Surgical Navigation in the Anterior Skull Base Using 3-Dimensional Endoscopy and Surface Reconstruction.

Importance: Image guidance is an important adjunct for endoscopic sinus and skull base surgery. However, current systems require bulky external tracking equipment, and their use can interrupt efficient surgical workflow.

Objective: To evaluate a trackerless surgical navigation system using 3-dimensional (3D) endoscopy and simultaneous localization and mapping (SLAM) algorithms in the anterior skull base.

Separable Tendon-Driven Robotic Manipulator with a Long, Flexible, Passive Proximal Section.

This work tackles practical issues which arise when using a tendon-driven robotic manipulator (TDRM) with a long, flexible, passive proximal section in medical applications. Tendon-driven devices are preferred in medicine for their improved outcomes via minimally invasive procedures, but TDRMs come with unique challenges such as sterilization and reuse, simultaneous control of tendons, hysteresis in the tendon-sheath mechanism, and unmodeled effects of the proximal section shape. A separable TDRM which overcomes difficulties in actuation and sterilization is introduced, in which the body containing the electronics is reusable and the remainder is disposable.

Inner Ear Breaches from Vestibular Schwannoma Surgery: Revisiting the Incidence of Otologic Injury from Retrosigmoid and Middle Cranial Fossa Approaches.

Objective: To assess the rate of iatrogenic injury to the inner ear in vestibular schwannoma resections.

Study Design: Retrospective case review.

Setting: Multiple academic tertiary care hospitals.

Image-guided surgical planning of percutaneous nephrolithotomy with patient-specific CTRs.

Purpose: We propose the utilization of patient-specific concentric-tube robots (CTRs) whose designs are optimized to enhance their volumetric reachability of the renal stone, thus reducing the morbidities associated with percutaneous nephrolithotomy procedures. By employing a nested optimization-driven scheme, this work aims to determine a single surgical tract through which the patient-tailored CTR is deployed. We carry out a sensitivity analysis on the combined percutaneous access and optimized CTR design with respect to breathing-induced excursion of the kidneys based on preoperative images.

Iatrogenic inner ear dehiscence associated with lateral skull base surgery: a systematic analysis of drilling injuries and their causal factors.

Purpose: Drilling injuries of the inner ear are an underreported complication of lateral skull base (LSB) surgery. Inner ear breaches can cause hearing loss, vestibular dysfunction, and third window phenomenon. This study aims to elucidate primary factors causing iatrogenic inner ear dehiscences (IED) in 9 patients who presented to a tertiary care center with postoperative symptoms of IED following LSB surgery for vestibular schwannoma, endolymphatic sac tumor, Meniere's disease, paraganglioma jugulare, and vagal schwannoma.

On Surgical Planning of Percutaneous Nephrolithotomy with Patient-Specific CTRs.

Percutaneous nephrolithotomy (PCNL) is considered a first-choice minimally invasive procedure for treating kidney stones larger than 2 cm. It yields higher stone-free rates than other minimally invasive techniques and is employed when extracorporeal shock wave lithotripsy or uteroscopy are, for instance, infeasible. Using this technique, surgeons create a tract through which a scope is inserted for gaining access to the stones.

Double-Uncertainty Guided Spatial and Temporal Consistency Regularization Weighting for Learning-based Abdominal Registration.

In order to tackle the difficulty associated with the ill-posed nature of the image registration problem, regularization is often used to constrain the solution space. For most learning-based registration approaches, the regularization usually has a fixed weight and only constrains the spatial transformation. Such convention has two limitations: (i) Besides the laborious grid search for the optimal fixed weight, the regularization strength of a specific image pair should be associated with the content of the images, thus the "one value fits all" training scheme is not ideal; (ii) Only spatially regularizing the transformation may neglect some informative clues related to the ill-posedness.

EMDQ-SLAM: Real-time High-resolution Reconstruction of Soft Tissue Surface from Stereo Laparoscopy Videos.

