The Surgical Assistant Workstation (SAW) is a software development framework that can be used to
develop new applications in robot-assisted surgery with augmented visualization. Robot-assisted laparoscopic
surgery and micro-surgery as presented by the da Vinci telerobotic system and the Johns
Hopkins Steady Hand, respectively can be improved by providing fully-integrated image guidance and
information-enhanced intra-operative assistance to the surgical team and to the surgeon in particular.
This paper describes several use case applications of the SAW framework, as well as a novel user interface
library being developed to support 3D interactive menu systems and overlays for surgical guidance.

The Surgical Assistant Workstation

This paper is a position statement on the benefits that an Open Source toolkit can bring to the field of image guided surgery. The particular example discussed here is the IGSTK toolkit. IGSTK is an open source software project designed for supporting the development of image guided surgery software applications. It provides functionalities for interfacing with optical and electromagnetic trackers, reading and visualizing DICOM datasets, as well as loading and displaying geometrical models resulting from image segmentation. These functionalities are enveloped by a simplified API implemented in a layer designed with emphasis on patient safety. The Toolkit is designed to ease the development of IGS applications, providing the basic functionalities related to the management of basic components. IGSTK is based on the Insight Toolkit (ITK) and the Visualization Toolkit (VTK), and can be combined with GUI toolkits such as FLTK and Qt. The IGSTK toolkit is distributed under a BSD license. 
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IGSTK: Building High Quality Roads with Open Source Software

The finite element (FE) method is a powerful tool for the study of biomechanics. One of the limiting factors in transitioning this tool into the clinic is the time required to generate high quality meshes for analysis. Previously, we developed a mapped meshing technique that utilized force control and a finite element solver to warp a template mesh onto subject specific surfaces. This paper describes a displacement based method that directly warps the template mesh onto subject specific surfaces using distance as the driving measure for the deformable registration. The resulting meshes were evaluated for mesh quality and compared to the force based method. An initial evaluation was performed using a mathematical phantom. The algorithm was then applied to generate meshes for the phalanx bones of the human hand. The algorithm successfully mapped the template bone to all of the bony surfaces, with the exception of the distal phalanx bone. In this one case, significant differences existed between the geometries of the template mesh and the distal phalanx. Further refinement of the algorithm may allow the algorithm to successfully generate meshes even in the presence of large geometric shape differences.

Comparison of Displacement-Based and Force-Based Mapped Meshing

A new Combinatorial Ricci curvature and Laplacian operators for grayscale images are introduced and tested on 2D medical images. These notions are based upon more general concepts developed by R. Forman. Further applications are also suggested.

Combinatorial Ricci Curvature for Image Processing

Diﬀusion tensor imaging has become an important research and clinical tool, owing to its unique ability to infer microstructural properties of living tissue. Increased use has led to a demand for statistical tools to analyze diﬀusion tensor data and perform, for example, conﬁdence estimates, ROI analysis, and group comparisons. A ﬁrst step towards developing a statistical framework is establishing the basic notion of distance between tensors. We investigate the properties of two previously proposed metrics that deﬁne a Riemannian manifold: the aﬃne-invariant and Euclidean metrics. We ﬁnd that the Euclidean metric is more appropriate for intra-voxel comparisons, and suggest that a context-dependent metric may be required for inter-voxel comparisons.

On What Manifold do Diffusion Tensors Live?

The task of classifying or labeling cortical sulci is made difficult by the fact that individual sulci may not have unique distinguishing features and usually need to be identified by a multivariate feature set that takes the relative spatial arrangement into account. In this paper, classical multidimensional scaling (MDS), which gives a geometric interpretation to input dissimilarity data, is used to classify 180 sulci drawn from the ten major classes of sulci. Using a leave-one-out validation strategy, we acheive a success rate of 100% in the best case and 78% in the worst case. For these more difficult cases, we propose a second stage of classification using shape based features. One of these features is the geodesic distance between sulcal curves obtained from a new open curve representation in a geometric framework.
With MDS, we oﬀer a simple and intuitive approach to a challenging problem. Not only can we easily separate left and right brain sulci, but we also narrow the classification problem from, in this case, a 10-class to a 2-class problem. More generally, we can identify a region-of-interest (ROI) within which one can carry out further classification.

