Combining seeded region growing and k-nearest neighbours for the segmentation of routinely acquired spatio-temporal image data.

PURPOSE: The acquisition conditions of medical imaging are often precisely defined, leading to a high homogeneity among different data sets. Nonetheless, outliers or artefacts still appear and need to be reliably detected to ensure a reliable diagnosis. Thus, the algorithms need to handle small sample sizes especially, when working with domain specific imaging modalities. METHODS: In this work, we suggest a pipeline for the detection and segmentation of light pollution in near-infrared fluorescence optical imaging (NIR-FOI), based on a small sample size. NIR-FOI produces spatio-temporal data with two spatial and one temporal dimension. To calculate a two-dimensional light pollution map for the entire image stack, we combine region growing and k-nearest neighbours (kNN), which classifies pixels into fore- and background by its entire temporal component. Thus, decision-making on reduced data is omitted. RESULTS: We achieved a [Formula: see text] score of 0.99 for classifying a data set as light polluted or pollution-free. Additionally, we reached a total [Formula: see text] score of 0.90 for detecting regions of interest within the polluted data sets. Finally, an average Dice's coefficient measuring the segmentation performance over all polluted data sets of 0.80 was accomplished. CONCLUSIONS: A Dice's coefficient of 0.80 for the area segmentation does not seem perfect. However, there are two main factors, besides true prediction errors, lowering the score: Segmentation mistakes on small areas lead to a rapid decrease in the score and labelling errors due to complex data. However, in combination with the light-polluted data set and pollution area detection, these results can be considered successful and play a key role in our general goal: Exploiting NIR-FOI for the early detection of arthritis within hand joints.

An objective, automated and robust scoring using fluorescence optical imaging to evaluate changes in micro-vascularisation indicating early arthritis.

Fluorescence optical imaging technique (FOI) is a well-established and valid method for visualization of changes in micro vascularization at different organ systems. As increased vascularization is an early feature of joint inflammation, FOI is a promising method to assess arthritis of the hands. But usability of the method is limited to the assessors experience as the measurement of FOI is semi-quantitative using an individual grading system such as the fluorescence optical imaging activity score (FOIAS). The goal of the study was to automatically and thus, objectively analyze the measured fluorescence intensity generated by FOI to evaluate the amount of inflammation of each of the subject's joints focusing on the distinction between normal joint status or arthritis in psoriatic arthritis patients compared to healthy volunteers. Due to the heterogeneity of the pathophysiological perfusion of the hands, a method to overcome the absoluteness of the data by extracting heatmaps out of the image stacks is developed. To calculate a heatmap for one patient, firstly the time series for each pixel is extracted, which is then represented by a feature value. Secondly, all feature values are clustered. The calculated cluster values represent the relativity between the different pixels and enable a comparison of multiple patients. As a metric to quantify the conspicuousness of a joint a score is calculated based on the extracted cluster values. These steps are repeated for a total number of three features. With this method a tendency towards a classification into unaffected and inflamed joints can be achieved. However, further research is necessary to transform the tendency into a robust classification model.

Innovative Imaging Technique for Visualization of Vascularization and Established Methods for Detection of Musculoskeletal Inflammation in Psoriasis Patients.

Psoriasis (PsO) is one of the common chronic inflammatory skin diseases. Approximately 3% of the European Caucasian population is affected. Psoriatic arthritis (PsA) is a chronic immune-mediated disease associated with PsO characterized by distinct musculoskeletal inflammation. Due to its heterogeneous clinical manifestations (e.g., oligo- or polyarthritis, enthesitis, dactylitis, and axial inflammation), early diagnosis of PsA is often difficult and delayed. Approximately 30% of PsO patients will develop PsA. The responsible triggers for the transition from PsO only to PsA are currently unclear, and the impacts of different factors (e.g., genetic, environmental) on disease development are currently discussed. There is a high medical need, recently unmet, to specifically detect those patients with an increased risk for the development of clinically evident PsA early to initiate sufficient treatment to inhibit disease progression and avoid structural damage and loss of function or even intercept disease development. Increased neoangiogenesis and enthesial inflammation are hypothesized to be early pathological findings in PsO patients with PsA development. Different disease states describe the transition from PsO to PsA. Two of those phases are of value for early detection of PsA at-risk patients to prevent later development of PsA as changes in biomarker profiles are detectable: the subclinical phase (soluble and imaging biomarkers detectable, no clinical symptoms) and the prodromal phase (imaging biomarkers detectable, unspecific musculoskeletal symptoms such as arthralgia and fatigue). To target the unmet need for early detection of this at-risk population and to identify the subgroup of patients who will transition from PsO to PsA, imaging plays an important role in characterizing patients precisely. Imaging techniques such as ultrasound (US), magnetic resonance imaging (MRI), and computerized tomography (CT) are advanced techniques to detect sensitively inflammatory changes or changes in bone structure. With the use of these techniques, anatomic structures involved in inflammatory processes can be identified. These techniques are complemented by fluorescence optical imaging as a sensitive method for detection of changes in vascularization, especially in longitudinal measures. Moreover, high-resolution peripheral quantitative CT (HR-pQCT) and dynamic contrast-enhanced MRI (DCE-MRI) may give the advantage to identify PsA-related early characteristics in PsO patients reflecting transition phases of the disease.