Deep Learning Pipeline for Automated Assessment of Distances Between Tonsillar Tumors and the Internal Carotid Artery.

Background: Evaluating the minimum distance (dTICA) between the internal carotid artery (ICA) and tonsillar tumors (TT) on imaging is essential for preoperative planning; we propose a tool to automatically extract dTICA.

Methods: CT scans of 96 patients with TT were selected from the cancer imaging archive. nnU-Net, a deep learning framework, was implemented to automatically segment both the TT and ICA from these scans.

A Label-Efficient Framework for Automated Sinonasal CT Segmentation in Image-Guided Surgery.

Objective: Segmentation, the partitioning of patient imaging into multiple, labeled segments, has several potential clinical benefits but when performed manually is tedious and resource intensive. Automated deep learning (DL)-based segmentation methods can streamline the process. The objective of this study was to evaluate a label-efficient DL pipeline that requires only a small number of annotated scans for semantic segmentation of sinonasal structures in CT scans.

Realtime assessment of vascular occlusion and reperfusion in animal models of intraoperative imaging - a pilot study.

Objectives: Intraoperative monitoring of blood flow (BF) remains vital to guiding surgical decisions. Here, we report the use of SurgeON™ Blood Flow Monitor (BFM), a prototype system that attaches to surgical microscopes and implements laser speckle contrast imaging (LSCI) to noninvasively obtain and present vascular BF information in real-time within the microscope's eyepiece.

Methods: The ability of SurgeON BFM to monitor BF status during reversible vascular occlusion procedures was investigated in two large animal models: occlusion of saphenous veins in six NZW rabbit hindlimbs and clipping of middle cerebral artery (MCA) branches in four Dorset sheep brain hemispheres.

SVPath: A Deep Learning Tool for Analysis of Stria Vascularis from Histology Slides.

Introduction: The stria vascularis (SV) may have a significant role in various otologic pathologies. Currently, researchers manually segment and analyze the stria vascularis to measure structural atrophy. Our group developed a tool, SVPath, that uses deep learning to extract and analyze the stria vascularis and its associated capillary bed from whole temporal bone histopathology slides (TBS).

A Deep Learning Framework for Analysis of the Eustachian Tube and the Internal Carotid Artery.

Objective: Obtaining automated, objective 3-dimensional (3D) models of the Eustachian tube (ET) and the internal carotid artery (ICA) from computed tomography (CT) scans could provide useful navigational and diagnostic information for ET pathologies and interventions. We aim to develop a deep learning (DL) pipeline to automatically segment the ET and ICA and use these segmentations to compute distances between these structures.

Study Design: Retrospective cohort.

A Novel Instrument Holder to Improve Operative Efficiency in Endoscopic Laryngeal and Airway Surgery.

Objective: Receiving instruments from surgical technicians during endoscopic laryngeal and airway microsurgery (ELAM) has challenges including repeated, expeditious handling of delicate instruments and passing them to the surgeon's hand opposite of where the surgical assistant is standing. Optimizing this interaction may reduce surgical errors and improve operative efficiency.

Methods: A proprietary ELAM instrument holder was attached to both sides of the operating room bed.