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We propose a segmentation methodology for brainstem cranial nerves using statistical shape model (SSM)-based deformable 3D contours from T2 MR images. We create shape models for ten pairs of cranial nerves. High-resolution T2 MR images are segmented for nerve centerline using a 1-Simplex discrete deformable 3D contour model. These segmented centerlines comprise training datasets for the shape model. Point correspondence for the training dataset is performed using an entropy-based energy minimization framework applied to particles located on the centerline curve. The shape information is incorporated into the 1-Simplex model by introducing a shape-based internal force, making the deformation stable against low resolution and image artifacts. The proposed method is validated through extensive experiments using both synthetic and patient MRI data. The robustness and stability of the proposed method are experimented using synthetic datasets. SSMs are constructed independently for ten pairs (CNIII–CNXII) of brainstem cranial nerves using ten non-pathological image datasets of the brainstem. The constructed ten SSMs are assessed in terms of compactness, specificity and generality. In order to quantify the error distances between segmented results and ground truths, two metrics are used: mean absolute shape distance (MASD) and Hausdorff distance (HD). MASD error using the proposed shape model is 0.19 ± 0.13 (mean ± std. deviation) mm and HD is 0.21 mm which are sub-voxel accuracy given the input image resolution. This paper described a probabilistic digital atlas of the ten brainstem-attached cranial nerve pairs by incorporating a statistical shape model with the 1-Simplex deformable contour. The integration of shape information as a priori knowledge results in robust and accurate centerline segmentations from even low-resolution MRI data, which is essential in neurosurgical planning and simulations for accurate and robust 3D patient-specific models of critical tissues including cranial nerves.

Surgical data science (SDS) and computer-assisted surgery (CAS) are becoming increasingly conventional as more physicians incorporate surgical navigation systems into their workflows. Consequently, the necessity for enhanced anatomical structure depictions during intraoperative surgical guidance is also increasing. Anatomically accurate musculoskeletal representations are particularly necessary for orthopedic operations, especially spinal procedures. Additionally, CAS system recognition of clinically relevant structures along surgical corridors is of equal importance; such recognition permits intraoperative guidance when procedures are supervised by surgical process models (SPMs). Therefore, this study outlines a comprehensive solution, termed the “Surgical GPS”, which merges patient-specific, ligamentoskeletal models and physician-designated landmarks and ontologies, to expand spinal anatomy representations and demonstrate SPM applications toward several spine procedures. Patient models are validated with Dice similarity coefficient and Hausdorff distance metrics. Anatomical landmarks are queried from SPMs using pertinent search engines and are highlighted on the patient models, potentially enhancing successful surgical outcomes through context-aware, “GPS” guided CAS.

Scoliotic deformities may be addressed with either anterior or posterior approaches for scoliosis correction procedures. While typically quite invasive, the impact of these operations may be reduced through the use of computer-assisted surgery. A combination of physician-designated anatomical landmarks and surgical ontologies allows for real-time intraoperative guidance during computer-assisted surgical interventions. Predetermined landmarks are labeled on an identical patient model, which seeks to encompass vertebrae, intervertebral disks, ligaments, and other soft tissues. The inclusion of this anatomy permits the consideration of hypothetical forces that are previously not well characterized in a patient-specific manner. Updated ontologies then suggest procedural directions throughout the surgical corridor, observing the positioning of both the physician and the anatomical landmarks of interest at the present moment. Merging patient-specific models, physician-designated landmarks, and ontologies to produce real-time recommendations magnifies the successful outcome of scoliosis correction through enhanced pre-surgical planning, reduced invasiveness, and shorted recovery time.

The creation of a new modeling and simulation engineering program presented the opportunity to evaluate the core skills desirable in a well-rounded graduate. Software was identified as an often neglected aspect of modeling and simulation programs and an attempt was made to remedy this. This paper discusses the software skills identified as necessary/desirable in a graduate. The focus is on discrete event simulation (DES), though the skills are transferable to other paradigms. The discussion is partitioned into discussing skills appropriate for simulation application development and simulation tool development. The discussion is further partitioned to discuss core computer science skills (object-oriented programming and data structures), software architecture, graphics development, implementing DES worldviews, and an ability to work with open source software. The result is a graduate that is desirable to industry and graduate research.

