Leveraging SAM for automatic prostate segmentation on micro-ultrasound images
Bibliographic record
Abstract
Purpose: Medical image segmentation models are essential for efficiently diagnosing prostate cancer. Current models for segmentation of micro ultrasound images do not utilize foundation models, or geometric and spatial data from the ultrasound image. We aim to leverage the Segment Anything Model (SAM) as a pretrained backbone to create a fully automatic prostate segmentation model and investigate prompting with positonal information such as the slice frame number and previous segmentations. Methods: The Segment Anything Model (SAM) was fine-tuned on Micro-Ultrasound images1 and adapted for the context of clinical prostate segmentation. We explored different fine-tuning methods and prompting combinations fit for automatic segmentation. Dice coefficient (DSC) and Hausdorff 95% distance (HD95) were used as evaluation metrics to compare our model against past models. Results: In comparison to the state of the art models, SliceTrack-SAM improved the mean Dice coefficient from the MicroSegNet2 model (93.1) to 94.3, and reduced the Hausdorff distance from the nnU-Net model (1.09 mm) to 0.81 mm. Conclusion: In summary, using a fully-finetuned SAM, improves prostate segmentation accuracy. In addition, prompting with geometric and spatial data derived from ultrasound images shows potential to enhance segmentation accuracy, particularly on difficult datasets.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".