The Labeling of Cortical Sulci using Multidimensional Scaling

Abstract. Statistical shape models (SSM) capture the variation of shape across a population, in order to allow further analysis. Previous work demonstrates that deformation fields contain global transformation components, even if global pre-
registration is performed. It is crucial to construction of SSMs to remove these global transformation components from the local deformations - thus obtaining minimal deformations - prior to using these as input for SSM construction. In medical image processing, parameterized SSMs based on control points of free-form deformations (FFD) are a popular choice, since they offer several advantages compared to SSMs based on dense deformation ﬁelds. In this work, we extend the previous approach by presenting a framework for construction of both, unparameterized and FFD-based SSMs from minimal deformations. The core of the method is computation of minimal deformations by extraction of the linear part from the original dense deformations. For FFD-based SSMs, the FFD-parameterization of the minimal deformations is performed by projection onto the space of FFDs. Both steps are computed by close-form solutions optimally in the least-square sense. The proposed method is evaluated on a data set of 62 MR images of the corpus callosum. The results show a significant improvement achieved by the proposed method for SSMs built on dense ﬁelds, as well as on FFD-based SSMs.

Construction of Statistical Shape Models from Minimal Deformations

We propose a new clustering algorithm. This algorithm performs clustering and manifold learning simultaneously by using a graph-theoretical approach to manifold learning. We apply this algorithm in order to cluster white matter ﬁber tracts obtained fromDiffusion TensorMRI (DT-MRI) through streamline tractography. Our algorithm is able perform clustering of these fiber tracts incorporating information about the shape of the ﬁber and a priori knowledge as the probability of the fiber belonging to known anatomical structures. This anatomical knowledge is incorporated as a volumetric white matter atlas, in this case LONI's ICBM DTI-81

Simultaneous Manifold Learning and Clustering: Grouping White Matter Fiber Tracts Using a Volumetric White Matter Atlas

The ﬁeld of computational anatomy has developed rigorous frameworks for analyzing anatomical shape, based on diffeomorphic transformations of a template. However, differences in algorithms used for template warping, in regularization parameters, and in the template itself, lead to different representations of the same anatomy. Variations of these parameters are considered as confounding factors. Recently, extensions of the conventional computational anatomy framework to account for such confounding variations has shown that learning the equivalence class derived from the multitude of representations can lead to improved and more stable morphological descriptors. Herein, we follow that approach, estimating the morphological appearance manifold obtained by varying parameters of the template warping procedure. Our approach parallels work in the computer vision ﬁeld, in which variations lighting, pose and other parameters leads to image appearance manifolds representing the exact same ﬁgure in different ways. The proposed framework is then used for groupwise registration and statistical analysis of biomedical images, by employing a minimum variance criterion to perform manifold-constrained optimization, i.e. to traverse each individual’s morphological appearance manifold until all individuals' representations come as close to each other as possible. Eﬀectively,
this process removes the aforementioned confounding eﬀects and potentially leads to morphological representations reﬂecting purely biological variations, instead of variations introduced by modeling assumptions and parameter settings. The nonlinearity of a morphological appearance manifold is treated via local approximations of the manifold via PCA.

Morphological Appearance Manifolds in Computational Anatomy: Groupwise Registration and Morphological Analysis

This paper describes the setup of a segmentation competition for the automatic extraction of Multiple Sclerosis (MS) lesions from brain Magnetic Resonance Imaging (MRI) data. This competition is one of three competitions that make up a comparison workshop at the 2008 Medical Image Computing and Computer Assisted Intervention (MICCAI) conference and was modeled after the successful comparison workshop on liver and caudate segmentation at the 2007 MICCAI conference. In this paper, the rationale for organizing the competition is discussed, the training and test data sets for both segmentation tasks are described and the scoring system used to evaluate the segmentation is presented.