. We have designed a semi-automated atlas-based approach for segmentation of the RT. Our approach required 2-3 minutes of manual interaction for each of 20 cases tested. Each result was verified to be accurate by an experienced otologist. These results show the method is efficient and accurate enough to be applied to a large scale dataset. We also created a statistical shape model from the resulting segmentations that can be used to synthesize new plausible RT shapes for comprehensive virtual evaluation of endoscope designs and show that it can represent new RT shapes with average errors of 0.5 mm.

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