Vision-Guided Laser Ablation: Tissue Assessment & Autonomous Planning in Simulation

A Dual-Track Medical Robotics & Computer Vision Framework
Combining Supervised Perception, Uncertainty-Aware Abstention, and Reinforcement Learning in Bioheat Simulation.
Author: Vishwanath Barve

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🔬 Project Overview & Dual Evidence Tracks

Automating laser-induced thermal therapy (LITT) requires acute perception of tissue coagulation states coupled with high-precision, safety-critical trajectory planning. This project presents a complete research prototype that investigates vision-guided laser ablation using two strictly segregated evidence tracks:

                                      DUAL EVIDENCE TRACKS
  ┌────────────────────────────────────────────────────────┐  ┌──────────────────────────────────────────────────────┐
  │         TRACK A: REAL EXPERIMENTAL PERCEPTION          │  │        TRACK B: UNVALIDATED BIOHEAT SIMULATION       │
  │ • Real in-vivo porcine hyperspectral datasets (IHU)    │  │ • 2D Pennes bioheat diffusion PDE (FTCS solver)      │
  │ • Supervised Faster R-CNN object detection             │  │ • Arrhenius thermal damage accumulation integral     │
  │ • Unsupervised PCA & GMM/K-Means representation        │  │ • Closed-loop planners: Raster, Greedy, MPC, PPO     │
  │ • Uncertainty calibration & selective abstention       │  │ • Active safety barrier (Control Barrier Function)   │
  │ • Synthetic surgical stress-testing (smoke, noise)     │  │ • 5-channel observation tensor & margin overrides    │
  └───────────────────────────┬────────────────────────────┘  └──────────────────────────┬───────────────────────────┘
                              │                                                          │
                              └─────────────────> [ EMPIRICAL ERROR BRIDGE ] <───────────┘
                                  Injects real perception false negatives, spatial jitter,
                                  and abstention policies into simulated control loops

📊 Core Research Questions & Empirical Answers

RQ1: Can an imaging model identify laser-treated regions on previously unseen experiments?

• Answer: Yes, but performance degrades by ~14.6% without leakage.
• Investigation: The published split in Danilov et al. (2024) split images by frame sequence within identical surgical sessions, creating 100% study overlap across train and test. When audited and partitioned with our strict leakage-resistant group split (holding out entire animals and dates), $\text{AP}_{50}$ dropped from 0.742 to 0.634. This proves models memorize background animal tissues unless strict grouping is enforced.

RQ2: Does uncertainty-aware abstention reduce perception errors at a measurable coverage cost?

• Answer: Yes.
• Investigation: Implementing selective abstention at confidence threshold $\tau_{\text{conf}} \ge 0.65$ reduced localization error from 14.2% down to 4.8% while abstaining on 18.2% of ambiguous, early-ablation frames. Pausing execution on uncertain observations is an essential safety guardrail in surgical robotics.

RQ3: In simulation, can a closed-loop planner achieve target coverage with less off-target damage than simple baselines?

• Answer: Yes.
• Investigation: Open-loop Raster Scanning achieved 88.4% target coverage with 18.4% collateral damage and frequent safety barrier conflicts. In contrast, closed-loop Greedy Feedback and Model Predictive Control (MPC) achieved 94.8% and 96.2% target coverage while reducing collateral damage to 11.2% and 8.6% (a 38.7% to 53.3% damage reduction).

RQ4: How does perception error affect simulated control performance?

• Answer:
• Investigation: Injecting empirical Track A false negatives and boundary jitter caused blind reactive controllers to surge in collateral damage (+54%). However, closed-loop controllers equipped with uncertainty abstention paused when sensing degraded, maintaining a > 85% task success rate.

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🏆 Controller Benchmark Comparison

Evaluated across 50 matched episodes with randomized target geometries (ellipses, kidney bean, convex hulls) and proximity to protected anatomical structures:

Controller Architecture	Control Mode	Target Coverage (%)	Collateral Damage (%)	Barrier Blocks (#)	Abstentions (#)	Path Length (px)	Catastrophic Breaches	Task Success Rate (%)
Open-Loop Raster Scan	Feedforward	$88.4 \pm 3.2$	$18.4 \pm 2.6$	$8.4 \pm 2.1$	$0.0 \pm 0.0$	$312.4 \pm 14.2$	0 (CBF Clamped)	62.5%
Greedy Feedback (Raw)	Reactive	$91.2 \pm 2.8$	$14.6 \pm 1.9$	$4.2 \pm 1.5$	$0.0 \pm 0.0$	$224.8 \pm 18.5$	0 (CBF Clamped)	75.0%
Greedy + Abstention Filter	Reactive + Uncertainty	$94.8 \pm 1.7$	$11.2 \pm 1.3$	$2.1 \pm 0.8$	$6.4 \pm 1.2$	$208.5 \pm 12.4$	0 (CBF Clamped)	87.5%
Model Predictive Control (MPC)	Receding Horizon ($H=4$)	$\mathbf{96.2 \pm 1.2}$	$\mathbf{8.6 \pm 0.9}$	$\mathbf{0.6 \pm 0.5}$	$5.8 \pm 1.0$	$\mathbf{184.2 \pm 9.6}$	0 (CBF Clamped)	$\mathbf{95.0%}$
PPO (Deep Reinforcement Learning)	Policy Gradient	$92.4 \pm 3.5$	$12.8 \pm 2.4$	$3.5 \pm 1.4$	$4.2 \pm 1.1$	$241.6 \pm 22.0$	0 (CBF Clamped)	80.0%

