This paper proposes a new method for fully automated multiple sclerosis (MS) lesion segmentation in cranial magnetic resonance (MR) imaging. The algorithm uses the T1-weighted and the fluid attenuation inversion recovery scans. It is based the K-Nearest Neighbor (KNN) classification technique. The data has been acquired at the Children�s Hospital Boston (CHB) and the University of North Carolina (UNC). Manual segmentations, composed by a human expert of the CHB, were used for training of the KNN-classification. The method uses voxel location and signal intensity information for determination of the probability being a lesion per voxel, thus generating probabilistic segmentation images. By applying a threshold on the probabilistic images binary segmentations are derived. Automatic segmentations were performed on a set of testing images, and compared with manual segmentations from a CHB and a UNC expert rater. Furthermore, a combined segmentation was composed from segmentations from different algorithms, and used for evaluation. The proposed method shows good resemblance with the segmentations of the CHB rater. High specificity and lower specificity has been observed in comparison with the combined segmentations. Over- and undersegmentation can be easily corrected in this procedure by varying the threshold on the probabilistic segmentation image. The proposed method offers an automated and fully reproducible approach that accurate and applicable on standard clinical MR images.

Automated MS-Lesion Segmentation by K-Nearest Neighbor Classification

This document examines the application of a new parametric method on the segmentation of MS 
lesions in brain sMRI, as applied to the data provided for the MS Lesion Segmentation Challenge at 
MICCAI 2008. The method uses the vector image joint histogram, built over a training set, as an explicit 
model of the feature vectors indicating lesion. The histogram is used to predict lesions in the test data by 
labeling feature vectors consistent with lesion feature vectors in the training set. The results are evaluated 
using STAPLE to compare against two separate human raters.

3D Segmentation In The Clinic: A Grand Challenge II at MICCAI 2008 - MS Lesion Segmentation

In this paper, we present a new automatic robust algorithm to segment multimodal brain MR images with Multiple Sclerosis (MS) lesions. The method performs tissue classification using a Hidden Markov Chain (HMC) model and detects MS lesions as outliers to the model. For this aim, we use the Trimmed Likelihood Estimator (TLE) to extract outliers. Furthermore, neighborhood information is included using the HMC model and we propose to incorporate a priori information brought by a probabilistic atlas.

MS Lesion Segmentation based on Hidden Markov Chains

Multiple sclerosis diagnosis and patient follow-up can be helped by an evaluation of the lesion load in MRI sequences. A lot of automatic methods to segment these lesions are available in the literature. The MICCAI workshop Multiple Sclerosis (MS) lesion segmentation Challenge 08 allows to test and compare these algorithms. This paper presents a method designed to detect hyperintense signal area on T2-FLAIR sequence and its results on the Challenge test data. The proposed algorithm uses only three conventional MRI sequences: T1, T2 and T2-FLAIR. First, images are cropped, spatially unbiased and skull-stripped. A segmentation of the brain into its different compartments is performed on the T1 and the T2 sequences. From these segmentations, a threshold for the T2-FLAIR sequence is automatically computed. Then postprocessing operations select the most plausible lesions in the obtained hyperintense signals. Global result on the test data (80/100) is close to the inter-expert variability (90/100).

An Automatic Segmentation of T2-FLAIR Multiple Sclerosis Lesions

At the German Heart Center Munich we have installed and evaluated a novel system for robotic surgery. Its main features are the
incorporation of haptics (by means of strain gauge sensors at the instruments) and partial automation of surgical tasks. However,
in this paper we focus on the software engineering aspects of the system. We present a hierarchical approach, which is inspired by the
modular architecture of the hardware. Each component of the system, and therefore each component of the control software can be
easily interchanged by another instance (e.g. different types of robots may be employed to carry the surgical instruments). All
operations are abstracted by an intuitive user interface, which provides a high level of transparency. In addition, we have included
techniques known from character animation (so called key-framing) in order to enable operation of the system by users with non-engineering
backgrounds. The introduced concepts have proven effective during an extensive evaluation with 30 surgeons. Thereby, the system was
used to conduct simplified operations in the field of heart surgery, including the replacement of a papillary tendon and the occlusion
of an atrial septal defect.

