Watertight and 2-Manifold Surface Meshes Using Dual Contouring With Tetrahedral Decomposition of Grid Cubes

The Dual Contouring algorithm (DC) is a grid-based process used to generate surface meshes from volumetric data. The advantage of DC is that it can reproduce sharp features by inserting vertices anywhere inside the grid cube, as opposed to the Marching Cubes (MC) algorithm that can insert vertices only on the grid edges. However, DC is unable to guarantee 2-manifold and watertight meshes due to the fact that it produces only one vertex for each grid cube. We present a modified Dual Contouring algorithm that is capable of overcoming this limitation. Our method decomposes an ambiguous grid cube into a maximum of twelve tetrahedral cells; we introduce novel polygon generation rules that produce 2-manifold and watertight surface meshes. We have applied our proposed method on realistic data, and a comparison of the results of our proposed method with results from traditional DC shows the effectiveness of our method

Controlling the Error on Target Motion through Real-time Mesh Adaptation: Applications to Deep Brain Stimulation

We present an error-controlled mesh refinement procedure for needle insertion simulation and apply it to the simulation of electrode implantation for deep brain stimulation, including brain shift. Our approach enables to control the error in the computation of the displacement and stress fields around the needle tip and needle shaft by suitably refining the mesh, whilst maintaining a coarser mesh in other parts of the domain. We demonstrate through academic and practical examples that our approach increases the accuracy of the displacement and stress fields around the needle without increasing the computational expense. This enables real-time simulations. The proposed methodology has direct implications to increase the accuracy and control the computational expense of the simulation of percutaneous procedures such as biopsy, brachytherapy, regional anesthesia, or cryotherapy and can be essential to the development of robotic guidance.Comment: 21 pages, 14 figure

Robotically Steered Needles: A Survey of Neurosurgical Applications and Technical Innovations

This paper surveys both the clinical applications and main technical innovations related to steered needles, with an emphasis on neurosurgery. Technical innovations generally center on curvilinear robots that can adopt a complex path that circumvents critical structures and eloquent brain tissue. These advances include several needle-steering approaches, which consist of tip-based, lengthwise, base motion-driven, and tissue-centered steering strategies. This paper also describes foundational mathematical models for steering, where potential fields, nonholonomic bicycle-like models, spring models, and stochastic approaches are cited. In addition, practical path planning systems are also addressed, where we cite uncertainty modeling in path planning, intraoperative soft tissue shift estimation through imaging scans acquired during the procedure, and simulation-based prediction. Neurosurgical scenarios tend to emphasize straight needles so far, and span deep-brain stimulation (DBS), stereoelectroencephalography (SEEG), intracerebral drug delivery (IDD), stereotactic brain biopsy (SBB), stereotactic needle aspiration for hematoma, cysts and abscesses, and brachytherapy as well as thermal ablation of brain tumors and seizure-generating regions. We emphasize therapeutic considerations and complications that have been documented in conjunction with these applications

Active Duty Training For Support of Navy\u27s Additive Manufacturing Strategy

Additive manufacturing has recently gained the attention of multiple stakeholders, including those in the advanced manufacturing industry, research and government labs, academia, and the Navy community. Various efforts within the Navy focus on studying the best way for parts to be built and repaired for marine and naval vessels. Rapid manufacturing of spare components is particularly important for sailors, especially while deployed on warships, as they often do not have timely access to spare parts from the supply chain. For that purpose, a multidisciplinary team of engineering and education faculty have developed a series of workshops to train on-duty sailors in designing, testing, reverse engineering, and printing parts needed for their daily operations. The workshop has modules focused on rapid prototyping, reverse engineering, computer aided design, material testing, product data management, and product lifecycle management. The Office of Naval Research Workforce Development program funds this program

Engineering Collaborations in Medical Modeling and Simulation

Fifty years ago computer science was just beginning to see common acceptance as a growing discipline and very few universities had a computer science department although other departments were utilizing computers and software to enhance their methodologies. We believe modeling and simulation (M&S) is on a similar path. Many other disciplines utilize M&S to enhance their methodologies but we also believe that M&S fundamentals can be essential in making better decisions by utilizing the appropriate model for the problem at hand, expanding the solution space through simulation, and understanding it through visualization and proper analyses. After our students learn these fundamentals, we offer the opportunity to apply them to varied application areas. One such application area is medical M&S, which is a broad area involving anatomical modeling, planning and training simulations, image-guided procedures and more. In this paper, we share several research projects involving M&S and the collaborations that make them possible

Surface-based characterization of intra-surgical brain deformations in image-guided surgery

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. I 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.Finally, this thesis also features a review of image guidance for brain surgery, a published survey of anatomical surface registration, and a literature review of biomechanical modeling of brain tissue

An integrated range-tedPxxx segmentation and registration framework for the characterization ofintra-PIfix8Wfi brain deformations inimage-PIfi)8 surgery

Image-P)Psurgery (IGS) is a technique for localizing anatomical structures on the basis of volumetric image data and for determining the optimal surgical path to reach thesestructures, 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 byintra-PIUCxx8 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 onnon-8):P 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-fi--VP segmentation of thepre-8U--PIU)-- brain surface in magnetic resonance image data,range-WfixPIU of the cortex in the OR, range--MR rigid transformation computation, andrange---PI8 non-e--- 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 Computer Vision and Image Understanding 89 (2003) 226--251 www.elsevier.com/locate/cviu Corresponding author

Gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation

Medical image segmentation involves a process of categorization of target regions that are typically varied in terms of shape, orientation and scales. This requires highly accurate algorithms as marginal segmentation errors in medical images may lead to inaccurate diagnosis in subsequent procedures. The U-Net framework has become one of the dominant deep neural network architectures for medical image segmentation. Due to complex and irregular shape of objects involved in medical images, robust feature representations that correspond to various spatial transformations are key to achieve successful results. Although U-Net-based deep architectures can perform feature extraction and localization, the design of specialized architectures or layer modifications is often an intricate task. In this paper, we propose an effective solution to this problem by introducing Gabor filter banks into the U-Net encoder, which has not yet been well explored in existing U-Net-based segmentation frameworks. In addition, global self-attention mechanisms and Transformer layers are also incorporated into the U-Net framework to capture global contexts. Through extensive testing on two benchmark datasets, we show that the Gabor filter-embedded U-Net with Transformer encoders can enhance the robustness of deep-learned features, and thus achieve a more competitive performance

Gabor Filter-Embedded U-Net with Transformer-Based Encoding for Biomedical Image Segmentation

Medical image segmentation involves a process of categorization of target regions that are typically varied in terms of shape, orientation and scales. This requires highly accurate algorithms as marginal segmentation errors in medical images may lead to inaccurate diagnosis in subsequent procedures. The U-Net framework has become one of the dominant deep neural network architectures for medical image segmentation. Due to complex and irregular shape of objects involved in medical images, robust feature representations that correspond to various spatial transformations are key to achieve successful results. Although U-Net-based deep architectures can perform feature extraction and localization, the design of specialized architectures or layer modifications is often an intricate task. In this paper, we propose an effective solution to this problem by introducing Gabor filter banks into the U-Net encoder, which has not yet been well explored in existing U-Net-based segmentation frameworks. In addition, global self-attention mechanisms and Transformer layers are also incorporated into the U-Net framework to capture global contexts. Through extensive testing on two benchmark datasets, we show that the Gabor filter-embedded U-Net with Transformer encoders can enhance the robustness of deep-learned features, and thus achieve a more competitive performance