Machine-Learning Assisted Segmentation to Assess the Outcomes of Tissue Ablation with Nanosecond Pulses
Bibliographic record
Abstract
High-intensity electrical pulsing has emerged as a robust, versatile, and minimally invasive method for ablating tumors and unwanted tissues. The pulse duration varies from nanoseconds to milliseconds, encompassing three primary ablation pulses: Irreversible electroporation (IRE, us-ms), high-frequency IRE (HFIRE, bipolar $\mu{\mathrm{s}}$), and nanosecond pulses (nsEP). In this study, we concentrated on utilizing a plant tissue model (Yukon Gold) to explore the outcomes of ablations using three different pulse waveforms: unipolar 100 $\mu{\mathrm{s}}$ (IRE), Bipolar ±300 ns, and Unipolar 300 ns. The images of the treated potatoes underwent analysis using a U-net machine learning model trained with manually segmented ablation areas as the ground truth. The treated tissues were evaluated at multiple time points. The trained model’s performance was validated on unseen images, achieving an intersection over union (IoU) of 99.41%. Subsequently, we employed the trained model on unsegmented potato images to identify ablation areas. This study aims to investigate the impact of pulse waveforms on the tumor ablation area’s (AA) size, the homogeneity (HG) of the ablation, and the presence of a distinct boundary, known as boundary gradient (BG). Our findings indicated that the bipolar nanosecond pulse seems to be the optimal waveform for tissue ablation. Additionally, we demonstrated that while the Segment Anything Model (SAM), a large segmentation model developed by Meta, excelled in various zero-shot segmentation applications, it failed to precisely segment the ablation area. This suggests that further efforts are necessary to adapt SAM for specialized tasks.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".