This paper presents early work on a fall detection method using transfer learning method, in conjunction with a long-term effort to combine efficient machine learning and prior personalized musculoskeletal modeling to deploy fall injury mitigation in geriatric subjects. Inspired by the tremendous progress in image-based object recognition with deep convolutional neural networks (DCNNs), we opt for a pre-trained kinematics-based machine learning approach through existing large-scale annotated accelerometry datasets. The accelerometry datasets are converted to images using time-frequency analysis, based on scalograms, by computing the continuous wavelet transform filter bank. Subsequently, data augmentation is performed on these scalogram images to increase accuracy, thereby complementing limited labeled fall sensor data, enabling transfer learning from the existing pre-trained model. The experimental results on publicly available URFD datasets demonstrate that transfer learning leads to a better performance than the existing methods in the case of scarce labeled training data.

Scoliosis correction surgery is typically a highly invasive procedure that involves either an anterior or posterior release, which respectively entail the resection of ligaments and bone facets from the front or back of the spine, in order to make it sufficiently compliant to enable the correction of the deformity. In light of progress in other areas of surgery in minimally invasive therapies, orthopedic surgeons have begun envisioning computer simulation-assisted planning that could answer unprecedented what-if questions. This paper presents preliminary steps taken towards simulation-based surgery planning that will provide answers as to how much anterior or posterior release is truly necessary, provided we also establish the amplitude of surgical forces involved in corrective surgery. This question motivates us to pursue a medical image-based anatomical modeling pipeline that can support personalized finite elements simulation, based on models of the spine that not only feature vertebrae and inter-vertebral discs (IVDs), but also descriptive ligament models. This paper suggests a way of proceeding, based on the application of deformable multi-surface Simplex model applied to a CAD-based representation of the spine that makes explicit all spinal ligaments, along with vertebrae and IVDs. It presents a preliminary model-based segmentation study whereby Simplex meshes of CAD vertebrae are registered to the subject’s corresponding vertebrae in CT data, which then drives ligament and IVD model registration by aggregation of neighboring vertebral transformations. This framework also anticipates foreseen improvements in MR imaging that could achieve better contrasts in ligamentous tissues in the future.

Purpose: We describe a shape-aware multisurface simplex deformable model for the segmentation of healthy as well as pathological lumbar spine in medical image data. Approach: This model provides an accurate and robust segmentation scheme for the identification of intervertebral disc pathologies to enable the minimally supervised planning and patient-specific simulation of spine surgery, in a manner that combines multisurface and shape statistics-based variants of the deformable simplex model. Statistical shape variation within the dataset has been captured by application of principal component analysis and incorporated during the segmentation process to refine results. In the case where shape statistics hinder detection of the pathological region, user assistance is allowed to disable the prior shape influence during deformation. Results: Results demonstrate validation against user-assisted expert segmentation, showing excellent boundary agreement and prevention of spatial overlap between neighboring surfaces. This section also plots the characteristics of the statistical shape model, such as compactness, generalizability and specificity, as a function of the number of modes used to represent the family of shapes. Final results demonstrate a proof-of-concept deformation application based on the open-source surgery simulation Simulation Open Framework Architecture toolkit. Conclusions: To summarize, we present a deformable multisurface model that embeds a shape statistics force, with applications to surgery planning and simulation.

More accurate and robust image segmentations are needed for identification of spine pathologies and to assist with spine surgery planning and simulation. A framework for 3D segmentation of healthy and herniated intervertebral discs from T2-weighted magnetic resonance imaging was developed that exploits weak shape priors encoded in simplex mesh active surface models. Weak shape priors inherent in simplex mesh deformable models have been exploited to automatically segment intervertebral discs. An ellipsoidal simplex template mesh was initialized within the disc image boundary through affine landmark-based registration and was allowed to deform according to image gradient forces. Coarse-to-fine multi-resolution approach was adopted in conjunction with decreasing shape memory forces to accurately capture the disc boundary. User intervention is allowed to turn off the shape feature and guide model deformation when the internal simplex shape memory influence hinders detection of pathology. A resulting surface mesh was utilized for disc compression simulation under gravitational and weight loads using Simulation Open Framework Architecture. For testing, 16 healthy discs were automatically segmented, and five pathological discs were segmented with minimal supervision. Segmentation results were validated against expert guided segmentation and demonstrate mean absolute shape distance error of $$