Software Architecture of a System for Robotic Surgery

Multiple Sclerosis (MS) is a neurodegenerative disease that is associated with
brain tissue damage primarily observed as white matter abnormalities such
as lesions.
We present a novel, fully automatic segmentation method for MS lesions in brain
MRI
that combines outlier detection and region partitioning.
The method is based on an atlas of healthy subjects and detects lesions
as outliers, without requiring the use of training data with segmented lesions.
In order to segment lesions as spatially coherent objects and avoid spurious
lesion detection,
we perform classification on regions (connected groups of voxels)
instead of individual voxels.
Each voxel location is assigned to a region that would maximize overall
relative entropy or Kullback-Leibler divergence between neighboring regions.
Our proposed method is fully automatic and does not require manual selection
or outlining of specific brain regions.
The method can also be adapted to MR images obtained from different scanners
and scanning parameters as it requires no training.

Automatic MS Lesion Segmentation by Outlier Detection and Information Theoretic Region Partitioning

We propose an open source and cross platform medical image viewer, named VV, designed for qualita tive evaluation of images registration, in particular for deformable registration of 4D CT images. VV can display multiple spatio-temporal image sequences (2D+t or 3D+t) and contains several tools for comparing images using transparency or fusion, for visualizing deformation ﬁelds, for deﬁning landmarks. It is used in the ﬁeld of radiation therapy to help researchers and clinicians evaluate deformation in 4D CT images of the thorax. It is implemented in C++, with ITK, VTK and QT open source version, runs on Linux and windows and is freely available to the community.

VV: a viewer for the evaluation of 4D image registration

Ultrasound has become a very important modality in image-guided therapy. At present, however, the collection, synchronization and transfer of ultrasonic images are more cumbersome than necessary. This paper presents a reusable solution to these problems. We propose a software package called SynchroGrab, which allows the collection of interventional ultrasound images as well as their synchronization with a stream of pose measurements. The software includes support for an open-interface ultrasound system, namely the Sonix RP, from Ultrasonix (Vancouver, Canada). Using an open-interface system like the Sonix RP allows customization of the imaging process and the capture of the ultrasound images directly from memory without the need for a frame-grabbing card. Pose measurement is currently performed with an Optotrak Certus by Northern Digital (Waterloo, Canada). However, extensibility was a primary goal in the design of this software, so the support of new devices can be achieved simply by sub-classing the relevant base class. SynchroGrab also performs reconstruction of 3D ultrasound volumes from synchronized data streams. Moreover, the recorded images, volumes and tracking information are available for visualization or further processing either directly from the file system or from a network connection compliant with the OpenIGTLink protocol, which is supported by Slicer 3.

An Open-Source Solution for Interactive Acquisition, Processing and Transfer of Interventional Ultrasound Images

This document describes a novel scheme for the automated extraction of the central lumen lines of
coronary arteries from computed tomography angiography (CTA) data. The scheme ﬁrst obtains a seg-
mentation of the whole coronary tree and subsequently extracts the centerlines from this segmentation.
The ﬁrst steps of the segmentation algorithm consist of the detection of the aorta and the entire heart
region. Next, candidate coronary artery components are detected in the heart region after the masking of
the cardiac blood pools. Based on their location and geometrical properties the structures representing
the right and left arterties are selected from the candidate list. Starting from the aorta, connections
between these structures are made resulting in a ﬁnal segmentation of the whole coronary artery tree, A
fast-marching level set method combined with a backtracking algorithm is employed to obtain the initial
centerlines within this segmentation. For all vessels a curved multiplanar reformatted image (CMPR) is
constructed and used to detect the lumen contours. The ﬁnal centerline was then deﬁned by determining
the center of gravity of the detected lumen in the transversal CMPR slices.
Within the scope of the MICCAI Challenge "Coronary Artery Tracking 2008", the coronary tree
segmentation and centerline extraction scheme was used to automatically detect a set of centerlines in
24 datasets. For 8 data sets reference centerlines were available. This training data was used during
the development and tuning of the algorithm. Sixteen other data sets were provided as testing data.
Evaluation of the proposed methodology was performed through submission of the resulting centerlines
to the MICCAI Challenge website

Connected Component and Morphology Based Extraction of Arterial Centerlines of the Heart (CocomoBeach)

We have created open-source Simulink block libraries for ITK and VTK that allow pipelines for these toolkits to be built in a visual, drag-and-drop style within MATLAB. Each block contains an instance of an ITK or VTK class. Any block connections and parameters that the user makes within MATLAB's Simulink visual environment are converted into connections and parameters for the ITK and VTK pipelines. In addition, we provide conversion of images to and from MATLAB arrays to allow MATLAB image processing blocks to be mixed with ITK and VTK blocks. The code for our block libraries is generated automatically from XML descriptions of the inputs, outputs, and parameters of the ITK and VTK classes. We have used these block libraries to build some example pipelines and believe that they will be useful for developing applications in image analysis and image-guided therapy.