2019, Pradhan, Pranita, Meyer, Tobias, Vieth, Michael, Stallmach, Andreas, Waldner, Maximilian, Schmitt, Michael, Popp, Juergen, Bocklitz, Thomas, De Marsico, Maria, Sanniti di Baja, Gabriella, Fred, Ana

Non-linear multimodal imaging, the combination of coherent anti-stokes Raman scattering (CARS), two-photon excited fluorescence (TPEF) and second harmonic generation (SHG), has shown its potential to assist the diagnosis of different inflammatory bowel diseases (IBDs). This label-free imaging technique can support the ‘gold-standard’ techniques such as colonoscopy and histopathology to ensure an IBD diagnosis in clinical environment. Moreover, non-linear multimodal imaging can measure biomolecular changes in different tissue regions such as crypt and mucosa region, which serve as a predictive marker for IBD severity. To achieve a real-time assessment of IBD severity, an automatic segmentation of the crypt and mucosa regions is needed. In this paper, we semantically segment the crypt and mucosa region using a deep neural network. We utilized the SegNet architecture (Badrinarayanan et al., 2015) and compared its results with a classical machine learning approach. Our trained SegNet mod el achieved an overall F1 score of 0.75. This model outperformed the classical machine learning approach for the segmentation of the crypt and mucosa region in our study.

2018, Campos, David, Pinho, Renato, Neugebauer, Ute, Popp, Juergen, Oliveira, José Luis, Zwiggelaar, Reyer, Gamboa, Hugo, Fred, Ana, Bermúdez i Badia, Sergi

Sepsis is still a little acknowledged public health issue, despite its increasing incidence and the growing mortality rate. In addition, a clear diagnosis can be lengthy and complicated, due to highly variable symptoms and non-specific criteria, causing the disease to be diagnosed and treated too late. This paper presents the HemoSpec platform, a decision support system which, by collecting and automatically processing data from several acquisition devices, can help in the early diagnosis of sepsis.

2017, Sieger, Tomáš, Hurley, Catherine B., Fiser, Karel, Beleites, Claudia

Hierarchical cluster analysis is a valuable tool for exploring data by describing their structure using a dendrogram. However, proper visualization and interactive inspection of the dendrogram are needed to unlock the information in the data. We describe a new R package, idendro, that enables the user to inspect dendrograms interactively: to select and color clusters, to zoom and pan the dendrogram, and to visualize the clustered data not only in a built-in heat map, but also in any interactive plot implemented in the cranvas package. A lightweight version idendr0 with reduced dependencies is also available from the Comprehensive R Archive Network.

2019, Geitner, Robert, Fritzsch, Robby, Bocklitz, Thomas W., Popp, Jürgen

In the package corr2D two-dimensional correlation analysis is implemented in R. This paper describes how two-dimensional correlation analysis is done in the package and how the mathematical equations are translated into R code. The paper features a simple tutorial with executable code for beginners, insight into the calculations done before the correlation analysis, a detailed look at the parallelization of the fast Fourier transformation based correlation analysis and a speed test of the calculation. The package corr2D offers the possibility to preprocess, correlate and postprocess spectroscopic data using exclusively the R language. Thus, corr2D is a welcome addition to the toolbox of spectroscopists and makes two-dimensional correlation analysis more accessible and transparent.