Blake C. Lucas

The Java Image Science Toolkit (JIST) for Rapid Prototyping and Publishing of Neuroimaging Software

Non-invasive neuroimaging techniques enable extraordinarily sensitive and specific in vivo study of the structure, functional response and connectivity of biological mechanisms. With these advanced methods comes a heavy reliance on computer-based processing, analysis and interpretation. While the neuroimaging community has produced many excellent academic and commercial tool packages, new tools are often required to interpret new modalities and paradigms. Developing custom tools and ensuring interoperability with existing tools is a significant hurdle. To address these limitations, we present a new framework for algorithm development that implicitly ensures tool interoperability, generates graphical user interfaces, provides advanced batch processing tools, and, most importantly, requires minimal additional programming or computational overhead. Java-based rapid prototyping with this system is an efficient and practical approach to evaluate new algorithms since the proposed system ensures that rapidly constructed prototypes are actually fully-functional processing modules with support for multiple GUI’s, a broad range of file formats, and distributed computation. Herein, we demonstrate MRI image processing with the proposed system for cortical surface extraction in large cross-sectional cohorts, provide a system for fully automated diffusion tensor image analysis, and illustrate how the system can be used as a simulation framework for the development of a new image analysis method. The system is released as open source under the Lesser GNU Public License (LGPL) through the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC).

Multi-Object geodesic active contours (MOGAC)

An emerging topic is to build image segmentation systems that can segment hundreds to thousands of objects (i.e. cell segmentation\tracking, full brain parcellation, full body segmentation, etc.). Multi-object Level Set Methods (MLSM) perform this task with the benefit of sub-pixel precision. However, current implementations of MLSM are not as computationally or memory efficient as their region growing and graph cut counterparts which lack sub-pixel precision. To address this performance gap, we present a novel parallel implementation of MLSM that leverages the sparse properties of the algorithm to minimize its memory footprint for multiple objects. The new method, Multi-Object Geodesic Active Contours (MOGAC), can represent N objects with just two functions: a label mask image and unsigned distance field. The time complexity of the algorithm is shown to be O((M (power)d)/P) for M (power)d pixels and P processing units in dimension d = {2,3}, independent of the number of objects. Results are presented for 2D and 3D image segmentation problems.

An Active Contour Method for Bone Cement Reconstruction From C-Arm X-Ray Images

A novel algorithm is presented to segment and reconstruct injected bone cement from a sparse set of X-ray images acquired at arbitrary poses. The sparse X-ray multi-view active contour (SxMAC-pronounced “smack”) can 1) reconstruct objects for which the background partially occludes the object in X-ray images, 2) use X-ray images acquired on a noncircular trajectory, and 3) incorporate prior computed tomography (CT) information. The algorithms inputs are preprocessed X-ray images, their associated pose information, and prior CT, if available. The algorithm initiates automated reconstruction using visual hull computation from a sparse number of X-ray images. It then improves the accuracy of the reconstruction by optimizing a geodesic active contour. Experiments with mathematical phantoms demonstrate improvements over a conventional silhouette based approach, and a cadaver experiment demonstrates SxMACs ability to reconstruct high contrast bone cement that has been injected into a femur and achieve sub-millimeter accuracy with four images.

Spring Level Sets: A Deformable Model Representation to Provide Interoperability between Meshes and Level Sets

A new type of deformable model is presented that merges meshes and level sets into one representation to provide interoperability between methods designed for either. This includes the ability to circumvent the CFL time step restriction for methods that require large step sizes. The key idea is to couple a constellation of disconnected triangular surface elements (springls) with a level set that tracks the moving constellation. The target application for Spring Level Sets (SpringLS) is to implement comprehensive imaging pipelines that require a mixture of deformable model representations to achieve the best performance. We demonstrate how to implement key components of a comprehensive imaging pipeline with SpringLS, including image segmentation, registration, tracking, and atlasing.

