Flow Visualization Tools for Image Analysis of Capillary Networks
Bibliographic record
Abstract
OBJECTIVE: Video recordings of red blood cell (RBC) flow through capillary networks contain a considerable amount of information pertaining to oxygen transport through the microcirculation. Image analysis of these video recordings has been widely used to determine RBC dynamics (velocity, lineal density and supply rate) and oxygenation (Brunner et al., 2000; Ellis et al., 1990, 1992; Ellsworth et al., 1987; Klyscz et al., 1997; Pries 1988). However, not all capillaries in a given field of view are suitable for image analysis. Typically, capillary segments that are relatively straight and in sharp focus, and exhibit flow of individual RBCs that are well separated by plasma gaps, are good candidates for analysis. We have developed several image processing tools to aid in the selection of such capillaries for analysis and to obtain quick overviews of RBC flow through the microcirculation. METHODS: Burgess et al. (Microcirc. 2:75, 1995) and Burkell et al. (Annals Biomed. Eng. 24:1, 1996; J. Vasc. Res. 35:2, 1998) have previously introduced mean and variance images to aid in the selection of capillaries for analysis. We have extended their concept and developed similar two dimensional visualization techniques for studies of RBC flow through capillary networks. RESULTS: Five new methods of processing video data were developed. The minimum image highlights all capillaries containing RBCs in a given field of view. The maximum image identifies capillaries that exhibit high lineal density or stopped flow. The range image represents the difference between the maximum and minimum light intensity values that occur at a given pixel over a given time period, and helps to identify capillary segments that are in good focus and are perfused by RBCs and plasma. The difference image represents the cumulative sum of the square of differences in intensity values between consecutive frames and gives an indication of the frequency of passage of RBCs separated by plasma gaps. The transition image represents the number of times the intensity at a given pixel crosses a predefined threshold and indicates the number of RBCs (or trains of RBCs) that passes a given location during the observation period. CONCLUSIONS: The above flow visualization techniques are valuable tools to aid in the study of image focus, network geometry, RBC flow paths and dynamics, that can then be used in identifying capillaries for subsequent (separate) detailed analysis to provide quantitative information about RBC flow.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.040 | 0.007 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".