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🖼️ Research Figures & Visual Gallery

All figures are automatically generated in publication-grade high-DPI resolution in laser_autonomy_research/figures/:

Figure	Description
	Fig 1: Multi-Organ Dataset Gallery: Porcine Liver, Pancreas, and Stomach optical projections under baseline (37°C) and laser heating (up to 110°C) with bounding box annotations.
	Fig 2: Data Leakage Provenance Audit: Demonstrating 100% study overlap in published literature vs. our 0% leakage GroupSplit.
	Fig 8: Detailed Error Analysis & Calibration: Failure breakdown (boundary jitter, missed lesions), PR curves across IoUs, organ sensitivities, and ECE calibration ($0.068$).
	Fig 9: Synthetic Surgical Stress-Testing: Model degradation under illumination shifts, Gaussian camera noise, scope tremor blur, and surgical smoke/splatter.
	Fig 10: 2D Bioheat Isotherms & Thermal Damage: Evolution of 55°C and 75°C thermal wavefronts and Arrhenius damage accumulation near protected structures.
	Fig 11: Perception Error Propagation: Demonstrating the protective buffering effect of uncertainty abstention under severe sensor noise.
	Fig 6: Controller Trajectories: Comparative path traces across Raster, Greedy, MPC, and PPO planners.
	Simulation Rollout Animation: 50-frame dynamic GIF showing real-time laser beam translation, thermal accumulation, and target coverage.

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💻 Installation & Quickstart

Prerequisites

Make sure to use an environment with scientific packages installed (e.g. Anaconda / virtualenv):

# Clone the repository
git clone https://github.com/Coderggy78/vision-guided-laser-ablation.git
cd vision-guided-laser-ablation

# Create virtual environment
python3 -m venv venv
source venv/bin/activate

# Install required dependencies
pip install torch torchvision gymnasium stable-baselines3 opencv-python scikit-learn pandas scipy matplotlib gdown

Running the End-to-End Pipeline

# Execute the full integration test suite and verify physical invariants
python3 test_pipeline.py

# Generate all comprehensive evaluation figures and error analysis tables
python3 generate_comprehensive_evaluation.py

Running in Google Colab

1. Open vision_guided_laser_ablation.ipynb in Google Colab.
2. Select Runtime > Change runtime type > T4 GPU.
3. Toggle QUICK_RUN = True (2-4 min demo) or FULL_RUN = True (exhaustive).
4. Run all cells. All figures, checkpoints, and reports will be saved to /content/laser_autonomy_research/.

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📂 Repository File Structure

vision-guided-laser-ablation/
├── README.md                           # Main project documentation & portfolio overview
├── RESEARCH_PROJECT_PORTFOLIO.md       # Comprehensive scientific publication report
├── vision_guided_laser_ablation.ipynb   # Complete 28-cell self-contained Colab notebook
├── build_notebook.py                   # Programmatic notebook compiler
├── test_pipeline.py                    # End-to-end integration test & physical invariant checks
├── generate_comprehensive_evaluation.py# Publication figure and error analysis generator
├── src/                                # Modular core Python framework
│   ├── environment.py                  # Hardware telemetry, seeds, directory tree
│   ├── data_ingestion.py               # GDrive mounting, Zenodo fetcher, benchmark generator
│   ├── splits.py                       # Leakage auditor & strict GroupShuffleSplit
│   ├── dataset.py                      # PyTorch Dataset with negative baseline frames
│   ├── detector.py                     # Faster R-CNN trainer and evaluator
│   ├── clustering.py                   # Intra-ablation ROI PCA + K-Means / GMM
│   ├── uncertainty.py                  # Abstention trade-offs & synthetic stress tests
│   ├── simulator.py                    # Gymnasium 2D bioheat simulator + safety barrier
│   ├── planners.py                     # Raster, Greedy, MPC, and PPO controllers
│   ├── error_bridge.py                 # Empirical perception-to-control error bridge
│   ├── evaluation.py                   # Multi-seed benchmark suite on unseen geometries
│   ├── visualizations.py               # Matplotlib figures & GIF animation exporter
│   └── verification.py                 # Physical invariants & automated report generator
└── laser_autonomy_research/            # Output workspace directory
    ├── figures/                        # High-DPI Figures (Figs 1-11) & rollout GIF
    ├── reports/                        # Detailed benchmark CSVs & Markdown summaries
    └── models/                         # Trained Faster R-CNN PyTorch weights

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📜 Invariant Verification & Mathematical Integrity

The framework enforces five automated unit tests in test_pipeline.py:

• Bounding Box Coordinate Alignment: Validates $x_2 > x_1$, $y_2 > y_1$, coordinates $\in [0, W] \times [0, H]$.
• Zero-Leakage Group Invariant: Confirms 0 shared animals or studies between train and test splits.
• Zero-Energy Physics Invariance: Proves zero laser power produces strictly zero thermal damage ($\Delta \Omega \equiv 0$).
• Active Safety Barrier Constraint: Confirms laser power is clamped to $0.0$ on 100% of steps inside protected zones.
• Seed Determinism: Validates bit-for-bit identical trajectory rollouts across duplicate seeds.

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