Filters

2018 - 20192

More filters

2022 - 20232
Reset filters

Settings

End date: 2019 × Start date: 2010 × DDC: 004 × DDC: 610 × Subject: Konferenzschrift ×

Search Results

Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    A Computational Pipeline for Sepsis Patients’ Stratification and Diagnosis
    ([Setúbal, Portugal] : SCITEPRESS - Science and Technology Publications, Lda., 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.
  • Thumbnail Image
    Item
    Semantic segmentation of non-linear multimodal images for disease grading of inflammatory bowel disease: A segnet-based application
    ([Sétubal] : SCITEPRESS - Science and Technology Publications Lda., 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.