A Semi-Automated Usability Evaluation Framework for Interactive Image Segmentation Systems.

For complex segmentation tasks, the achievable accuracy of fully automated systems is inherently limited. Specifically, when a precise segmentation result is desired for a small amount of given data sets, semi-automatic methods exhibit a clear benefit for the user. The optimization of human computer interaction (HCI) is an essential part of interactive image segmentation. Nevertheless, publications introducing novel interactive segmentation systems (ISS) often lack an objective comparison of HCI aspects. It is demonstrated that even when the underlying segmentation algorithm is the same throughout interactive prototypes, their user experience may vary substantially. As a result, users prefer simple interfaces as well as a considerable degree of freedom to control each iterative step of the segmentation. In this article, an objective method for the comparison of ISS is proposed, based on extensive user studies. A summative qualitative content analysis is conducted via abstraction of visual and verbal feedback given by the participants. A direct assessment of the segmentation system is executed by the users via the system usability scale (SUS) and AttrakDiff-2 questionnaires. Furthermore, an approximation of the findings regarding usability aspects in those studies is introduced, conducted solely from the system-measurable user actions during their usage of interactive segmentation prototypes. The prediction of all questionnaire results has an average relative error of 8.9%, which is close to the expected precision of the questionnaire results themselves. This automated evaluation scheme may significantly reduce the resources necessary to investigate each variation of a prototype's user interface (UI) features and segmentation methodologies.

Registration of vascular structures using a hybrid mixture model.

PURPOSE: Morphological changes to anatomy resulting from invasive surgical procedures or pathology, typically alter the surrounding vasculature. This makes it useful as a descriptor for feature-driven image registration in various clinical applications. However, registration of vasculature remains challenging, as vessels often differ in size and shape, and may even miss branches, due to surgical interventions or pathological changes. Furthermore, existing vessel registration methods are typically designed for a specific application. To address this limitation, we propose a generic vessel registration approach useful for a variety of clinical applications, involving different anatomical regions. METHODS: A probabilistic registration framework based on a hybrid mixture model, with a refinement mechanism to identify missing branches (denoted as HdMM+) during vasculature matching, is introduced. Vascular structures are represented as 6-dimensional hybrid point sets comprising spatial positions and centerline orientations, using Student's t-distributions to model the former and Watson distributions for the latter. RESULTS: The proposed framework is evaluated for intraoperative brain shift compensation, and monitoring changes in pulmonary vasculature resulting from chronic lung disease. Registration accuracy is validated using both synthetic and patient data. Our results demonstrate, HdMM+ is able to reduce more than [Formula: see text] of the initial error for both applications, and outperforms the state-of-the-art point-based registration methods such as coherent point drift and Student's t-distribution mixture model, in terms of mean surface distance, modified Hausdorff distance, Dice and Jaccard scores. CONCLUSION: The proposed registration framework models complex vascular structures using a hybrid representation of vessel centerlines, and accommodates intricate variations in vascular morphology. Furthermore, it is generic and flexible in its design, enabling its use in a variety of clinical applications.

Glioma vessel abnormality quantification using time-of-flight MR angiography.

OBJECTIVES: To differentiate between abnormal tumor vessels and regular brain vasculature using new quantitative measures in time-of-flight (TOF) MR angiography (MRA) data. MATERIALS AND METHODS: In this work time-of-flight (TOF) MR angiography data are acquired in 11 glioma patients to quantify vessel abnormality. Brain vessels are first segmented with a new algorithm, efficient monte-carlo image-analysis for the location of vascular entity (EMILOVE), and are then characterized in three brain regions: tumor, normal-appearing contralateral brain, and the total brain volume without the tumor. For characterization local vessel orientation angles and the dot product between local orientation vectors are calculated and averaged in the 3 regions. Additionally, correlation with histological and genetic markers is performed. RESULTS: Both the local vessel orientation angles and the dot product show a statistically significant difference (p < 0.005) between tumor vessels and normal brain vasculature. Furthermore, the connection to both histology and the gene expression of the tumor can be found-here, the measures were compared to the proliferation marker Ki-67 [MIB] and genome-wide expression analysis. The results in a subgroup indicate that the dot product measure may be correlated with activated genetic pathways. CONCLUSION: It is possible to define a measure of vessel abnormality based on local vessel orientation angles which can differentiate between normal brain vasculature and glioblastoma vessels.

White Matter MS-Lesion Segmentation Using a Geometric Brain Model.

Brain magnetic resonance imaging (MRI) in patients with Multiple Sclerosis (MS) shows regions of signal abnormalities, named plaques or lesions. The spatial lesion distribution plays a major role for MS diagnosis. In this paper we present a 3D MS-lesion segmentation method based on an adaptive geometric brain model. We model the topological properties of the lesions and brain tissues in order to constrain the lesion segmentation to the white matter. As a result, the method is independent of an MRI atlas. We tested our method on the MICCAI MS grand challenge proposed in 2008 and achieved competitive results. In addition, we used an in-house dataset of 15 MS patients, for which we achieved best results in most distances in comparison to atlas based methods. Besides classical segmentation distances, we motivate and formulate a new distance to evaluate the quality of the lesion segmentation, while being robust with respect to minor inconsistencies at the boundary level of the ground truth annotation.

Non-Parametric Bayesian Registration (NParBR) of Body Tumors in DCE-MRI Data.

The identification of tumors in the internal organs of chest, abdomen, and pelvis anatomic regions can be performed with the analysis of Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) data. The contrast agent is accumulated differently by pathologic and healthy tissues and that results in a temporally varying contrast in an image series. The internal organs are also subject to potentially extensive movements mainly due to breathing, heart beat, and peristalsis. This contributes to making the analysis of DCE-MRI datasets challenging as well as time consuming. To address this problem we propose a novel pairwise non-rigid registration method with a Non-Parametric Bayesian Registration (NParBR) formulation. The NParBR method uses a Bayesian formulation that assumes a model for the effect of the distortion on the joint intensity statistics, a non-parametric prior for the restored statistics, and also applies a spatial regularization for the estimated registration with Gaussian filtering. A minimally biased intra-dataset atlas is computed for each dataset and used as reference for the registration of the time series. The time series registration method has been tested with 20 datasets of liver, lungs, intestines, and prostate. It has been compared to the B-Splines and to the SyN methods with results that demonstrate that the proposed method improves both accuracy and efficiency.

Bi-modal Non-rigid Registration of Brain MRI Data with Deconvolution of Joint Statistics.

A brain MRI protocol typically includes several imaging contrasts that can provide complementary information by highlighting different tissue properties. The acquired datasets often need to be co-registered or placed in a standard anatomic space before any further processing. Current registration methods particularly for multicontrast data are computationally very intensive, their resolution is lower than that of the images, and their distance metric and its optimization can be limiting. In this work a novel and effective non-rigid registration method is proposed that is based on the restoration of the joint statistics of pairs of such images. The registration is performed with the deconvolution of the joint statistics with an adaptive Wiener filter. The deconvolved statistics are forced back to the spatial domain to estimate a preliminary registration. The spatial transformation is also regularized with Gaussian spatial smoothing. The registration method has been compared with the B-Splines method implemented in 3DSlicer and with the SyN method implemented in the ANTs toolkit. The validation has been performed with a simulated Shepp-Logan phantom, a BrainWeb phantom, the real data of the NIREP database, and real multi-contrast datasets of healthy volunteers. The proposed method has shown improved comparative accuracy as well as analytical efficiency.