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# phase 3: end-to-end pipeline (no ui)
## purpose
Tie together Phase 1 (data loading) and Phase 2 (DeepISLES inference) into a cohesive pipeline. At the end of this phase, we can run stroke segmentation on any case from ISLES24-MR-Lite with a single function call.
## deliverables
- [ ] `src/stroke_deepisles_demo/pipeline.py` - Main orchestration
- [ ] `src/stroke_deepisles_demo/metrics.py` - Optional Dice computation
- [ ] CLI entry point for testing
- [ ] Unit tests with full mocking
- [ ] Integration test for complete flow
## vertical slice outcome
After this phase, you can run:
```python
from stroke_deepisles_demo.pipeline import run_pipeline_on_case
# Run segmentation on a specific case
result = run_pipeline_on_case("sub-001")
print(f"Input DWI: {result.input_files.dwi}")
print(f"Input ADC: {result.input_files.adc}")
print(f"Prediction: {result.prediction_mask}")
print(f"Ground truth: {result.ground_truth}")
print(f"Dice score: {result.dice_score:.3f}") # if computed
```
Or via CLI:
```bash
uv run stroke-demo run --case sub-001 --fast
uv run stroke-demo run --index 0 --output ./results
uv run stroke-demo list # List all available cases
```
## module structure
```
src/stroke_deepisles_demo/
├── pipeline.py # Main orchestration
├── metrics.py # Dice score computation
└── cli.py # CLI entry point (optional)
```
## interfaces and types
### `pipeline.py`
```python
"""End-to-end pipeline orchestration."""
from __future__ import annotations
import tempfile
from dataclasses import dataclass
from pathlib import Path
from typing import Mapping
from stroke_deepisles_demo.core.config import settings
from stroke_deepisles_demo.core.types import CaseFiles, InferenceResult
from stroke_deepisles_demo.data import CaseAdapter, load_isles_dataset, stage_case_for_deepisles
from stroke_deepisles_demo.inference import run_deepisles_on_folder
@dataclass(frozen=True)
class PipelineResult:
"""Complete result of running the pipeline on a case."""
case_id: str
input_files: CaseFiles
staged_dir: Path
prediction_mask: Path
ground_truth: Path | None
dice_score: float | None # None if ground truth unavailable or not computed
elapsed_seconds: float
def run_pipeline_on_case(
case_id: str | int,
*,
dataset_id: str | None = None,
output_dir: Path | None = None,
fast: bool = True,
gpu: bool = True,
compute_dice: bool = True,
cleanup_staging: bool = False,
) -> PipelineResult:
"""
Run the complete segmentation pipeline on a single case.
This function:
1. Loads the case from HuggingFace Hub (or cache)
2. Stages NIfTI files with DeepISLES-expected naming
3. Runs DeepISLES Docker container
4. Optionally computes Dice score against ground truth
5. Returns all paths and metrics
Args:
case_id: Case identifier (string) or index (int)
dataset_id: HF dataset ID (default from settings)
output_dir: Directory for results (default: temp dir)
fast: Use SEALS-only mode (ISLES'22 winner, DWI+ADC only, no FLAIR needed)
gpu: Use GPU acceleration
compute_dice: Compute Dice score if ground truth available
cleanup_staging: Remove staging directory after inference
Returns:
PipelineResult with all paths and optional metrics
Raises:
DataLoadError: If case cannot be loaded
MissingInputError: If required files missing
DeepISLESError: If inference fails
Example:
>>> result = run_pipeline_on_case("sub-001", fast=True)
>>> print(f"Dice: {result.dice_score:.3f}")
"""
...
def run_pipeline_on_batch(
case_ids: list[str | int],
*,
max_workers: int = 1,
**kwargs,
) -> list[PipelineResult]:
"""
Run pipeline on multiple cases.
Note: Parallel execution requires multiple GPUs or sequential mode.
Args:
case_ids: List of case identifiers or indices
max_workers: Number of parallel workers (default 1 for sequential)
**kwargs: Passed to run_pipeline_on_case
Returns:
List of PipelineResult, one per case
"""
...
def get_pipeline_summary(results: list[PipelineResult]) -> PipelineSummary:
"""
Compute summary statistics from multiple pipeline results.
