File size: 3,802 Bytes
5fdedf9 bce169b 5fdedf9 1d197a4 5fdedf9 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 | ---
language:
- en
metrics:
- accuracy
- recall
- f1
pipeline_tag: image-segmentation
---
# IVUS-Segmentation
DeepIVUS pipeline for lumen segmentation and bifurcation frame classification on IVUS DICOMs.
## What This Repo Contains
- Runtime package: `deepivus/`
- CLI entrypoints and pipeline orchestration
- Lumen segmentation inference
- Bifurcation classifier inference
- Video/JSON/XML export utilities
- Data and annotations:
- `data/`: source DICOM folders (`data/bifurcation`, `data/paul`)
- `evals/frame_bank_merged/`: canonical annotation bank
- `evals/splits/ivus_split_merged_600.json`: canonical train/val/test split
- Models:
- `models/standalone/lumen/`: standalone lumen TF SavedModel
- `models/standalone/bifurcation/best_bifurcation_classifier.keras`: standalone bifurcation classifier
- `models/standalone/bifurcation/threshold.json`: selected inference threshold (from validation sweep)
- Training and evaluation scripts:
- `scripts/finetune/bifurcation/`
- `scripts/finetune/lumen/`
- shared helpers: `scripts/finetune/shared/common.py`
- data tooling: `scripts/data/frame_bank.py`
## Main Runtime Workflow
### 1) Segment a DICOM and classify branching
```bash
python DeepIVUS.py segment data/paul/FILE00005.dcm
```
Outputs are written under `output/<timestamp>/` and include:
- contours XML and JSONL
- top-confidence JSONL
- lumen overlay video
- bifurcation predictions JSONL + summary JSON
- overlay video with branch/non-branch flag
Notes:
- `--bifurcation-threshold` is optional.
- If omitted, threshold is loaded from `threshold.json` beside the selected bifurcation model.
### 2) Edit contour annotations in GUI
```bash
python DeepIVUS.py edit-annotations data/paul/FILE00005.dcm
```
## Bifurcation Model Workflow
### 1) Sample new frames for manual labeling
```bash
python -u scripts/finetune/bifurcation/sample_new_bifurcation_frames.py
```
### 2) Label sampled frames in GUI
```bash
python -u scripts/finetune/bifurcation/annotate_bifurcation_samples.py
```
### 3) Merge labels into canonical frame bank
```bash
python -u scripts/finetune/bifurcation/merge_bifurcation_annotations.py
```
### 4) Create/update canonical split
```bash
python -u scripts/finetune/bifurcation/create_bifurcation_splits.py
```
### 5) Train bifurcation classifier
```bash
python -u scripts/finetune/bifurcation/train_bifurcation_classifier.py
```
### 6) Evaluate on test and select threshold from validation
```bash
python -u scripts/finetune/bifurcation/run_bifurcation_test_inference.py
```
This writes test metrics and persists selected threshold to:
- `threshold.json` beside the evaluated classifier model file.
## Lumen Model Workflow
### 1) Identify lumen class index in SavedModel logits
```bash
python -u scripts/finetune/lumen/identify_lumen_class.py
```
### 2) Fine-tune lumen model
```bash
python -u scripts/finetune/lumen/finetune_lumen_from_saved_model.py \
--output-model-dir models/standalone/lumen
```
### 3) Evaluate lumen model on test split
```bash
python -u scripts/finetune/lumen/run_test_inference.py \
--model-dir models/standalone/lumen
```
### 4) Run single-DICOM lumen + bifurcation overlay inference
```bash
python -u scripts/finetune/lumen/run_single_dicom_inference.py \
--dicom-path data/paul/FILE00005.dcm
```
## Repository Structure
- `DeepIVUS.py`: top-level CLI launcher
- `deepivus/`: runtime package
- `models/`: runtime models and threshold
- `evals/`: canonical annotation bank and split
- `scripts/`: training/eval/data utilities
- `output/`: generated artifacts (runs, metrics, videos)
## Environment
- Python project config: `pyproject.toml`
- Conda env file: `environment.yml`
Install dependencies with your preferred toolchain (`poetry`, `pip`, or `conda`) using those files. |