Fast and automatic bone segmentation and registration of 3D ultrasound to CT for the full pelvic anatomy: a comparative study.

Purpose: Ultrasound (US) is a safer alternative to X-rays for bone imaging, and its popularity for orthopedic surgical navigation is growing. Routine use of intraoperative US for navigation requires fast, accurate and automatic alignment of tracked US to preoperative computed tomography (CT) patient models. Our group previously investigated image segmentation and registration to align untracked US to CT of only the partial pelvic anatomy.

A Systematic Review and Meta-analysis on the Reproducibility of Ultrasound-based Metrics for Assessing Developmental Dysplasia of the Hip.

Background: The purposes of this study were to (1) perform a systematic review of articles that reported agreement or reproducibility in repeated diagnosis of developmental dysplasia of the hip (DDH) using ultrasound imaging, (2) estimate the reproducibility in the available dysplasia metrics, and (3) compare reproducibility of the available dysplasia metrics.

Methods: A systematic review of the Medline and Embase databases was performed by using a search strategy formulated from our research question: "For infants at risk of DDH, are US imaging-based diagnoses reproducible?" Two reviewers independently identified articles for inclusion in the systematic review, and then assessed the quality of the included studies using the Guidelines for Reporting Reliability and Agreement Studies guideline. Variability and agreement-related statistics in the included studies were extracted and included in a meta-analysis for summarizing the available statistics.

Modelling and extraction of pulsatile radial distension and compression motion for automatic vessel segmentation from video.

Identification of vascular structures from medical images is integral to many clinical procedures. Most vessel segmentation techniques ignore the characteristic pulsatile motion of vessels in their formulation. In a recent effort to automatically segment vessels that are hidden under fat, we motivated the use of the magnitude of local pulsatile motion extracted from surgical endoscopic video.

Variability in muscle activation of simple speech motions: A biomechanical modeling approach.

Biomechanical models of the oropharynx facilitate the study of speech function by providing information that cannot be directly derived from imaging data, such as internal muscle forces and muscle activation patterns. Such models, when constructed and simulated based on anatomy and motion captured from individual speakers, enable the exploration of inter-subject variability of speech biomechanics. These models also allow one to answer questions, such as whether speakers produce similar sounds using essentially the same motor patterns with subtle differences, or vastly different motor equivalent patterns.

Automatic Evaluation of Scan Adequacy and Dysplasia Metrics in 2-D Ultrasound Images of the Neonatal Hip.

Ultrasound (US) imaging of an infant's hip joint is widely used for early detection of developmental dysplasia of the hip. In current US-based diagnosis of developmental dysplasia of the hip, trained clinicians acquire US images and, if they judge them to be adequate (i.e.

Strain-Initialized Robust Bone Surface Detection in 3-D Ultrasound.

Three-dimensional ultrasound has been increasingly considered as a safe radiation-free alternative to radiation-based fluoroscopic imaging for surgical guidance during computer-assisted orthopedic interventions, but because ultrasound images contain significant artifacts, it is challenging to automatically extract bone surfaces from these images. We propose an effective way to extract 3-D bone surfaces using a surface growing approach that is seeded from 2-D bone contours. The initial 2-D bone contours are estimated from a combination of ultrasound strain images and envelope power images.

Endoscopic scene labelling and augmentation using intraoperative pulsatile motion and colour appearance cues with preoperative anatomical priors.

Purpose: Despite great advances in medical image segmentation, the accurate and automatic segmentation of endoscopic scenes remains a challenging problem. Two important aspects have to be considered in segmenting an endoscopic scene: (1) noise and clutter due to light reflection and smoke from cutting tissue, and (2) structure occlusion (e.g.

Stable Overlapping Replicator Dynamics for Brain Community Detection.

A fundamental means for understanding the brain's organizational structure is to group its spatially disparate regions into functional subnetworks based on their interactions. Most community detection techniques are designed for generating partitions, but certain brain regions are known to interact with multiple subnetworks. Thus, the brain's underlying subnetworks necessarily overlap.

Fast and Accurate Data Extraction for Near Real-Time Registration of 3-D Ultrasound and Computed Tomography in Orthopedic Surgery.