Returns:
Summary with mean Dice, success rate, etc.
"""
...
@dataclass(frozen=True)
class PipelineSummary:
"""Summary statistics from multiple pipeline runs."""
num_cases: int
num_successful: int
num_failed: int
mean_dice: float | None
std_dice: float | None
min_dice: float | None
max_dice: float | None
mean_elapsed_seconds: float
# Internal helper
def _load_or_get_adapter(
dataset_id: str | None = None,
cache: dict | None = None,
) -> CaseAdapter:
"""Load dataset and return adapter, using cache if available."""
...
```
### `metrics.py`
```python
"""Metrics for evaluating segmentation quality."""
from __future__ import annotations
from pathlib import Path
import nibabel as nib
import numpy as np
from numpy.typing import NDArray
def compute_dice(
prediction: Path | NDArray[np.float64],
ground_truth: Path | NDArray[np.float64],
*,
threshold: float = 0.5,
) -> float:
"""
Compute Dice similarity coefficient between prediction and ground truth.
Dice = 2 * |P ∩ G| / (|P| + |G|)
Args:
prediction: Path to NIfTI file or numpy array
ground_truth: Path to NIfTI file or numpy array
threshold: Threshold for binarization (if needed)
Returns:
Dice coefficient in [0, 1]
Raises:
ValueError: If shapes don't match
"""
...
def compute_volume_ml(
mask: Path | NDArray[np.float64],
voxel_size_mm: tuple[float, float, float] | None = None,
) -> float:
"""
Compute lesion volume in milliliters.
Args:
mask: Path to NIfTI file or numpy array
voxel_size_mm: Voxel dimensions in mm (read from NIfTI if None)
Returns:
Volume in milliliters (mL)
"""
...
def load_nifti_as_array(path: Path) -> tuple[NDArray[np.float64], tuple[float, ...]]:
"""
Load NIfTI file and return data array with voxel dimensions.
Returns:
Tuple of (data_array, voxel_sizes_mm)
"""
...
```
### `cli.py` (optional)
```python
"""Command-line interface for stroke-deepisles-demo."""
from __future__ import annotations
import argparse
import sys
from pathlib import Path
def main(argv: list[str] | None = None) -> int:
"""Main CLI entry point."""
parser = argparse.ArgumentParser(
prog="stroke-demo",
description="Run DeepISLES stroke segmentation on HF datasets",
)
subparsers = parser.add_subparsers(dest="command", required=True)
# List command
list_parser = subparsers.add_parser("list", help="List available cases")
list_parser.add_argument(
"--dataset", default=None, help="HF dataset ID"
)
# Run command
run_parser = subparsers.add_parser("run", help="Run segmentation")
run_parser.add_argument(
"--case", type=str, help="Case ID (e.g., sub-001)"
)
run_parser.add_argument(
"--index", type=int, help="Case index (alternative to --case)"
)
run_parser.add_argument(
"--output", type=Path, default=None, help="Output directory"
)
run_parser.add_argument(
"--fast", action="store_true", default=True, help="Use fast mode"
)
run_parser.add_argument(
"--no-gpu", action="store_true", help="Disable GPU"
)
args = parser.parse_args(argv)
if args.command == "list":
return cmd_list(args)
elif args.command == "run":
return cmd_run(args)
return 0
def cmd_list(args: argparse.Namespace) -> int:
"""Handle 'list' command."""
...
def cmd_run(args: argparse.Namespace) -> int:
"""Handle 'run' command."""
