Utilizing Fisheye Cameras and Mass Spectrometry Imaging to Obtain Probing Coordinates for Histological Samples
Bibliographic record
Abstract
Detecting chemical compositions in histological samples using mass spectrometry often requires time-consuming user interaction with a probe to ensure it is positioned as closely as possible to a sampling region. Fisheye cameras can obtain images of samples with a wide field-of-view; however, barrel distortion makes it difficult to analyze them as a flat plane. This research project aimed to develop an application that unwarps live images of histological samples captured with a fisheye camera and calculates the exact image-to-real-world probing coordinates with minor user interaction. Python’s PyQt5 and OpenCV libraries were utilized to create interactive software that removes fisheye distortion, finds image-to-real-world coordinates, and segments images. Connected hardware consisted of a Prusa 3D printer, liquid micro junction-surface sampling probe, fisheye camera, and LED light strips. The printer provided a bed for capturing sample images and control of the camera and probe within 1/100 of a millimetre. The software was outlined to process at least 2 photos of checkerboards for the fisheye undistortion, capture a photo of a sample to be unwarped, and require the user to map 4 points from the image to probe coordinates. Using this approach, real-world coordinates could be found for any image location. The application successfully unwarps live photos and converts image-to-real-world coordinates with sub-millimeter accuracy. Users can take new calibration photos, control lighting, save photos, and check undistortion entirely within the application to ensure all parameters are satisfactory. Coordinates of significant regions are displayed or may be manually selected, making it easier to sample specific areas. The success of unwarping relies on the camera’s focus and height, lighting, and object positioning. Future development plans to improve robustness and coordinate accuracy, and reduce the amount of calibration needed.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".