Automatic, accurate and real-time registration is an important step in providing effective guidance and successful anatomic restoration in ultrasound (US)-based computer assisted orthopedic surgery. We propose a method in which local phase-based bone surfaces, extracted from intra-operative US data, are registered to pre-operatively segmented computed tomography data. Extracted bone surfaces are downsampled and reinforced with high curvature features.

Coupled Stable Overlapping Replicator Dynamics for Multimodal Brain Subnetwork Identification.

Combining imaging modalities to synthesize their inherent strengths provides a promising means for improving brain subnetwork identification. We propose a multimodal integration technique based on a sex-differentiated formulation of replicator dynamics for identifying subnetworks of brain regions that exhibit high inter-connectivity both functionally and structurally. Our method has a number of desired properties, namely, it can operate on weighted graphs derived from functional magnetic resonance imaging (tMRI) and diffusion MRI (dMRI) data, allows for subnetwork overlaps, has an intrinsic criterion for setting the number of subnetworks, and provides statistical control on false node inclusion in the identified subnetworks via the incorporation of stability selection.

Simultaneous Multi-Structure Segmentation and 3D Nonrigid Pose Estimation in Image-Guided Robotic Surgery.

In image-guided robotic surgery, segmenting the endoscopic video stream into meaningful parts provides important contextual information that surgeons can exploit to enhance their perception of the surgical scene. This information provides surgeons with real-time decision-making guidance before initiating critical tasks such as tissue cutting. Segmenting endoscopic video is a challenging problem due to a variety of complications including significant noise attributed to bleeding and smoke from cutting, poor appearance contrast between different tissue types, occluding surgical tools, and limited visibility of the objects' geometries on the projected camera views.

Automatic segmentation of occluded vasculature via pulsatile motion analysis in endoscopic robot-assisted partial nephrectomy video.

Hilar dissection is an important and delicate stage in partial nephrectomy, during which surgeons remove connective tissue surrounding renal vasculature. Serious complications arise when the occluded blood vessels, concealed by fat, are missed in the endoscopic view and as a result are not appropriately clamped. Such complications may include catastrophic blood loss from internal bleeding and associated occlusion of the surgical view during the excision of the cancerous mass (due to heavy bleeding), both of which may compromise the visibility of surgical margins or even result in a conversion from a minimally invasive to an open intervention.

Automatic extraction of bone surfaces from 3D ultrasound images in orthopaedic trauma cases.

Purpose: 3D ultrasound (US) imaging has the potential to become a powerful alternative imaging modality in orthopaedic surgery as it is radiation-free and can produce 3D images (in contrast to fluoroscopy) in near-real time. Conventional B-mode US images, however, are characterized by high levels of noise and reverberation artifacts, image quality is user-dependent, and bone surfaces are blurred, which makes it difficult to both interpret images and to use them as a basis for navigated interventions. 3D US has great potential to assist orthopaedic care, possibly assisting during surgery if the anatomical structures of interest could be localized and visualized with sufficient accuracy and clarity and in a highly automated rapid manner.

Automatic labelling of tumourous frames in free-hand laparoscopic ultrasound video.

Laparoscopic ultrasound (US) is often used during partial nephrectomy surgeries to identify tumour boundaries within the kidney. However, visual identification is challenging as tumour appearance varies across patients and US images exhibit significant noise levels. To address these challenges, we present the first fully automatic method for detecting the presence of kidney tumour in free-hand laparoscopic ultrasound sequences in near real-time.

Efficient multi-organ segmentation in multi-view endoscopic videos using pre-operative priors.

Synergistic fusion of pre-operative (pre-op) and intraoperative (intra-op) imaging data provides surgeons with invaluable insightful information that can improve their decision-making during minimally invasive robotic surgery. In this paper, we propose an efficient technique to segment multiple objects in intra-op multi-view endoscopic videos based on priors captured from pre-op data. Our approach leverages information from 3D pre-op data into the analysis of visual cues in the 2D intra-op data by formulating the problem as one of finding the 3D pose and non-rigid deformations of tissue models driven by features from 2D images.

Auto localization and segmentation of occluded vessels in robot-assisted partial nephrectomy.

Hilar dissection is an important and delicate stage in partial nephrectomy during which surgeons remove connective tissue surrounding renal vasculature. Potentially serious complications arise when vessels occluded by fat are missed in the endoscopic view and are not appropriately clamped. To aid in vessel discovery, we propose an automatic method to localize and label occluded vasculature.