Printed and/or digital atlases are important tools for medical research and surgical intervention. While these atlases can provide guidance in identifying anatomical structures, they do not take into account the wide variations in the shape and size of anatomical structures that can occur from patient to patient. Accurate, patient-specific representations are especially important for surgical interventions like deep brain stimulation, where even small inaccuracies can result in dangerous complications. This research effort extends the discrete deformable 2-simplex mesh into the multi-material domain where geometry-based internal forces and image-based external forces are used in the deformation process. Multi-material 2-simplex meshes having shared boundaries are initialized from multi-material triangular surface meshes. A multi-material deformable framework is presented and used to segment anatomical structures of the deep brain region such as the subthalamic nucleus.

Discectomy procedure simulations require patient-specific and robust three-dimensional representation of vertebral and intervertebral disc structures, as well as existing pathology, of the lumbar spine. Prior knowledge, such as expected shape and variation within a sample population, can be incorporated through statistical shape models to optimize the image segmentation process. This paper describes a framework for construction of statistical shape models (SSMs) of nine L1 vertebrae and eight L1-L2 intervertebral discs from computed tomography and magnetic resonance (MR) images respectively. The generated SSMs are utilized as a reference for knowledge-based priors to optimize coarse-to-fine multi-surface segmentation of vertebrae and intervertebral discs in volumetric MR images. Correspondence between instances within each model has been established using entropy-based energy minimization of particles on the image surfaces, which is independent of any reference bias or surface parameterization techniques. The resulting shape models faithfully capture variability within the first seven principal modes.

This paper presents a cranial nerve segmentation technique that combines a 3D deformable contour and a 3D contour Statistical Shape Model (SSM). A set of training data for the construction of the 3D contour shape model is produced using a 1-simplex based discrete deformable contour model where the centerline identification proceeds by optimizing internal and external forces. Point-correspondence for the training dataset is performed using an entropy-based energy minimization of particles on the centerline curve. The resulting average shape is used as a prior knowledge, which is incorporated into the 1-simplex as a reference shape model, making the approach stable against low resolution and image artifacts during segmentation using MRI data. Shape variability is shown using the first 3 modes of variation. The segmentation result is validated quantitatively, with ground truth provided by an expert.

In this paper, we present a minimally supervised method for the identification of the intra-cranial portion of cranial nerves, using a novel, discrete 1-Simplex 3D active contour model. The clinical applications include planning and personalized simulation of skull base neurosurgery. The centerline of a cranial nerve is initialized from two user-supplied end points by computing a Minimal Path. The 1-Simplex is a Newtonian model for vertex motion, where every non-endpoint vertex has 2-connectivity with neighboring vertices, with which it is linked by edges. The segmentation behavior of the model is governed by the equilibrium between internal and external forces. The external forces include an image force that favors a centered path within high-vesselness points. The method is validated quantitatively using synthetic and real MRI datasets.

This paper presents a methodology for the digital formatting of a printed atlas of the brainstem and the delineation of cranial nerves from this digital atlas. It also describes on-going work on the 3D resampling and refinement of the 2D functional regions and nerve contours. In MRI-based anatomical modeling for neurosurgery planning and simulation, the complexity of the functional anatomy entails a digital atlas approach, rather than less descriptive voxel or surface-based approaches. However, there is an insufficiency of descriptive digital atlases, in particular of the brainstem. Our approach proceeds from a series of numbered, contour-based sketches coinciding with slices of the brainstem featuring both closed and open contours. The closed contours coincide with functionally relevant regions, whereby our objective is to fill in each corresponding label, which is analogous to painting numbered regions in a paint-by-numbers kit. Any open contour typically coincides with a cranial nerve. This 2D phase is needed in order to produce densely labeled regions that can be stacked to produce 3D regions, as well as identifying the embedded paths and outer attachment points of cranial nerves. Cranial nerves are modeled using an explicit contour based technique called 1-Simplex. The relevance of cranial nerves modeling of this project is two-fold: i) this atlas will fill a void left by the brain segmentation communities, as no suitable digital atlas of the brainstem exists, and ii) this atlas is necessary to make explicit the attachment points of major nerves (except I and II) having a cranial origin. Keywords: digital atlas, contour models, surface models

