Tina Kapur

Segmentation of Brain Tissue from Magnetic Resonance Images

Segmentation of medical imagery is a challenging problem due to the complexity of the images, as well as to the absence of models of the anatomy that fully capture the possible deformations in each structure. The brain is a particularly complex structure, and its segmentation is an important step for many problems, including studies in temporal change detection of morphology, and 3-D visualizations for surgical planning. We present a method for segmentation of brain tissue from magnetic resonance images that is a combination of three existing techniques from the computer vision literature: expectation/maximization segmentation, binary mathematical morphology, and active contour models. Each of these techniques has been customized for the problem of brain tissue segmentation such that the resultant method is more robust than its components. Finally, we present the results of a parallel implementation of this method on IBMs supercomputer Power Visualization System for a database of 20 brain scans each with 256 x 256 x 124 voxels and validate those results against segmentations generated by neuroanatomy experts.

OpenIGTLink: an open network protocol for image-guided therapy environment

With increasing research on system integration for image‐guided therapy (IGT), there has been a strong demand for standardized communication among devices and software to share data such as target positions, images and device status.

A variational framework for integrating segmentation and registration through active contours

Traditionally, segmentation and registration have been solved as two independent problems, even though it is often the case that the solution to one impacts the solution to the other. In this paper, we introduce a geometric, variational framework that uses active contours to simultaneously segment and register features from multiple images. The key observation is that multiple images may be segmented by evolving a single contour as well as the mappings of that contour into each image.

GBM Volumetry using the 3D Slicer Medical Image Computing Platform

Volumetric change in glioblastoma multiforme (GBM) over time is a critical factor in treatment decisions. Typically, the tumor volume is computed on a slice-by-slice basis using MRI scans obtained at regular intervals. (3D)Slicer – a free platform for biomedical research – provides an alternative to this manual slice-by-slice segmentation process, which is significantly faster and requires less user interaction. In this study, 4 physicians segmented GBMs in 10 patients, once using the competitive region-growing based GrowCut segmentation module of Slicer, and once purely by drawing boundaries completely manually on a slice-by-slice basis. Furthermore, we provide a variability analysis for three physicians for 12 GBMs. The time required for GrowCut segmentation was on an average 61% of the time required for a pure manual segmentation. A comparison of Slicer-based segmentation with manual slice-by-slice segmentation resulted in a Dice Similarity Coefficient of 88.43 ± 5.23% and a Hausdorff Distance of 2.32 ± 5.23 mm.

Enhanced Spatial Priors for Segmentation of Magnetic Resonance Imagery

A Bayesian, model-based method for segmentation of Magnetic Resonance images is proposed. A discrete vector valued Markov Random Field model is used as a regularizing prior in a Bayesian classification algorithm to minimize the effect of salt-and-pepper noise common in clinical scans. The continuous Mean Field solution to the MRP is recovered using an Expectation-Maximization algorithm, and is a probabilistic segmentation of the image. A separate model is used to encode the relative geometry of structures, and as a spatially varying prior in the Bayesian classifier. Preliminary results are presented for the segmentation of white matter, gray matter, fluid, and fat in Gradient Echo MR images of the brain.

Utilizing segmented MRI data in image-guided surgery

While the role and utility of Magnetic Resonance Images as a diagnostic tool are well established in current clinical practice, there are a number of emerging medical arenas in which MRI can play an equally important role. In this article, we consider the problem of image-guided surgery, and provide an overview of a series of techniques that we have recently developed in order to automatically utilize MRI-based anatomical reconstructions for surgical guidance and navigation.

3-T MR-guided brachytherapy for gynecologic malignancies.

