James V. Miller

3D Slicer as an image computing platform for the Quantitative Imaging Network

Quantitative analysis has tremendous but mostly unrealized potential in healthcare to support objective and accurate interpretation of the clinical imaging. In 2008, the National Cancer Institute began building the Quantitative Imaging Network (QIN) initiative with the goal of advancing quantitative imaging in the context of personalized therapy and evaluation of treatment response. Computerized analysis is an important component contributing to reproducibility and efficiency of the quantitative imaging techniques. The success of quantitative imaging is contingent on robust analysis methods and software tools to bring these methods from bench to bedside. 3D Slicer is a free open-source software application for medical image computing. As a clinical research tool, 3D Slicer is similar to a radiology workstation that supports versatile visualizations but also provides advanced functionality such as automated segmentation and registration for a variety of application domains. Unlike a typical radiology workstation, 3D Slicer is free and is not tied to specific hardware. As a programming platform, 3D Slicer facilitates translation and evaluation of the new quantitative methods by allowing the biomedical researcher to focus on the implementation of the algorithm and providing abstractions for the common tasks of data communication, visualization and user interface development. Compared to other tools that provide aspects of this functionality, 3D Slicer is fully open source and can be readily extended and redistributed. In addition, 3D Slicer is designed to facilitate the development of new functionality in the form of 3D Slicer extensions. In this paper, we present an overview of 3D Slicer as a platform for prototyping, development and evaluation of image analysis tools for clinical research applications. To illustrate the utility of the platform in the scope of QIN, we discuss several use cases of 3D Slicer by the existing QIN teams, and we elaborate on the future directions that can further facilitate development and validation of imaging biomarkers using 3D Slicer.

Atlas stratification

The process of constructing an atlas typically involves selecting one individual from a sample on which to base or root the atlas. If the individual selected is far from the population mean, then the resulting atlas is biased towards this individual. This, in turn, may bias any inferences made with the atlas. Unbiased atlas construction addresses this issue by either basing the atlas on the individual which is the median of the sample or by an iterative technique whereby the atlas converges to the unknown population mean. In this paper, we explore the question of whether a single atlas is appropriate for a given sample or whether there is sufficient image based evidence from which we can infer multiple atlases, each constructed from a subset of the data. We refer to this process as atlas stratification. Essentially, we determine whether the sample, and hence the population, is multi-modal and is best represented by an atlas per mode. In this preliminary work, we use the mean shift algorithm to identify the modes of the sample and multidimensional scaling to visualize the clustering process on clinical MRI neurological image datasets.

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.

Pulmonary Nodule Volume: Effects of Reconstruction Parameters on Automated Measurements—A Phantom Study

PURPOSE To prospectively evaluate in a phantom the effects of reconstruction kernel, field of view (FOV), and section thickness on automated measurements of pulmonary nodule volume. MATERIALS AND METHODS Spherical and lobulated pulmonary nodules 3-15 mm in diameter were placed in a commercially available lung phantom and scanned by using a 16-section computed tomographic (CT) scanner. Nodule volume (V) was determined by using the diameters of 27 spherical nodules and the mass and density values of 29 lobulated nodules measured by using the formulas V = (4/3)pi r(3) (spherical nodules) and V = 1000 x (M/D) (lobulated nodules) as reference standards, where r is nodule radius; M, nodule mass; and D, wax density. Experiments were performed to evaluate seven reconstruction kernels and the independent effects of FOV and section thickness. Automated nodule volume measurements were performed by using computer-assisted volume measurement software. General linear regression models were used to examine the independent effects of each parameter, with percentage overestimation of volume as the dependent variable of interest. RESULTS There was no substantial difference in the accuracy of volume estimations across the seven reconstruction kernels. The bone reconstruction kernel was deemed optimal on the basis of the results of a series of statistical analyses and other qualitative findings. Overall, volume accuracy was significantly associated (P < .0001) with larger reference standard-measured nodule diameter. There was substantial overestimation of the volumes of the 3-5-mm nodules measured by using the volume measurement software. Decreasing the FOV facilitated no significant improvement in the precision of lobulated nodule volume measurements. The accuracy of volume estimations--particularly those for small nodules--was significantly (P < .0001) affected by section thickness. CONCLUSION Substantial, highly variable overestimation of volume occurs with decreasing nodule diameter. A section thickness that enables the acquisition of at least three measurements along the z-axis should be used to measure the volumes of larger pulmonary nodules.

Imprecision in Automated Volume Measurements of Pulmonary Nodules and Its Effect on the Level of Uncertainty in Volume Doubling Time Estimation