...
if __name__ == "__main__":
sys.exit(main())
```
### pyproject.toml addition for CLI
```toml
[project.scripts]
stroke-demo = "stroke_deepisles_demo.cli:main"
```
## tdd plan
### test file structure
```
tests/
├── test_pipeline.py # Pipeline orchestration tests
├── test_metrics.py # Metrics computation tests
└── test_cli.py # CLI tests (optional)
```
### tests to write first (TDD order)
#### 1. `tests/test_metrics.py` - Pure functions, no mocks needed
```python
"""Tests for metrics module."""
from __future__ import annotations
from pathlib import Path
import nibabel as nib
import numpy as np
import pytest
from stroke_deepisles_demo.metrics import (
compute_dice,
compute_volume_ml,
load_nifti_as_array,
)
class TestComputeDice:
"""Tests for compute_dice."""
def test_identical_masks_return_one(self) -> None:
"""Dice of identical masks is 1.0."""
mask = np.array([[[1, 1, 0], [0, 1, 0], [0, 0, 1]]])
dice = compute_dice(mask, mask)
assert dice == 1.0
def test_no_overlap_returns_zero(self) -> None:
"""Dice of non-overlapping masks is 0.0."""
pred = np.array([[[1, 1, 0], [0, 0, 0], [0, 0, 0]]])
gt = np.array([[[0, 0, 0], [0, 0, 0], [0, 0, 1]]])
dice = compute_dice(pred, gt)
assert dice == 0.0
def test_partial_overlap(self) -> None:
"""Dice with partial overlap is between 0 and 1."""
pred = np.array([[[1, 1, 0], [0, 0, 0], [0, 0, 0]]])
gt = np.array([[[1, 0, 0], [0, 0, 0], [0, 0, 0]]])
dice = compute_dice(pred, gt)
# Overlap: 1, Pred: 2, GT: 1 -> Dice = 2*1 / (2+1) = 0.667
assert 0.6 < dice < 0.7
def test_empty_masks_return_one(self) -> None:
"""Dice of two empty masks is 1.0 (both agree on nothing)."""
empty = np.zeros((10, 10, 10))
dice = compute_dice(empty, empty)
assert dice == 1.0
def test_accepts_file_paths(self, temp_dir: Path) -> None:
"""Can compute Dice from NIfTI file paths."""
mask = np.array([[[1, 1, 0], [0, 1, 0], [0, 0, 1]]]).astype(np.float32)
img = nib.Nifti1Image(mask, np.eye(4))
pred_path = temp_dir / "pred.nii.gz"
gt_path = temp_dir / "gt.nii.gz"
nib.save(img, pred_path)
nib.save(img, gt_path)
dice = compute_dice(pred_path, gt_path)
assert dice == 1.0
def test_shape_mismatch_raises(self) -> None:
"""Raises ValueError if shapes don't match."""
pred = np.zeros((10, 10, 10))
gt = np.zeros((10, 10, 5))
with pytest.raises(ValueError, match="shape"):
compute_dice(pred, gt)
class TestComputeVolumeMl:
"""Tests for compute_volume_ml."""
def test_computes_volume_from_voxel_size(self) -> None:
"""Volume computed correctly from voxel dimensions."""
# 10x10x10 = 1000 voxels of size 1mm^3 each = 1000mm^3 = 1mL
mask = np.ones((10, 10, 10))
volume = compute_volume_ml(mask, voxel_size_mm=(1.0, 1.0, 1.0))
assert volume == pytest.approx(1.0, rel=0.01)
def test_reads_voxel_size_from_nifti(self, temp_dir: Path) -> None:
"""Reads voxel size from NIfTI header."""
mask = np.ones((10, 10, 10)).astype(np.float32)
# Affine with 2mm voxels
affine = np.diag([2.0, 2.0, 2.0, 1.0])
img = nib.Nifti1Image(mask, affine)
path = temp_dir / "mask.nii.gz"
nib.save(img, path)
# 1000 voxels * 8mm^3 = 8000mm^3 = 8mL
volume = compute_volume_ml(path)
assert volume == pytest.approx(8.0, rel=0.01)
class TestLoadNiftiAsArray:
"""Tests for load_nifti_as_array."""
def test_returns_array_and_voxel_sizes(self, temp_dir: Path) -> None:
"""Returns data array and voxel dimensions."""
data = np.random.rand(10, 10, 10).astype(np.float32)
affine = np.diag([1.5, 1.5, 2.0, 1.0])
img = nib.Nifti1Image(data, affine)
path = temp_dir / "test.nii.gz"
nib.save(img, path)
arr, voxels = load_nifti_as_array(path)
assert arr.shape == (10, 10, 10)
assert voxels == pytest.approx((1.5, 1.5, 2.0), rel=0.01)
```
#### 2. `tests/test_pipeline.py` - Full orchestration with mocks
```python
"""Tests for pipeline orchestration."""
