Computer Vision for Detecting and Measuring Multicellular Tumor Shperoids of Prostate Cancer
Bibliographic record
Abstract
We present a deep learning model to apply computer vision to detect prostate cancer spheroid cultures and calculate their volume. Multicellular tumour spheroids, or simply spheroids, represent a three-dimensional in vitro model of cancer. Spheroids are being increasingly used in drug discovery due to their superior ability to mimic the tumor microenvironment compared to monolayer cell cultures. A reduction in spheroid size in response to treatment with anticancer agents is indicative of the success of the therapy. As such, accurate spheroid detection and volume estimation is critical in assays involving spheroids. Automating spheroid detection and measurement reduces manual labor, laboratory costs, and research time. Our system is implemented using Darkflow YOLOv2, a single-phase object detector, based on a twenty-four-layer convolutional neural network. The network is trained on the custom data of biochemically-generated spheroids and their corresponding images, which are then bound and detected with an F1-score of 76% and an IoU of 69%. Volume calculations applied to the identified spheroids resulted in a high volume estimation accuracy with only 3.99% average error.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".