Image-guided surgery (IGS) is a technique for localizing anatomical structures on the basis of volumetric image data and for determining the optimal surgical path to reach these structures, by the means of a localization device, or probe, whose position is tracked over time. The usefulness of this technology hinges on the accuracy of the transformation between the image volume and the space occupied by the patient anatomy and spanned by the probe. Unfortunately, in neurosurgery this transformation can be degraded by intra-surgical brain shift, which often measures more than 10 mm and can exceed 25 mm. We propose a method for characterizing brain shift that is based on non-rigid surface registration, and can be combined with a constitutively realistic finite element approach for volumetric displacement estimation. The proposed registration method integrates in a unified framework all of the stages required to estimate the movement of the cortical surface in the operating room: model-based segmentation of the pre-operative brain surface in magnetic resonance image data, range-sensing of the cortex in the OR, range-MR rigid transformation computation, and range-based non-rigid brain motion estimation. The brain segmentation technique is an adaptation of the surface evolution model. Its convergence to the brain boundary is the result of a speed term restricted to white and grey matter voxels made explicit by a classifier, and the final result is post-processed to yield a Closest Point Map of the brain surface in MR space. In turn, this Closest Point Map is used to produce the homologous pairs required to determine a highly efficient, 2D spline-based, Iterative Closest Point (ICP) non-rigid surface registration. The baseline for computing intra-operative brain displacement, as well as the initial starting point of the non-rigid ICP registration, is determined by a very good rigid range-MR transformation, produced by a simple procedure for relating the range coordinate system to that of the probe, and ultimately to that of the MR volume.

This paper describes on-going work on the transposition to digital format of 2D images of a printed atlas of the brainstem. In MRI-based anatomical modeling for neurosurgery planning and simulation, the complexity of the functional anatomy entails a digital atlas approach, rather than less descriptive voxel or surface-based approaches. However, there is an insufficiency of descriptive digital atlases, in particular of the brainstem. Our approach proceeds from a series of numbered, contour-based sketches coinciding with slices of the brainstem featuring both closed and open contours. The closed contours coincide with functionally relevant regions, in which case our objective is to fill in each corresponding label, which is analogous to painting numbered regions in a paint-by-numbers kit. The open contours typically coincide with cranial nerve tracts as well as symbols representing the medullary pyramids. This 2D phase is needed in order to produce densely labeled regions that can be stacked to produce 3D regions, as well as identifying embedded paths and outer attachment points of cranial nerves. In future work, the stacked labeled regions will be resampled and refined probabilistically, through active contour and surface modeling based on MRI T1, T2 and tractographic data. The relevance to spine modeling of this project is two-fold: (i) this atlas will fill a void left by the spine and brain segmentation communities, as no digital atlas of the brainstem exist, and (ii) this atlas is necessary to make explicit the attachment points of major nerves having both cranial and spinal origin, specifically nerves X and XI, as well all the attachment points of cranial nerves other than I and II.

A framework for 3D segmentation of healthy and herniated intervertebral discs from T2-weighted MRI was developed that exploits weak shape priors encoded in simplex mesh active surface models. An ellipsoidal simplex template mesh was initialized within the disc image boundary through affine landmark-based registration, and was allowed to deform according to image gradient forces. Coarse-to-fine multi-resolution approach was adopted in conjunction with decreasing shape memory forces to accurately capture the disc boundary. User intervention is allowed to turn off the shape feature and guide model deformation when internal shape memory influence hinders detection of pathology. For testing, 16 healthy discs were automatically segmented, and 5 pathological discs were segmented with minimal supervision. A resulting surface mesh was utilized for disc compression simulation under gravitational and weight loads and Meshless-Mechanics (MM)-based cutting using Simulation Open Framework Architecture (SOFA). The surface-mesh based segmentation method is part of a processing pipeline for anatomical modeling to support interactive surgery simulation. Segmentation results were validated against expert guided segmentation and demonstrate mean absolute shape distance error of less than 1 mm.