We propose a novel stereo laparoscopy video-based non-rigid SLAM method called EMDQ-SLAM, which can incrementally reconstruct thee-dimensional (3D) models of soft tissue surfaces in real-time and preserve high-resolution color textures. EMDQ-SLAM uses the expectation maximization and dual quaternion (EMDQ) algorithm combined with SURF features to track the camera motion and estimate tissue deformation between video frames. To overcome the problem of accumulative errors over time, we have integrated a g2o-based graph optimization method that combines the EMDQ mismatch removal and as-rigid-as-possible (ARAP) smoothing methods.

EMDQ: Removal of Image Feature Mismatches in Real-Time.

This paper proposes a novel method for removing image feature mismatches in real-time that can handle both rigid and smooth deforming environments. Image distortion, parallax and object deformation may cause the pixel coordinates of feature matches to have non-rigid deformations, which cannot be represented using a single analytical rigid transformation. To solve this problem, we propose an algorithm based on the re-weighting and 1-point RANSAC strategy (R1P-RNSC), which operates under the assumption that a non-rigid deformation can be approximately represented by multiple rigid transformations.

UNIMODAL CYCLIC REGULARIZATION FOR TRAINING MULTIMODAL IMAGE REGISTRATION NETWORKS.

The loss function of an unsupervised multimodal image registration framework has two terms, i.e., a metric for similarity measure and regularization.

UNSUPERVISED MULTIMODAL IMAGE REGISTRATION WITH ADAPTATIVE GRADIENT GUIDANCE.

Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair.

F3RNet: full-resolution residual registration network for deformable image registration.

Purpose: Deformable image registration (DIR) is essential for many image-guided therapies. Recently, deep learning approaches have gained substantial popularity and success in DIR. Most deep learning approaches use the so-called mono-stream high-to-low, low-to-high network structure and can achieve satisfactory overall registration results.

A novel dual-network architecture for mixed-supervised medical image segmentation.

In medical image segmentation tasks, deep learning-based models usually require densely and precisely annotated datasets to train, which are time-consuming and expensive to prepare. One possible solution is to train with the mixed-supervised dataset, where only a part of data is densely annotated with segmentation map and the rest is annotated with some weak form, such as bounding box. In this paper, we propose a novel network architecture called Mixed-Supervised Dual-Network (MSDN), which consists of two separate networks for the segmentation and detection tasks respectively, and a series of connection modules between the layers of the two networks.

Real-Time Nonrigid Mosaicking of Laparoscopy Images.

The ability to extend the field of view of laparoscopy images can help the surgeons to obtain a better understanding of the anatomical context. However, due to tissue deformation, complex camera motion and significant three-dimensional (3D) anatomical surface, image pixels may have non-rigid deformation and traditional mosaicking methods cannot work robustly for laparoscopy images in real-time. To solve this problem, a novel two-dimensional (2D) non-rigid simultaneous localization and mapping (SLAM) system is proposed in this paper, which is able to compensate for the deformation of pixels and perform image mosaicking in real-time.

3D deep learning based classification of pulmonary ground glass opacity nodules with automatic segmentation.

Classifying ground-glass lung nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC) on diagnostic CT images is important to evaluate the therapy options for lung cancer patients. In this paper, we propose a joint deep learning model where the segmentation can better facilitate the classification of pulmonary GGNs. Based on our observation that masking the nodule to train the model results in better lesion classification, we propose to build a cascade architecture with both segmentation and classification networks.

Adversarial Uni- and Multi-modal Stream Networks for Multimodal Image Registration.

Deformable image registration between Computed Tomography (CT) images and Magnetic Resonance (MR) imaging is essential for many image-guided therapies. In this paper, we propose a novel translation-based unsupervised deformable image registration method. Distinct from other translation-based methods that attempt to convert the multimodal problem (e.

Mixed-Supervised Dual-Network for Medical Image Segmentation.

