LitCovid-sentences  

PMC:7473770 / 39071-43850 JSONTXT 9 Projects

Annnotations TAB TSV DIC JSON TextAE

Id Subject Object Predicate Lexical cue
T307 0-55 Sentence denotes Phase retrieval, image reconstruction, and segmentation
T308 57-91 Sentence denotes Phase retrieval and reconstruction
T309 92-440 Sentence denotes Phase retrieval was performed from dark and empty beam corrected holograms, using both linearised single step CTF-approach (Cloetens et al., 1999; Turner et al., 2004), and non-linear generalizations of the CTF-method based on Tikhonov regularization (NL-CTF), using our code package HoloTomoToolbox as described and deposited (Lohse et al., 2020).
T310 441-562 Sentence denotes Importantly, both CTF and NL-CTF implementations can be augmented by imposing support and range constraints, when needed.
T311 563-721 Sentence denotes When available, projections recorded at several defocus distances were first aligned with sub-pixel accuracy and then used for multi-distance phase retrieval.
T312 722-890 Sentence denotes For these purposes, the HoloTomoToolbox provides auxiliary functions, which also help to refine the Fresnel number or to correct for drift in the illumination function.
T313 891-1195 Sentence denotes After phase reconstruction of all projections, the tomographic reconstruction was carried out with the MATLAB implemented iradon-function (Ram-Lak filter) for the parallel geometry and with the FDK-function of the ASTRA toolbox (van Aarle et al., 2015; van Aarle et al., 2016) for the cone beam geometry.
T314 1196-1523 Sentence denotes Hot pixel and detector sensitivity variations as well as strong phase features in the parallel beam illumination resulting from upstream window materials, which persist after empty beam correction, can all result in ring artifacts in the tomographic reconstruction, in particular as these flaws can increase by phase retrieval.
T315 1524-1692 Sentence denotes To correct for this, the extra information provided by 360° scans was used to mask out the corresponding pixels and replace them with values of the opposing projection.
T316 1693-1800 Sentence denotes Stitching of reconstructions from different tomographic scans was performed using (Miettinen et al., 2019).
T317 1801-1868 Sentence denotes Resolution estimates were obtained by FSC analysis, see Appendix 2.
T318 1870-1907 Sentence denotes Image segmentation and quantification
T319 1908-2009 Sentence denotes For each patient the stitched overview scans were analyzed with regard to structural characteristics.
T320 2010-2232 Sentence denotes The 3D-reconstructions were first binned (2 × 2 × 2), and the tissue was then segmented from the surrounding paraffin using the segmentation software Ilastik (Berg et al., 2019), which was then further refined with MATLAB.
T321 2233-2371 Sentence denotes In order to exclude single macrophages or detached tissue, only voxels connected to the tissue block were considered for the distance map.
T322 2372-2487 Sentence denotes Further, the areas of the paraffin which represent air compartments were linked to the outside of the tissue block.
T323 2488-2583 Sentence denotes Individual self-contained areas of paraffin (not connected to air) were excluded from the mask.
T324 2584-2701 Sentence denotes Based on this segmentation, the distance to the nearest voxel containing oxygen was calculated for each tissue voxel.
T325 2702-2774 Sentence denotes The tissue volume V is given by the sum of all voxels containing tissue.
T326 2775-2865 Sentence denotes The surface area SA is defined by all tissue voxels with a distance of 1 pixel to the air.
T327 2866-2971 Sentence denotes From this information we calculated the specific surface(2) SV=SA/Vwith S⁢A/V surface area volume -ratio.
T328 2972-3080 Sentence denotes Further a characteristic length(3) Lc=V/SA⋅vxwith v⁢x edge length of a voxel was determined for each sample.
T329 3081-3216 Sentence denotes Additionally, the mean distance (dO2¯) from all tissue voxels to the closest air compartment and its standard deviation was calculated.
T330 3217-3355 Sentence denotes Hyaline membranes and capillary networks were extracted using semi-manual segmentation functions in Avizo (Thermo Fisher Scientific, USA).
T331 3356-3511 Sentence denotes Beside hyaline membranes and the capillary network, different cell types can be readily identified based on the 3D reconstructions of the electron density.
T332 3512-3622 Sentence denotes In particular, inflammatory cell subpopulations - that is, macrophages and lymphocytes - can be distinguished.
T333 3623-3736 Sentence denotes To this end, an automatic and parameter-controlled algorithm denoted as BlobFinder was used (arivis AG, Germany).
T334 3737-3880 Sentence denotes Based on its segmentation output, the amount and position of the lymphocytes in the 3D-reconstructions from parallel beam scans was calculated.
T335 3881-3953 Sentence denotes The algorithm is able to identify roundish structures with a given size.
T336 3954-4037 Sentence denotes For the segmentation of the lymphocytes, we chose a characteristic size of 6.5⁢μ⁢m.
T337 4038-4136 Sentence denotes The structures identified in this step also include macrophages and parts of the capillary system.
T338 4137-4285 Sentence denotes For the unbinned datasets, a distinction between lymphocytes and the nuclei of the macrophages was made based on the difference in electron density.
T339 4286-4394 Sentence denotes In the tomographic reconstructions, the lymphocytes appear denser compared to the nuclei of the macrophages.
T340 4395-4535 Sentence denotes Further, nuclei from endothelial cells and parts of the capillaries filled with blood residues were excluded based on their elongated shape.
T341 4536-4601 Sentence denotes Only structures with a sphericity higher than 0.55 were included.
T342 4602-4779 Sentence denotes Based on the segmentation of lymphocytes, the total number of lymphocytes Nl was obtained and the mean concentration of lymphocytes within the lung tissue cl=Nl/V was estimated.