BACKGROUND Detection of small indeterminate pulmonary nodules (4 to 10 mm in diameter) in clinical practice is increasing, largely because of increased utilization and improved imaging technology. Although there currently exists software for CT scan machines that automate nodule volume estimation, the imprecision associated with volume estimates is particularly poor for nodules < or = 6 mm in diameter, with greater imprecision associated with increasing CT scan slice thickness. This study examined the effects of the volume estimation error associated with four CT scan slice thicknesses (0.625, 1.25, 2.50, and 5.00 mm) on estimates of volume doubling time (VDT) for solid nodules of various sizes. METHODS Data reflecting the accuracy of 1,624 automated volume estimations were obtained from experiments incorporating volume estimation software, performed on a commercially available lung phantom. These data informed mathematical simulations that were used to estimate imprecision around VDT estimates for hypothetical pairs of volume estimates for a given solid pulmonary nodule observed at different time points. RESULTS The confidence intervals around the VDT estimates were extremely wide for 2.50- and 5.00-mm slice thicknesses, often encompassing values traditionally associated with both benignity and malignity for simulated 1- and 2-mm growths in diameter. CONCLUSIONS Because of the inaccuracy in automated volume estimation, the confidence a clinician should have in estimating VDT should be highly dependent on the degree of observed growth and on the CT scan slice thickness. The performance of CT scanners with slice thicknesses of > or = 2.5 mm for assessing growth in pulmonary nodules is essentially inadequate for 1-mm changes in nodule diameter.

Model-Based analysis of local shape for lesion detection in CT scans

Thin-slice computer tomography provides high-resolution images that facilitate the diagnosis of early-stage lung cancer. However, the sheer size of the CT volumes introduces variability in radiological readings, driving the need for automated detection systems. The main contribution of this paper is a technique for combining geometric and intensity models with the analysis of local curvature for detecting pulmonary lesions in CT. The local shape at each voxel is represented via the principal curvatures of its associated isosurface without explicitly extracting the isosurface. The comparison of these curvatures to values derived from analytical shape models is then used to label the voxel as belonging to particular anatomical structures, e.g., nodules or vessels. The algorithm was evaluated on 242 CT exams with expert-determined ground truth. The performance of the algorithm is quantified by free-response receiver-operator characteristic curves, as well as by its potential for improvement in radiologist sensitivity.

Effect of the nitric oxide donor, glyceryl trinitrate, on human gall bladder motility

Background—Nitric oxide is a major neurotransmitter in non-adrenergic, non-cholinergic (NANC) pathways. NANC inhibitory innervation has been shown in human gall bladder muscle in vitro; the role of nitric oxide in human gall bladder emptying however is undefined. Aims—To study the effect of glyceryl trinitrate, a nitric oxide donor, on gall bladder emptying in healthy subjects using a randomised, double blind, crossover, placebo controlled design. Methods—Ultrasonographic gall bladder volume was measured in the fasting state in eight healthy volunteers after randomised administration of either glyceryl trinitrate 1200 μg buccal spray or placebo spray. On two further occasions, after randomised administration of either glyceryl trinitrate 1200 μg buccal spray or placebo spray, gall bladder volumes were also measured after a liquid test meal. Results—Glyceryl trinitrate significantly increased fasting gall bladder volume to a mean of 114% (SEM 5%) of pretreatment volume (p=0.039). Glyceryl trinitrate also significantly impaired gall bladder emptying between five and 40 minutes postprandially. Gall bladder ejection fraction was also reduced after glyceryl trinitrate compared with placebo (43 (6.9)% versus 68.4 (6.5)%, p=0.016). Conclusions—This study shows that glyceryl trinitrate produces gall bladder dilatation in the fasting state and reduces postprandial gall bladder emptying, suggesting that nitric oxide mechanisms may be operative in the human gall bladder in vivo.

Lung nodule detection via Bayesian voxel labeling

This paper describes a system for detecting pulmonary nodules in CT images. It aims to label individual image voxels in accordance to one of a number of anatomical (pulmonary vessels or junctions), pathological (nodules), or spurious (noise) events. The approach is orthodoxly Bayesian, with particular care taken in the objective establishment of prior probabilities and the incorporation of relevant medical knowledge. We provide, under explicit modeling assumptions, closed-form expressions for all the probability distributions involved. The technique is applied to real data, and we present a discussion of its performance.

Active learning guided interactions for consistent image segmentation with reduced user interactions

Interactive techniques leverage the expert knowledge of users to produce accurate image segmentations. However, the segmentation accuracy varies with the users. Additionally, users may require training with the algorithm and its exposed parameters to obtain the best segmentation with minimal effort. Our work combines active learning with interactive segmentation and (i) achieves as good or better accuracy as a fully user guided segmentation with significantly lower number of user interactions (on average 50%), and (ii) achieves robust segmentation despite user variability. Our approach interacts with user to suggest placement of gestures. We present extensive experimental evaluation of our results on two different publicly available datasets.

A method for registering diffusion weighted magnetic resonance images

Diffusion weighted magnetic resonance (DWMR or DW) imaging is a fast evolving technique to investigate the connectivity of brain white matter by measuring the self-diffusion of the water molecules in the tissue. Registration is a key step in group analysis of the DW images that may lead to understanding of functional and structural variability of the normal brain, understanding disease process, and improving neurosurgical planning. In this paper, we present a new method for registering DW images. The method works directly on the diffusion weighted images without using tensor reconstruction, fiber tracking, and fiber clustering. Therefore, the performance of the method does not rely on the accuracy and robustness of these steps. Moreover, since all the information in the original diffusion weighted images is used for registration, the results of the method is robust to imaging noise. We demonstrate the method on intra-subject registration with an affine transform using DW images acquired on the same scanner with the same imaging protocol. Extension to deformable registration for images acquired on different scanners and/or with different imaging protocols is also discussed.