Optimized Spectrometers Characterization Procedure for Near Ground Support of ESA FLEX Observations: Part 1 Spectral Calibration and Characterisation

The paper presents two procedures for the wavelength calibration, in the oxygen telluric absorption spectral bands (O2-A, λc = 687 nm and O2-B, λc = 760.6 nm), of field fixed-point spectrometers used for reflectance and Sun-induced fluorescence measurements. In the first case, Ne and Ar pen-type spectral lamps were employed, while the second approach is based on a double monochromator setup. The double monochromator system was characterized for the estimation of errors associated with different operating configurations. The proposed methods were applied to three Piccolo Doppio-type systems built around two QE Pros and one USB2 + H16355 Ocean Optics spectrometers. The wavelength calibration errors for all the calibrations performed on the three spectrometers are reported and potential methodological improvements discussed. The suggested calibration methods were validated, as the wavelength corrections obtained by both techniques for the QE Pro designed for fluorescence investigations were similar. However, it is recommended that a neon emission line source, as well as an argon or mercury-argon source be used to have a reference wavelength closer to the O2-B feature. The wavelength calibration can then be optimised as close to the O2-B and O2-A features as possible. The monochromator approach could also be used, but that instrument would need to be fully characterized prior to use, and although it may offer a more accurate calibration, as it could be tuned to emit light at the same wavelengths as the absorption features, it would be more time consuming as it is a scanning approach

Volumetric Segmentation of Dental CT Data

Hlavným cieľom tejto práce bola segmentácia objemových CT dát za použitia neurónových sietí. Ako vedľajší produkt bol vytvorený nový dataset spolu s silnými aj slabými anotáciami a nástroj pre automatický preprocessing dát. Takisto bola overená možnosť využitia transfer learningu a viacfázového trénovania. Z mnohých vykonaných testov možno vyvodiť záver, že aj tranfer learning aj viacfázové trénovanie mali pozitívny vplyv na vývoj dice skóre v porovnaní so základnou použitou metódou či už pri silných, alebo slabých anotáciách.The main goal of this work was to use neural networks for volumetric segmentation of dental CBCT data. As a byproducts, both new dataset including sparse and dense annotations and automatic preprocessing pipeline were produced. Additionally, the possibility of applying transfer learning and multi-phase training in order to improve segmentation results was tested. From the various tests that were carried out, conclusion can be drawn that both multi-phase training and transfer learning showed substantial improvement in dice score for both sparse and dense annotations compared to the baseline method.