Gynecologic malignancies are a leading cause of death in women worldwide. Standard treatment for many primary and recurrent gynecologic cancer cases includes external-beam radiation followed by brachytherapy. Magnetic resonance (MR) imaging is beneficial in diagnostic evaluation, in mapping the tumor location to tailor radiation dose and in monitoring the tumor response to treatment. Initial studies of MR guidance in gynecologic brachytherapy demonstrate the ability to optimize tumor coverage and reduce radiation dose to normal tissues, resulting in improved outcomes for patients. In this article, we describe a methodology to aid applicator placement and treatment planning for 3 Tesla (3-T) MR-guided brachytherapy that was developed specifically for gynecologic cancers. This methodology has been used in 18 cases from September 2011 to May 2012 in the Advanced Multimodality Image Guided Operating (AMIGO) suite at Brigham and Womens Hospital. AMIGO comprises state-of-the-art tools for MR imaging, image analysis and treatment planning. An MR sequence using three-dimensional (3D)-balanced steady-state free precession in a 3-T MR scanner was identified as the best sequence for catheter identification with ballooning artifact at the tip. 3D treatment planning was performed using MR images. Items in development include software designed to support virtual needle trajectory planning that uses probabilistic bias correction, graph-based segmentation and image registration algorithms. The results demonstrate that 3-T MR image guidance has a role in gynecologic brachytherapy. These novel developments have the potential to improve targeted treatment to the tumor while sparing the normal tissues.

Fiber tractography based on diffusion tensor imaging compared with high-angular-resolution diffusion imaging with compressed sensing : initial experience

BACKGROUND The most frequently used method for fiber tractography based on diffusion tensor imaging (DTI) is associated with restrictions in the resolution of crossing or kissing fibers and in the vicinity of tumor or edema. Tractography based on high-angular-resolution diffusion imaging (HARDI) is capable of overcoming this restriction. With compressed sensing (CS) techniques, HARDI acquisitions with a smaller number of directional measurements can be used, thus enabling the use of HARDI-based fiber tractography in clinical practice. OBJECTIVE To investigate whether HARDI+CS-based fiber tractography improves the display of neuroanatomically complex pathways and in areas of disturbed diffusion properties. METHODS Six patients with gliomas in the vicinity of language-related areas underwent 3-T magnetic resonance imaging including a diffusion-weighted data set with 30 gradient directions. Additionally, functional magnetic resonance imaging for cortical language sites was obtained. Fiber tractography was performed with deterministic streamline algorithms based on DTI using 3 different software platforms. Additionally, tractography based on reconstructed diffusion signals using HARDI+CS was performed. RESULTS HARDI+CS-based tractography displayed more compact fiber bundles compared with the DTI-based results in all cases. In 3 cases, neuroanatomically plausible fiber bundles were displayed in the vicinity of tumor and peritumoral edema, which could not be traced on the basis of DTI. The curvature around the sylvian fissure was displayed properly in 6 cases and in only 2 cases with DTI-based tractography. CONCLUSION HARDI+CS seems to be a promising approach for fiber tractography in clinical practice for neuroanatomically complex fiber pathways and in areas of disturbed diffusion, overcoming the problem of long acquisition times.

Tegmental and petromastoid defects in the temporal bone

Fifty temporal bones were examined using the temporal bone dissecting microscope. 34 per cent were found to have defects in the tegmen and petromastoid segments, resulting in communications between the cranial cavity and the middle ear cleft. However, no defects were found in the overlying dura. This may have an important bearing on the intracranial spread of infection from the middle ear cleft, even in the absence of any bony destruction due to chronic middle ear disease.

Square-Cut: A Segmentation Algorithm on the Basis of a Rectangle Shape

We present a rectangle-based segmentation algorithm that sets up a graph and performs a graph cut to separate an object from the background. However, graph-based algorithms distribute the graphs nodes uniformly and equidistantly on the image. Then, a smoothness term is added to force the cut to prefer a particular shape. This strategy does not allow the cut to prefer a certain structure, especially when areas of the object are indistinguishable from the background. We solve this problem by referring to a rectangle shape of the object when sampling the graph nodes, i.e., the nodes are distributed non-uniformly and non-equidistantly on the image. This strategy can be useful, when areas of the object are indistinguishable from the background. For evaluation, we focus on vertebrae images from Magnetic Resonance Imaging (MRI) datasets to support the time consuming manual slice-by-slice segmentation performed by physicians. The ground truth of the vertebrae boundaries were manually extracted by two clinical experts (neurological surgeons) with several years of experience in spine surgery and afterwards compared with the automatic segmentation results of the proposed scheme yielding an average Dice Similarity Coefficient (DSC) of 90.97±2.2%.