from __future__ import annotations
from pathlib import Path
from unittest.mock import MagicMock, patch
import pytest
from stroke_deepisles_demo.core.types import CaseFiles
from stroke_deepisles_demo.pipeline import (
PipelineResult,
PipelineSummary,
get_pipeline_summary,
run_pipeline_on_case,
)
class TestRunPipelineOnCase:
"""Tests for run_pipeline_on_case."""
@pytest.fixture
def mock_dependencies(self, temp_dir: Path):
"""Mock all external dependencies."""
with patch(
"stroke_deepisles_demo.pipeline.load_isles_dataset"
) as mock_load, patch(
"stroke_deepisles_demo.pipeline.CaseAdapter"
) as mock_adapter_cls, patch(
"stroke_deepisles_demo.pipeline.stage_case_for_deepisles"
) as mock_stage, patch(
"stroke_deepisles_demo.pipeline.run_deepisles_on_folder"
) as mock_inference, patch(
"stroke_deepisles_demo.pipeline.compute_dice"
) as mock_dice:
# Configure mocks
mock_adapter = MagicMock()
mock_adapter.get_case.return_value = CaseFiles(
dwi=temp_dir / "dwi.nii.gz",
adc=temp_dir / "adc.nii.gz",
flair=None,
ground_truth=temp_dir / "gt.nii.gz",
)
mock_adapter_cls.return_value = mock_adapter
mock_stage.return_value = MagicMock(
input_dir=temp_dir / "staged",
dwi_path=temp_dir / "staged" / "dwi.nii.gz",
adc_path=temp_dir / "staged" / "adc.nii.gz",
flair_path=None,
)
mock_inference.return_value = MagicMock(
prediction_path=temp_dir / "results" / "pred.nii.gz",
elapsed_seconds=10.5,
)
mock_dice.return_value = 0.85
yield {
"load": mock_load,
"adapter_cls": mock_adapter_cls,
"adapter": mock_adapter,
"stage": mock_stage,
"inference": mock_inference,
"dice": mock_dice,
}
def test_returns_pipeline_result(self, mock_dependencies, temp_dir) -> None:
"""Returns PipelineResult with expected fields."""
result = run_pipeline_on_case("sub-001")
assert isinstance(result, PipelineResult)
assert result.case_id == "sub-001"
def test_loads_case_from_adapter(self, mock_dependencies, temp_dir) -> None:
"""Loads case using CaseAdapter."""
run_pipeline_on_case("sub-001")
mock_dependencies["adapter"].get_case.assert_called_once_with("sub-001")
def test_stages_files_for_deepisles(self, mock_dependencies, temp_dir) -> None:
"""Stages files with correct naming."""
run_pipeline_on_case("sub-001")
mock_dependencies["stage"].assert_called_once()
def test_runs_deepisles_inference(self, mock_dependencies, temp_dir) -> None:
"""Runs DeepISLES on staged directory."""
run_pipeline_on_case("sub-001", fast=True, gpu=False)
mock_dependencies["inference"].assert_called_once()
call_kwargs = mock_dependencies["inference"].call_args.kwargs
assert call_kwargs.get("fast") is True
assert call_kwargs.get("gpu") is False
def test_computes_dice_when_ground_truth_available(
self, mock_dependencies, temp_dir
) -> None:
"""Computes Dice score when ground truth is available."""
result = run_pipeline_on_case("sub-001", compute_dice=True)
mock_dependencies["dice"].assert_called_once()
assert result.dice_score == 0.85
def test_skips_dice_when_disabled(self, mock_dependencies, temp_dir) -> None:
"""Skips Dice computation when compute_dice=False."""
result = run_pipeline_on_case("sub-001", compute_dice=False)
mock_dependencies["dice"].assert_not_called()
assert result.dice_score is None
def test_handles_missing_ground_truth(self, mock_dependencies, temp_dir) -> None:
"""Handles cases without ground truth gracefully."""
