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

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

Automated MS-Lesion Segmentation by K-Nearest Neighbor Classification
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 [...]

3D Segmentation in the Clinic: A Grand  Challenge II: MS lesion segmentation
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This paper describes the setup of a segmentation competition for the automatic extraction of Multiple Sclerosis (MS) lesions from brain Magnetic Resonance Imaging (MRI) data. This competition is one of three competitions that make up a comparison workshop at [...]

Fostering Open Science in Lung Cancer Lesion Sizing with ITK module LSTK
This document describes the latest efforts in integrating the Lesion Sizing Toolkit (LSTK) into ITK v4 as an external/remote module providing an Open Science dashboard website with a large open image archive of lung cancer CT images for LSTK development and [...]

Ensemble segmentation using AdaBoost with application to liver lesion extraction from a CT volume
This paper describes an ensemble segmentation trained by the AdaBoost algorithm, which finds a sequence of weak hypotheses, each of which is appropriate for the distribution on training example, and combines the weak hypotheses by a weighted majority vote. In [...]

Auto-kNN: Brain Tissue Segmentation using Automatically Trained k-Nearest-Neighbor Classification
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In this paper we applied one of our regularly used processing pipelines for fully automated brain tissue segmentation. Brain tissue was segmented in cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM). Our algorithms for skull stripping, tissue [...]

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