The potential relationship between chronic traumatic encephalopathy (CTE) and head injuries such as blast-related traumatic brain injury (TBI) is an important area of study, particularly for military and contact sports populations, yet little is known about this relationship. To address this topic, the Department of Defense (DoD) Blast Injury Research Program Coordinating Office organized the 2015 International State-of-The-Science Meeting, which brought together subject matter experts from the DoD, other federal agencies, academia, industry, foreign allies, and the sports community. Over the course of the meeting, this community of experts reached a consensus regarding the current body of knowledge and the future of the field. The overarching finding was that there is insufficient existing scientific evidence to link blast-related TBI with CTE. The meeting's Expert Panel also agreed on 13 additional findings describing research and knowledge gaps, clinical gaps, and research opportunities that, if addressed with focused effort, would further the understanding of the relationship between blast-related TBI and CTE. To this end, the Expert Panel also developed six recommendations for advancing research, each with short-and long-Term goals. Among the six recommendations, the Expert Panel identified the first four as highest priority for addressing pressing research needs. These four high-priority recommendations include, in order of priority: (1) more collection and study of clinical neuropathology samples, (2) standardization of clinical diagnostic criteria, (3) development of clinically appropriate and standardized animal models, and (4) development of noninvasive serial assessment strategies (i.e., imaging or biospecimen biomarkers). © Copyright 2017, Mary Ann Liebert, Inc. 2017.

In this paper we evaluate three different mesh generation approaches with respect to their fitness for use in a surgery simulation and navigation system. The behavior of such a system can be thought of as a trade-off between material fidelity and computation time. We focus on one critical component of this system, namely non-rigid registration, and conduct an experimental study of the selected mesh generation approaches with respect to material fidelity of the resulting meshes, shape of mesh elements, condition number of the resulting stiffness matrix, and the registration error. We concluded that meshes with very bad fidelity do not affect the accuracy drastically. On the contrary, meshes with very good fidelity hurt the speed of the mesher due to the poor quality they exhibit. We also observed that the speed of the solver is very sensitive to mesh quality rather than to fidelity. For these reasons, we think that mesh generation should first try to produce high quality meshes, possibly sacrificing fidelity.

This paper presents on-going research that addresses uncertainty along the Z-axis in image-guided surgery, for applications to large surgical workspaces, including those found in veterinary medicine. Veterinary medicine lags human medicine in using image guidance, despite MR and CT data scanning of animals. The positional uncertainty of a surgical tracking device can be modeled as an octahedron with one long axis coinciding with the depth axis of the sensor, where the short axes are determined by pixel resolution and workspace dimensions. The further a 3D point is from this device, the more elongated is this long axis, and the greater the uncertainty along Z of this point's position, in relation to its components along X and Y. Moreover, for a triangulation-based tracker, its position error degrades with the square of distance. Our approach is to use two or more Micron Trackers to communicate with each other, and combine this feature with flexible positioning. Prior knowledge of the type of surgical procedure, and if applicable, the species of animal that determines the scale of the workspace, would allow the surgeon to pre-operatively configure the trackers in the OR for optimal accuracy. Our research also leverages the open-source Image-guided Surgery Toolkit (IGSTK).

This paper presents on-going work on a method for determining which subvolumes of a patient-specific tissue map, extracted from CT data of the head, are relevant to simulating endoscopic sinus surgery of that individual, and for decomposing these relevant tissues into triangles and tetrahedra whose mesh size is well controlled. The overall goal is to limit the complexity of the real-time biomechanical interaction while ensuring the clinical relevance of the simulation. Relevant tissues are determined as the union of the pathology present in the patient, of critical tissues deemed to be near the intended surgical path or pathology, and of bone and soft tissue near the intended path, pathology or critical tissues. The processing of tissues, prior to meshing, is based on the Fast Marching method applied under various guises, in a conditional manner that is related to tissue classes. The meshing is based on an adaptation of a meshing method of ours, which combines the Marching Tetrahedra method and the discrete Simplex mesh surface model to produce a topologically faithful surface mesh with well controlled edge and face size as a first stage, and Almost-regular Tetrahedralization of the same prescribed mesh size as a last stage.

An iterative classification method developed for 2D CT head data classification problem and using both statistical and spatial information is introduced in this paper. The method reduces the chance of misclassification, preserving the contiguity of tissue classes. This method is minimally supervised so that it enforces a relation between tissues and classes. In later iterations high-confidence points are used to help classify nearby ambiguous points, based on the assumption that points in close proximity and of comparable intensities are probably representing the same tissue class.

