Comorbidity clusters in generalized osteoarthritis among female patients: A cross-sectional study.

Objectives: To investigate the prevalence of comorbidities among female patients with generalized osteoarthritis (GOA) in comparison to an age- and sex matched control group. To identify clusters of comorbidities in both groups.

Methods: An observational, cross-sectional study was conducted.

A patient-derived and patient-reported outcome measure for assessing psoriatic arthritis: elaboration and preliminary validation of the Psoriatic Arthritis Impact of Disease (PsAID) questionnaire, a 13-country EULAR initiative.

Introduction: The objective was to develop a questionnaire that can be used to calculate a score reflecting the impact of psoriatic arthritis (PsA) from the patients' perspective: the PsA Impact of Disease (PsAID) questionnaire.

Methods: Twelve patient research partners identified important domains (areas of health); 139 patients prioritised them according to importance. Numeric rating scale (NRS) questions were developed, one for each domain.

Liver segmentation for contrast-enhanced MR images using partitioned probabilistic model.

Purpose: Liver volume segmentation is important in computer assisted diagnosis and therapy planning of liver tumors. Manual segmentation is time-consuming, tedious and error prone, so automated methods are needed. Automatic segmentation of MR images is more challenging than for CT images, so a robust system was developed.

Automatic segmentation of the liver from multi- and single-phase contrast-enhanced CT images.

Segmentation of contrast-enhanced abdominal CT images is required by many clinical applications of computer aided diagnosis and therapy planning. The research on automated methods involves different organs among which the liver is the most emphasized. In the current clinical practice more images (at different phases) are acquired from the region of interest in case of a contrast-enhanced abdominal CT examination.

Comparison and evaluation of methods for liver segmentation from CT datasets.

This paper presents a comparison study between 10 automatic and six interactive methods for liver segmentation from contrast-enhanced CT images. It is based on results from the "MICCAI 2007 Grand Challenge" workshop, where 16 teams evaluated their algorithms on a common database. A collection of 20 clinical images with reference segmentations was provided to train and tune algorithms in advance.