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Suyash Prakash Awate¹, Hui Zhang¹, James C. Gee¹

¹University of Pennsylvania, Philadelphia, USA

We present novel methods for statistically modeling and segmenting fiber bundles in diffusion-tensor images.

Ting Song¹, Henry Rusinek², Qun Chen², Louisa Bokacheva², Jeff L. Zhang², Andrew F. Laine¹, Vivian S. Lee²

¹Columbia University, New York, New York, USA; ²NYU School of Medicine, New York, New York, USA

A novel 4D segmentation framework based on a temporal dynamics 4D level set requires less than one minute to automatically segment a 4D data set with more than 40 time points. The novelty of the method is that it combines both information from spatial anatomical structures and temporal dynamics from the time axis. The performance of the fully automatic 4D level set algorithm was found to be comparable to manual segmentation performed by two experts in renal anatomy.

Chunming Li¹, Li Wang², Chiu-Yen Kao³, Zhaohua Ding¹, John C. Gore¹

¹Vanderbilt University, Nashville, Tennessee, USA; ²Nanjing University of Science and Technology, Nanjing, People's Republic of China; ³The Ohio State University, Columbus, Ohio, USA

Intensity inhomogeneity is often seen in MR images, and these cause considerable difficulties in applying existing image segmentation algorithms. In this work, we propose new multiphase level set method for brain MR images segmentation. The proposed method is able to segment images with intensity inhomogeneities, without the bias field correction. Our method has been applied to brain MR images at 3T with promising results.

Hassan Bagher-Ebadian¹, ², Kourosh Jafari-Khouzani¹, Panayiotis D. Mitsias¹, Michael Chopp¹, ³, James R. Ewing¹, ³

¹Henry Ford Hospital, Detroit, Michigan, USA; ²Amir-Kabir University of Technology, Tehran, Iran; ³Oakland University, Rochester, Michigan, USA

Experimental and clinical studies indicate that the likelihood for progression to infarction in the penumbra of physiologically impaired but potentially salvageable tissue surrounding in stroke ischemia is the most important factor in evaluating treatment efficacy. Thus, a multi-parametric analysis that increases the ability of investigators to characterize ischemic penumbra in the early stages of stroke may have a profound clinical significance.  In this study, Kohonen’s Multi-Parametric Self Organizing Map (KMP-SOM) technique was used to detect the ischemic penumbra using MR acute information (T1 pre-contrast, T2, Diffusion-Weighted and proton density). Considering the DW and T2 lesions, the KMP-SOM maps have distinguished the penumbra and core of the lesions much better than ISODATA.  We conclude that a KMP-SOM is capable of predicting the size and pattern of ischemic penumbra, from MR acute information and such modeling may play an important role in the assessment of subacute therapeutic interventions in the treatment of stroke.

Niranjan Bhaskar Joshi¹, Sarah Bond², Michael Brady¹

¹University of Oxford, Oxford, UK; ²Siemens Molecular Imaging, Oxford, UK

Relative distance of tumour from the mesorectal fascia (MF) in colorectal MR images provides important information about the stage of the cancer and likely success of the surgery. We present an image segmentation algorithm to estimate the position of the MF and size and location of the tumour using computer automated methods. Experiments are performed on oblique T2 weighted MRI dataset collected from 10 patients. Results show that the maximum of the average differences between an expert’s delineation of the MF and that segmented with our algorithm is just over 2 mm, which is equivalent to 3-5 pixels.

Boubakeur Belaroussi¹, Michael O'Sullivan², Fabrice Vincent¹, Chahin Pachai¹

¹Bio-Imaging Technologies S.A.S, Lyon, France; ²Neurologische Klinik, Ludwig-Maximilians-Universität, Munchen, Germany

In this work, we proposed HippoQuant, a new, fast, semi-automatic hippocampus segmentation procedure in 3D T1-weighted MRI images based on user-defined landmarks. From a set of user-defined landmarks, a 3D discrete hippocampus surface model is geometrically deformed using an Iterative Closest Point transform, supplemented by a Thin Plate Spline transform. This step leads to a “binary mask”. In parallel, the 3D MR image is segmented into 3 tissue classes (WM, GM and CSF) to identify CSF or CSF-like structures (uncal sulcus and “black holes”) located within the hippocampus. The output is a “presegmentation mask”. In the final step, the binary and presegmentation masks are merged to generate a hippocampal mask. HippoQuant combined both geometrical and statistical information, leading to potentially dramatic reductions in rater time and interaction, greater reliability, and a technique that is insensitive to variations in hippocampal size and shape, - all - important considerations for application in multi-centers studies.

