Synthetic data accelerates the development of generalizable learning-based algorithms for X-ray image analysis.

Artificial intelligence (AI) now enables automated interpretation of medical images. However, AI's potential use for interventional image analysis remains largely untapped. This is because the post hoc analysis of data collected during live procedures has fundamental and practical limitations, including ethical considerations, expense, scalability, data integrity and a lack of ground truth.

Towards 2D/3D Registration of the Preoperative MRI to Intraoperative Fluoroscopic Images for Visualization of Bone Defects.

Magnetic Resonance Imaging (MRI) is a medical imaging modality that allows for the evaluation of soft-tissue diseases and the assessment of bone quality. Preoperative MRI volumes are used by surgeons to identify defected bones, perform the segmentation of lesions, and generate surgical plans before the surgery. Nevertheless, conventional intraoperative imaging modalities such as fluoroscopy are less sensitive in detecting potential lesions.

Visualization in 2D/3D registration matters for assuring technology-assisted image-guided surgery.

Purpose: Image-guided navigation and surgical robotics are the next frontiers of minimally invasive surgery. Assuring safety in high-stakes clinical environments is critical for their deployment. 2D/3D registration is an essential, enabling algorithm for most of these systems, as it provides spatial alignment of preoperative data with intraoperative images.

The Impact of Machine Learning on 2D/3D Registration for Image-Guided Interventions: A Systematic Review and Perspective.

Image-based navigation is widely considered the next frontier of minimally invasive surgery. It is believed that image-based navigation will increase the access to reproducible, safe, and high-precision surgery as it may then be performed at acceptable costs and effort. This is because image-based techniques avoid the need of specialized equipment and seamlessly integrate with contemporary workflows.

Fiducial-Free 2D/3D Registration for Robot-Assisted Femoroplasty.

Femoroplasty is a proposed alternative therapeutic method for preventing osteoporotic hip fractures in the elderly. Previously developed navigation system for femoroplasty required the attachment of an external X-ray fiducial to the femur. We propose a fiducial-free 2D/3D registration pipeline using fluoroscopic images for robot-assisted femoroplasty.

Extended Capture Range of Rigid 2D/3D Registration by Estimating Riemannian Pose Gradients.

Traditional intensity-based 2D/3D registration requires near-perfect initialization in order for image similarity metrics to yield meaningful updates of X-ray pose and reduce the likelihood of getting trapped in a local minimum. The conventional approaches strongly depend on image appearance rather than content, and therefore, fail in revealing large pose offsets that substantially alter the appearance of the same structure. We complement traditional similarity metrics with a convolutional neural network-based (CNN-based) registration solution that captures large-range pose relations by extracting both local and contextual information, yielding meaningful X-ray pose updates without the need for accurate initialization.

Automatic annotation of hip anatomy in fluoroscopy for robust and efficient 2D/3D registration.

Purpose: Fluoroscopy is the standard imaging modality used to guide hip surgery and is therefore a natural sensor for computer-assisted navigation. In order to efficiently solve the complex registration problems presented during navigation, human-assisted annotations of the intraoperative image are typically required. This manual initialization interferes with the surgical workflow and diminishes any advantages gained from navigation.

Pose Estimation of Periacetabular Osteotomy Fragments With Intraoperative X-Ray Navigation.

Objective: State-of-the-art navigation systems for pelvic osteotomies use optical systems with external fiducials. In this paper, we propose the use of X-ray navigation for pose estimation of periacetabular fragments without fiducials.

Methods: A two-dimensional/three-dimensional (2-D/3-D) registration pipeline was developed to recover fragment pose.

Learning to detect anatomical landmarks of the pelvis in X-rays from arbitrary views.

Purpose: Minimally invasive alternatives are now available for many complex surgeries. These approaches are enabled by the increasing availability of intra-operative image guidance. Yet, fluoroscopic X-rays suffer from projective transformation and thus cannot provide direct views onto anatomy.

Biomechanical Guidance System for Periacetabular Osteotomy.

This chapter presents a biomechanical guidance navigation system for performing periacetabular osteotomy (PAO) to treat developmental dysplasia of the hip. The main motivation of the biomechanical guidance system (BGS) is to plan and track the osteotomized fragment in real time during PAO while simplifying this challenging procedure. The BGS computes the three-dimensional position of the osteotomized fragment in terms of conventional anatomical angles and simulates biomechanical states of the joint.

Estimation of attachment regions of hip muscles in CT image using muscle attachment probabilistic atlas constructed from measurements in eight cadavers.

Purpose: Patient-specific musculoskeletal biomechanical simulation is useful in preoperative surgical planning and postoperative assessment in orthopedic surgery and rehabilitation medicine. A difficulty in application of the patient-specific musculoskeletal modeling comes from the fact that the muscle attachment regions are typically invisible in CT and MRI. Our purpose is to develop a method for estimating patient-specific muscle attachment regions from 3D medical images and to validate with cadaver experiments.

Shape Tracking of a Dexterous Continuum Manipulator Utilizing Two Large Deflection Shape Sensors.

Dexterous continuum manipulators (DCMs) can largely increase the reachable region and steerability for minimally and less invasive surgery. Many such procedures require the DCM to be capable of producing large deflections. The real-time control of the DCM shape requires sensors that accurately detect and report large deflections.