Deep learning based medical image segmentation models usually require large datasets with high-quality dense segmentations to train, which are very time-consuming and expensive to prepare. One way to tackle this difficulty is using the mixed-supervised learning framework, where only a part of data is densely annotated with segmentation label and the rest is weakly labeled with bounding boxes. The model is trained jointly in a multi-task learning setting.

Real-Time Surface Deformation Recovery from Stereo Videos.

Tissue deformation during the surgery may significantly decrease the accuracy of surgical navigation systems. In this paper, we propose an approach to estimate the deformation of tissue surface from stereo videos in real-time, which is capable of handling occlusion, smooth surface and fast deformation. We first use a stereo matching method to extract depth information from stereo video frames and generate the tissue template, and then estimate the deformation of the obtained template by minimizing ICP, ORB feature matching and as-rigid-as-possible (ARAP) costs.

Re-weighting and 1-Point RANSAC-Based P nP Solution to Handle Outliers.

The ability to handle outliers is essential for performing the perspective- n-point (P nP) approach in practical applications, but conventional RANSAC+P3P or P4P methods have high time complexities. We propose a fast P nP solution named R1PP nP to handle outliers by utilizing a soft re-weighting mechanism and the 1-point RANSAC scheme. We first present a P nP algorithm, which serves as the core of R1PP nP, for solving the P nP problem in outlier-free situations.

Real-Time Dense Reconstruction of Tissue Surface From Stereo Optical Video.

We propose an approach to reconstruct dense three-dimensional (3D) model of tissue surface from stereo optical videos in real-time, the basic idea of which is to first extract 3D information from video frames by using stereo matching, and then to mosaic the reconstructed 3D models. To handle the common low-texture regions on tissue surfaces, we propose effective post-processing steps for the local stereo matching method to enlarge the radius of constraint, which include outliers removal, hole filling, and smoothing. Since the tissue models obtained by stereo matching are limited to the field of view of the imaging modality, we propose a model mosaicking method by using a novel feature-based simultaneously localization and mapping (SLAM) method to align the models.

A Novel Mixed Reality Navigation System for Laparoscopy Surgery.

Objective: To design and validate a novel mixed reality head-mounted display for intraoperative surgical navigation.

Design: A mixed reality navigation for laparoscopic surgery (MRNLS) system using a head mounted display (HMD) was developed to integrate the displays from a laparoscope, navigation system, and diagnostic imaging to provide context-specific information to the surgeon. Further, an immersive auditory feedback was also provided to the user.

Kalman filter-based EM-optical sensor fusion for needle deflection estimation.

Purpose: In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects.

Methods: In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme.

Real-time probe tracking using EM-optical sensor for MRI-guided cryoablation.

Background: A method of real-time, accurate probe tracking at the entrance of the MRI bore is developed, which, fused with pre-procedural MR images, will enable clinicians to perform cryoablation efficiently in a large workspace with image guidance.

Methods: Electromagnetic (EM) tracking coupled with optical tracking is used to track the probe. EM tracking is achieved with an MRI-safe EM sensor working under the scanner's magnetic field to compensate the line-of-sight issue of optical tracking.

Computer-Aided Diagnosis of Ground-Glass Opacity Nodules Using Open-Source Software for Quantifying Tumor Heterogeneity.

Objective: The purposes of this study are to develop quantitative imaging biomarkers obtained from high-resolution CTs for classifying ground-glass nodules (GGNs) into atypical adenomatous hyperplasia (AAH), adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (IAC); to evaluate the utility of contrast enhancement for differential diagnosis; and to develop and validate a support vector machine (SVM) to predict the GGN type.

Materials And Methods: The heterogeneity of 248 GGNs was quantified using custom software. Statistical analysis with a univariate Kruskal-Wallis test was performed to evaluate metrics for significant differences among the four GGN groups.

Jugulodigastric lymph node size by age on CT in an adult cancer-free population.

Purpose: Evaluate jugulodigastric node size in cancer-free patients.

Methods: Retrospective review of 234 neck CT angiograms was performed. Known neoplasm, inflammation or poor imaging were excluded.