# Modify mock to return no ground truth
mock_dependencies["adapter"].get_case.return_value = CaseFiles(
dwi=temp_dir / "dwi.nii.gz",
adc=temp_dir / "adc.nii.gz",
flair=None,
ground_truth=None,
)
result = run_pipeline_on_case("sub-001", compute_dice=True)
assert result.dice_score is None
assert result.ground_truth is None
def test_accepts_integer_index(self, mock_dependencies, temp_dir) -> None:
"""Accepts integer index as case identifier."""
mock_dependencies["adapter"].get_case_by_index.return_value = (
"sub-001",
CaseFiles(
dwi=temp_dir / "dwi.nii.gz",
adc=temp_dir / "adc.nii.gz",
flair=None,
ground_truth=None,
),
)
result = run_pipeline_on_case(0)
assert result.case_id == "sub-001"
class TestGetPipelineSummary:
"""Tests for get_pipeline_summary."""
def test_computes_mean_dice(self) -> None:
"""Computes mean Dice from results."""
results = [
MagicMock(dice_score=0.8, elapsed_seconds=10),
MagicMock(dice_score=0.9, elapsed_seconds=12),
MagicMock(dice_score=0.7, elapsed_seconds=8),
]
summary = get_pipeline_summary(results)
assert summary.mean_dice == pytest.approx(0.8, rel=0.01)
def test_handles_none_dice_scores(self) -> None:
"""Handles results with None Dice scores."""
results = [
MagicMock(dice_score=0.8, elapsed_seconds=10),
MagicMock(dice_score=None, elapsed_seconds=12),
MagicMock(dice_score=0.7, elapsed_seconds=8),
]
summary = get_pipeline_summary(results)
# Mean of 0.8 and 0.7 only
assert summary.mean_dice == pytest.approx(0.75, rel=0.01)
def test_counts_successful_and_failed(self) -> None:
"""Counts successful and failed runs."""
results = [
MagicMock(dice_score=0.8, elapsed_seconds=10),
MagicMock(dice_score=None, elapsed_seconds=0), # Failed
]
summary = get_pipeline_summary(results)
assert summary.num_cases == 2
assert summary.num_successful == 1
assert summary.num_failed == 1
@pytest.mark.integration
class TestPipelineIntegration:
"""Integration tests for full pipeline."""
@pytest.mark.slow
def test_run_on_real_case(self) -> None:
"""Run pipeline on actual ISLES24-MR-Lite case."""
# Requires: network, Docker, DeepISLES image
# Run with: pytest -m "integration and slow"
result = run_pipeline_on_case(
0, # First case
fast=True,
gpu=False,
compute_dice=True,
)
assert result.prediction_mask.exists()
assert 0 <= result.dice_score <= 1
```
### what to mock
- `load_isles_dataset` - Avoid network calls
- `CaseAdapter` - Return synthetic CaseFiles
- `stage_case_for_deepisles` - Return mock staged paths
- `run_deepisles_on_folder` - Avoid Docker
- `compute_dice` - Return fixed value for deterministic tests
### what to test for real
- Dice computation (pure NumPy)
- Volume computation (pure NumPy + nibabel)
- NIfTI loading
- Integration: full pipeline on real data
## "done" criteria
Phase 3 is complete when:
1. All unit tests pass: `uv run pytest tests/test_pipeline.py tests/test_metrics.py -v`
2. Dice computation is correct for known test cases
3. Pipeline orchestrates all components correctly
4. CLI works: `uv run stroke-demo list` and `uv run stroke-demo run --index 0`
5. Integration test passes: `uv run pytest -m "integration and slow"`
6. Type checking passes: `uv run mypy src/stroke_deepisles_demo/pipeline.py src/stroke_deepisles_demo/metrics.py`
7. Code coverage for pipeline module > 80%
## implementation notes
- Use dataclasses for results (immutable, typed)
- Consider caching the loaded dataset in module-level variable
- Dice should handle edge cases (empty masks, shape mismatches)
- CLI is optional but useful for manual testing
- Batch processing is sequential by default (GPU constraint)
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