Multi-object spring level sets (MUSCLE)

A new data structure is presented for geometrically modeling multi-objects. The model can exhibit elastic and fluid-like behavior to enable interpretability between tasks that require both deformable registration and active contour segmentation. The data structure consists of a label mask, distance field, and springls (a constellation of disconnected triangles). The representation has sub-voxel precision, is parametric, re-meshes, tracks point correspondences, and guarantees no self-intersections, air-gaps, or overlaps between adjacent structures. In this work, we show how to apply existing registration algorithms and active contour segmentation to the data structure; and as a demonstration, the data structure is used to segment cortical and subcortical structures (74 total) in the human brain.

Sensor and Sampling-based motion planning for minimally invasive robotic exploration of osteolytic lesions

This paper develops a sensor- and sampling-based motion planner to control a surgical robot in order to explore osteolytic lesions in orthopedic surgery. Because of the difficulty of using conventional surgical tools, such exploration is needed in minimally-invasive treatments of “particle diseases,” which commonly result from material wear in total hip replacements. Since a geometric model of the osteolytic cavity is not always available, the planner relies only on a robot model that can detect collisions. As such, the planner can work in conjunction with real systems. The planner effectively combines global and local exploration. The global layer determines which regions to explore, while local exploration uses information gain to move the robot tip to positions in the region that increase exploration. Simulation experiments are conducted using a snake-like cannula robot on surgically-relevant osteolytic cavities. As desired in minimally-invasive treatment of osteolysis, performance is measured as the volume explored by the robot tip. The proposed method achieves 83–92% performance rate when compared to methods that require 3D models of osteolytic cavities. Comparisons to sensor-based related work (i.e., no 3D models) show significant improvements in performance.

SpringLS: a deformable model representation to provide interoperability between meshes and level sets

A new type of deformable model is presented that merges meshes and level sets into one representation to provide interoperability between methods designed for either. The key idea is to use a constellation of triangular surface elements (springls) to define a level set. A Spring Level Set (SpringLS) can be interpreted as a mesh or level set and used in place of them in many instances. There is no loss of shape information in the transformation from triangle mesh or level set into SpringLS. As examples, we present results for joint segmentation/spherical mapping of a human brain cortex and atlas/non-atlas segmentation of a pelvis.

Supervised Learning of Anatomical Structures Using Demographic and Anthropometric Information

A supervised learning approach to predict anatomical structures derived from computed tomography (CT) images using demographic and anthropometric information is proposed. The approach applies a dimensionality reduction technique to a training dataset to learn a low-dimensional manifold representing variation of organ geometry or variation of the CT intensities itself, which computes a mapping between a low-dimensional feature vector and the organ geometry or CT volume. Regression analysis is then applied to determine a regression function between the low-dimensional feature coordinates and external measurements of the subjects such as demographic or anthropometric data. Then for an unseen subject, the low-dimensional feature coordinates are predicted from external measurements using the computed regression function. Subsequently, the organ geometry or the CT volume is estimated from the mapping computed in the dimensionality reduction. As an example case, lung shapes and thoracic CT scans were analyzed based on available demographic parameters (age, gender, race) and anthropometric measurements (height, weight, and chest dimensions). The training dataset consisted of lung shapes represented as a topologically consistent point distribution model (PDM) and CT volumes (\(256^{3}\,\mathrm{voxels}, 1.5^{3}\,\mathrm{mm}/\mathrm{voxel}\)) of 124 subjects. The prediction error of lung shape of an unknown subject based on 11 independent demographic and anthropometric variables was \(10.71 \pm 5.48\,\mathrm{mm}\). Isomap analysis of CT volumes revealed that 95 % of the total variance was explained with 4 dimensions, and the computed mapping clearly captured trends in anatomical variation. This suggested a potential for a direct CT-volume based statistical analysis using dimensionality reduction, which we call a voxel-based statistical atlas. Potential application areas of the proposed approach includes subject-specific ergonomic design in personal protective equipment or population-specific finite-element modeling in biomechanical analysis. Examples also include the use of a predicted patient-specific CT volume as it a prior information for image quality improvement in low dose CT, and optimization of CT scanning protocols.