The simulation of pituitary gland surgery requires a precise classification of soft tissues, vessels and bones. Bone structures tend to be thin and have diffuse edges in CT data, and thus the common method of thresholding can produce incomplete segmentations. In this paper, we present a novel multi-scale sheet enhancement measure and apply it to paranasal sinus bone segmentation. The measure uses local shape information obtained from an eigenvalue decomposition of the Hessian matrix. It attains a maximum in the middle of a sheet, and also provides local estimates of its width and orientation. These estimates are used to create a vector field orthogonal to bone boundaries, so that a flux maximizing flow algorithm can be applied to recover them. Hence, the sheetness measure has the essential properties to be incorporated into the computation of anatomical models for the simulation of pituitary surgery, enabling it to better account for the presence of sinus bones. We validate the approach quantitatively on synthetic examples, and provide comparisons with existing segmentation techniques on paranasal sinus CT data.

This paper presents a method for tessellating tissue boundaries and their interiors, given as input a tissue map consisting of relevant classes of the head, in order to produce anatomical models for finite element-based simulation of endoscopic pituitary surgery. Our surface meshing method is based on the simplex model, which is initialized by duality from the topologically accurate results of the Marching Cubes algorithm, and which features explicit control over mesh scale, while using tissue information to adhere to relevant boundaries. Our mesh scale strategy is spatially varying, based on the distance to a central point or linearized surgical path. The tetrahedralization stage also features a spatially varying mesh scale, consistent with that of the surface mesh.

This paper presents a method for identifying tissue classes of the head from co-registered MR and CT, given a small set of training points of each class, in a manner that exploits the embedded or tubular structure, as well as the contiguity, of each tissue. Tissue maps produced by this method are then applied to a suite of patient-specific anatomical models for the simulation of trans-nasal pituitary surgery, through subsequent tissue-guided surface and volume meshing. The method presented here overcomes shortcomings of a previous method of ours that exploited the embedded structure and contiguity of tissues to produce a tissue map that accounted for intra-cranial, intra-orbital and extra-cranial regions. Specifically, the assumption of embedded structure breaks down for critical tissues such as vasculature and cranial nerves, which often straddle two regions. This paper presents a method that first identifies critical tissues, beginning with a Minimal Path (MP) through user-specified points from each critical structure, generally coinciding with loci of strongly negative outward Gradient Flux, known as sink points. The rest of the critical tissue voxels can then be identified by proceeding radially from the GF-weighted minimal path through sink points. The final step is the identification of the remaining tissues, making use of embedded structure and contiguity, based on our previous method.

Summary form only given. Voxel Classification is a mapping of feature vectors to a discrete set of tissue classes. Surgical simulation is a technique whereby a user interacts, through virtual surgical tools, with an anatomical model endowed with realistic constitutive properties. Up to now, such virtual anatomical models have been obtained by elaborate manual segmentation. This paper suggests a minimally supervised procedure for extracting from CT and MR scans, co-registered in a distortion-tolerant manner, a highly descriptive classification of tissues which exploits global contiguity and prior assumptions about spatial extent and embedded structure, in order to generate a patient-specific anatomical model.

This paper presents ongoing research on a semi-automatic method for computing, from CT and MR data, patient-specific anatomical models used in surgical simulation. Up to now, virtual anatomical models for surgical simulation have been highly task-specific and obtained by highly elaborated computer–user interaction, including segmentation and meshing. We propose a minimally supervised procedure for extracting from a set of MR and CT scans a highly descriptive anatomical mesh, leading not merely to one generic model, but to a family of patient-specific models. This procedure integrates a distortion-tolerant mutual information MR-CT registration, a new tissue classification exploiting global spatial cues, a simplex-based surface meshing model to identify and triangulate the relevant anatomical boundaries, and a final almost-regular tetrahedralization of tissue volumes surrounded by these boundaries. This procedure is designed to be extensible to other surgical applications.