Robust bone detection in ultrasound using combined strain imaging and envelope signal power detection.

Bone localization in ultrasound (US) remains challenging despite encouraging advances. Current methods, e.g.

Overlapping replicator dynamics for functional subnetwork identification.

Functional magnetic resonance imaging (fMRI) has been widely used for inferring brain regions that tend to work in tandem and grouping them into subnetworks. Despite that certain brain regions are known to interact with multiple subnetworks, few existing techniques support identification of subnetworks with overlaps. To address this limitation, we propose a novel approach based on replicator dynamics that facilitates detection of sparse overlapping subnetworks.

Volume-specific parameter optimization of 3D local phase features for improved extraction of bone surfaces in ultrasound.

Background: Accurate localization of bone surfaces remains a challenge hampering adoption of ultrasound guidance in computer-assisted orthopaedic surgery. Local phase image features have recently been proven efficacious for segmenting bone surfaces from ultrasound images, but the quality of the processing depends on numerous filter parameters that are currently set through a trial and error process that is tedious, unintuitive and subject to large inter-user variability.

Methods: A method is presented for automatically selecting parameters of Log-Gabor filters used to extract bone surfaces from 3D ultrasound volumes that is based on properties estimated directly from the specific image.

3D ultrasound-CT registration in orthopaedic trauma using GMM registration with optimized particle simulation-based data reduction.

Accurate real-time registration of intra-operative ultrasound (US) to computed tomography (CT) remains a challenging problem. In orthopedic applications, a recent promising approach proposed the use of Gaussian mixture modeling for bone surface registration. Though relatively successful, the method relied on naive and error prone subsampling of the surfaces registered to reduce computational cost and also heavily relied on heuristically-set parameters for bone surface generation.

Fiber connectivity integrated brain activation detection.

Inference of brain activation through the analysis of functional magnetic resonance imaging (fMRI) data is seriously confounded by the high level f noise in the observations. To mitigate the effects of noise, we propose incorporating anatomical connectivity into brain activation detection as motivated by how the functional integration of distinct brain areas is facilitated via neural fiber pathways. In this work, we formulate activation detection as a probabilistic graph-based segmentation problem with fiber networks estimated from diffusion MRI (dMRI) data serving as a prior.

Modeling brain activation in fMRI using group MRF.

Noise confounds present serious complications to functional magnetic resonance imaging (fMRI) analysis. The amount of discernible signals within a single dataset of a subject is often inadequate to obtain satisfactory intra-subject activation detection. To remedy this limitation, we propose a novel group Markov random field (GMRF) that extends each subject's neighborhood system to other subjects to enable information coalescing.

Automatic bone localization and fracture detection from volumetric ultrasound images using 3-D local phase features.

This article presents a novel method for bone segmentation from three-dimensional (3-D) ultrasound images that derives intensity-invariant 3-D local image phase measures that are then employed for extracting ridge-like features similar to those that occur at soft tissue/bone interfaces. The main contributions in this article include: (1) the extension of our previously proposed phase-symmetry-based bone surface extraction from two-dimensional (2-D) to 3-D images using 3-D Log-Gabor filters; (2) the design of a new framework for accuracy evaluation based on using computed tomography as a gold standard that allows the assessment of surface localization accuracy across the entire 3-D surface; (3) the quantitative validation of accuracy of our 3-D phase-processing approach on both intact and fractured bone surfaces using phantoms and ex vivo 3-D ultrasound scans; and (4) the qualitative validation obtained by scanning emergency room patients with distal radius and pelvis fractures. We show a 41% improvement in surface localization error over the previous 2-D phase symmetry method.

Group replicator dynamics: a novel group-wise evolutionary approach for sparse brain network detection.

Functional magnetic resonance imaging (fMRI) is increasingly used for studying functional integration of the brain. However, large inter-subject variability in functional connectivity, particularly in disease populations, renders detection of representative group networks challenging. In this paper, we propose a novel technique, "group replicator dynamics" (GRD), for detecting sparse functional brain networks that are common across a group of subjects.

Active learning for interactive 3D image segmentation.

We propose a novel method for applying active learning strategies to interactive 3D image segmentation. Active learning has been recently introduced to the field of image segmentation. However, so far discussions have focused on 2D images only.