Graeme Wardlaw¹, ², Raimond Wong¹, ³, Pierre Major¹, ³, Michael D. Noseworthy¹, ²

¹McMaster University, Hamilton, Canada; ²Brain-Body Institute, Hamilton, Canada; ³Oncology, Hamilton, Canada

Typical clinical evaluation of tumour microvasculature utilises dynamic contrast enhanced MRI (dceMRI). However, this approach utilises numerous mathematical models with characteristic physiologic assumptions, often leading to inconclusive results. Alternatively, blood oxygen level dependent (BOLD) magnetic resonance imaging (MRI) is  sensitive to the microvascular environment through fluctuation in the oxyhemoglobin to deoxyhemoglobin ratio. Consequently, quantification of the BOLD signal's temporal complexity using a fractal dimension index allows maps to be generated that are physiologically distinct and provide insight into tumour microvasculature without prior assumption as in dceMRI. This approach to tumour microvascular evaluation is presented using rectal cancer data.

Zhengyi Yang¹, Aleksandra Zapotoczna², Giuseppe Sasso², John Simpson², Gary Cowin¹, Deming Wang¹

¹University of Queensland, Brisbane, Australia; ²The Townsville Hospital, Australia

While contouring the prostate manually, oncologists know the pelvic anatomy (not only the shape of prostate, but also its spatial relationship with other organs, such as bladder, rectum, and seminal versicles) and take the intensity distribution not only within but also surrounding the prostate into account. In order to incorporate this a prior knowledge of the neighbouring intensity distribution into model based segmentation method, Intensity Distribution Shell (IDS) model was proposed in this study. Given a prostate surface, by dilating and shrinking given numbers of voxels, we get two new surfaces enclosing the neighbouring tissues. The volume between these two surfaces is called a shell. The number of voxels is the shell thickness. The IDS model is the integration of a shape model obtained by principal component analysis (PCA) and an intensity distribution model represented by the histogram of the voxels within a shell with predefined shell thickness enclosing the shape surface.

Thomas Buelow¹, Lina Arbash Meinel², Axel Saalbach³, Rafael Wiemker¹, Martin Bergtholdt¹

¹Philips Research Europe, Hamburg, Germany; ²Philips Research North America, Briarcliff Manor, New York, USA; ³Philips Research Europe, Aachen, Germany

Dynamic contrast enhanced breast MRI has been emerging as diagnostic tool. Due to its limited specificity there is a demand for computer aided diagnosis tools for this application. In order to build robust CAD applications yielding understandable and reproducible results, a carefully selected small set of features should be used. In this paper we compare three morphologic features with respect to their robustness against variations in the mass lesion segmentations that are the input to the feature computation step. The newly proposed feature "Normalized Mean Distance to Surface" proves more robust and more specific wrt. to lesion characterization than the common features "sphericality" and "compactness".

Jacobus F.A. Jansen¹, Paul A.M. Hofman¹, Walter H. Backes¹

¹Maastricht University Hospital, Maastricht, Netherlands

White matter lesions (WML) are areas of bright, high signal intensity in the white matter depicted on T2-weighted magnetic resonance imaging. In a population with relatively mild WML, namely patients with localization-related, cryptogenic epilepsy and healthy volunteers, the performance of an automated WML detection algorithm, based on regional intensity evaluation, was assessed. The WML volumes from the automated segmentation method were found to be significantly correlated to the volumes obtained visually by neuroradiological assessment. The automated WML detection algorithm using a regional Z-score analysis can successfully segment and quantify the WML on FLAIR images.