This paper presents a literature survey of automatic 3D surface registration techniques emphasizing the mathematical and algorithmic underpinnings of the subject. The relevance of surface registration to medical imaging is that there is much useful anatomical information in the form of collected surface points which originate from complimentary modalities and which must be reconciled. Surface registration can be roughly partitioned into three issues: choice of transformation, elaboration of surface representation and similarity criterion, and matching and global optimization. The first issue concerns the assumptions made about the nature of relationships between the two modalities, e.g. whether a rigid-body assumption applies, and if not, what type and how general a relation optimally maps one modality onto the other. The second issue determines what type of information we extract from the 3D surfaces, which typically characterizes their local or global shape, and how we organize this information into a representation of the surface which will lead to improved efficiency and robustness in the last stage. The last issue pertains to how we exploit this information to estimate the transformation which best aligns local primitives in a globally consistent manner or which maximizes a measure of the similarity in global shape of two surfaces. Within this framework, this paper discusses in detail each surface registration issue and reviews the state-of-the-art among existing techniques.

We propose a method for estimating intrasurgical brain shift for image-guided surgery. This method consists of five stages: the identification of relevant anatomical surfaces within the MRI/CT volume, range-sensing of the skin and cortex in the OR, rigid registration of the skin range image with its MRI/CT homologue, non-rigid motion tracking over time of cortical range images, and lastly, interpolation of this surface displacement information over the whole brain volume via a realistically valued finite element model of the head. This paper focuses on the anatomical surface identification and cortical range surface tracking problems. The surface identification scheme implements a recent algorithm which imbeds 3D surface segmentation as the level- set of a 4D moving front. A by-product of this stage is a Euclidean distance and closest point map which is later exploited to speed up the rigid and non-rigid surface registration. The range-sensor uses both laser-based triangulation and defocusing techniques to produce a 2D range profile, and is linearly swept across the skin or cortical surface to produce a 3D range image. The surface registration technique is of the iterative closest point type, where each iteration benefits from looking up, rather than searching for, explicit closest point pairs. These explicit point pairs in turn are used in conjunction with a closed-form SVD-based rigid transformation computation and with fast recursive splines to make each rigid and non-rigid registration iteration essentially instantaneous. Our method is validated with a novel deformable brain-shaped phantom, made of Polyvinyl Alcohol Cryogel.

We propose a method for estimating intrasurgical brain shift for image-guided surgery. This method consists of five stages: the identification of relevant anatomical surfaces within the MRI/CT volume, range-sensing of the skin and cortex in the OR, rigid registration of the skin range image with its MRI/CT homologue, non-rigid motion tracking over time of cortical range images, and lastly, interpolation of this surface displacement information over the whole brain volume via a realistically valued finite element model of the head. This papers focuses on the anatomical surface identification and cortical range surface tracking problems. The surface identification scheme implements a recent algorithm which imbeds 3D surface segmentation as the level-set of a 4D moving front. A by-product of this stage is a Euclidean distance and closest point map which is later exploited to speed up the rigid and non-rigid surface registration. The range-sensor uses both laser-based triangulation and defocusing techniques to produce a 2D range profile, and is linearly swept across the skin or cortical surface to produce a 3D range image. The surface registration technique is of the iterative closest point type, where each iteration benefits from looking up, rather than searching for, explicit closest point pairs. These explicit point pairs in turn are used in conjunction with a closed-form SVD-based rigid transformation computation and with fast recursive splines to make each rigid and non-rigid registration iteration essentially instantaneous. Our method is validated with a novel deformable brain-shaped phantom, made of Polyvinyl Alcohol Cryogel.

Image-guided surgery has evolved over the past 15 years from stereotactic planning, where the surgeon planned approaches to intracranial targets on the basis of 2D images presented on a simple workstation, to the use of sophisticated multi- modality 3D image integration in the operating room, with guidance being provided by mechanically, optically or electro-magnetically tracked probes or microscopes. In addition, sophisticated procedures such as thalamotomies and pallidotomies to relieve the symptoms of Parkinson's disease, are performed with the aid of volumetric atlases integrated with the 3D image data. Operations that are performed stereotactically, that is to say via a small burr- hole in the skull, are able to assume that the information contained in the pre-operative imaging study, accurately represents the brain morphology during the surgical procedure. On the other hand, preforming a procedure via an open craniotomy presents a problem. Not only does tissue shift when the operation begins, even the act of opening the skull can cause significant shift of the brain tissue due to the relief of intra-cranial pressure, or the effect of drugs. Means of tracking and correcting such shifts from an important part of the work in the field of image-guided surgery today. One approach has ben through the development of intra-operative MRI imaging systems. We describe an alternative approach which integrates intra-operative ultrasound with pre-operative MRI to track such changes in tissue morphology.