diff --git a/README.md b/README.md index 01985c9..37d1cd5 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,9 @@ For additional format support: ```bash # For Imaris (.ims) file support pip install zarrnii[imaris] + +# For StarDist deep learning segmentation +pip install zarrnii[stardist] ``` ### Development installation @@ -68,6 +71,42 @@ segmented = znimg.segment(plugin) segmented.to_ome_zarr("segmented_image.ome.zarr") ``` +### StarDist Deep Learning Segmentation + +ZarrNii supports StarDist for accurate cell and nuclei instance segmentation: + +```python +from zarrnii import ZarrNii + +# Load your image +znimg = ZarrNii.from_ome_zarr("image.ome.zarr") + +# Apply StarDist segmentation with pre-trained model +segmented = znimg.segment_stardist( + model_name="2D_versatile_fluo", # or "3D_demo" for 3D + prob_thresh=0.5, + use_gpu=True, # Enable GPU acceleration if available + use_dask_relabeling=False # Set to True if dask_relabeling is available +) + +# Or use custom model +segmented = znimg.segment_stardist( + model_path="/path/to/custom/model", + prob_thresh=0.6, + overlap=64 # Overlap size for tiled processing +) +``` + +**Note**: StarDist requires additional dependencies. Install with: +```bash +pip install zarrnii[stardist] +``` + +For advanced tiled processing of very large images, you can optionally install dask_relabeling: +```bash +pip install git+https://github.com/TheJacksonLaboratory/dask_relabeling.git +``` + ### Custom Plugins Create your own segmentation algorithms by extending the `SegmentationPlugin` base class: diff --git a/docs/examples/stardist_segmentation.md b/docs/examples/stardist_segmentation.md new file mode 100644 index 0000000..673710b --- /dev/null +++ b/docs/examples/stardist_segmentation.md @@ -0,0 +1,227 @@ +# StarDist Segmentation Example + +This example demonstrates how to use the StarDist segmentation plugin in ZarrNii for deep learning-based instance segmentation. + +## Prerequisites + +Install ZarrNii with StarDist dependencies: + +```bash +pip install zarrnii[stardist] +``` + +This will install StarDist, TensorFlow, and the dask-relabel package for efficient processing of large images. + +## Basic StarDist Segmentation + +```python +import numpy as np +import dask.array as da +from zarrnii import ZarrNii + +# Load or create your image data +# For this example, we'll create synthetic cell-like data +np.random.seed(42) +image_data = np.random.normal(0.2, 0.05, (1, 512, 512)) # Background + +# Add some cell-like structures +for i in range(10): + x, y = np.random.randint(50, 462, 2) + size = np.random.randint(20, 40) + xx, yy = np.meshgrid(np.arange(512), np.arange(512)) + mask = ((xx - x) ** 2 + (yy - y) ** 2) < (size ** 2) + image_data[0][mask] = np.random.normal(0.8, 0.1, np.sum(mask)) + +# Create ZarrNii instance +darr = da.from_array(image_data, chunks=(1, 256, 256)) +znimg = ZarrNii.from_darr(darr, axes_order="ZYX", orientation="RAS") + +# Method 1: Using the convenience method +segmented = znimg.segment_stardist( + model_name="2D_versatile_fluo", + prob_thresh=0.5, + nms_thresh=0.4, + use_gpu=True, # Use GPU if available +) + +# Method 2: Using the plugin directly +from zarrnii.plugins.segmentation import StarDistSegmentation + +plugin = StarDistSegmentation( + model_name="2D_versatile_fluo", + prob_thresh=0.6, + nms_thresh=0.3, + use_dask_relabeling=True, + overlap=64, +) +segmented = znimg.segment(plugin) + +print(f"Original shape: {znimg.shape}") +print(f"Segmented shape: {segmented.shape}") +print(f"Number of objects found: {np.max(segmented.data.compute())}") + +# Save results +segmented.to_ome_zarr("stardist_segmented.ome.zarr") +``` + +## Using Pre-trained Models + +StarDist provides several pre-trained models: + +```python +# 2D models +models_2d = [ + "2D_versatile_fluo", # Fluorescence images (recommended) + "2D_versatile_he", # H&E stained images + "2D_paper_dsb2018", # Data Science Bowl 2018 +] + +# 3D models +models_3d = [ + "3D_demo", # Demo 3D model +] + +# Use different models +for model_name in models_2d: + try: + segmented = znimg.segment_stardist(model_name=model_name) + print(f"Successfully segmented with {model_name}") + print(f"Found {np.max(segmented.data.compute())} objects") + except Exception as e: + print(f"Failed with {model_name}: {e}") +``` + +## Custom Models + +You can also use your own trained StarDist models: + +```python +# Use custom model +segmented = znimg.segment_stardist( + model_path="/path/to/your/custom/stardist/model", + prob_thresh=0.4, + nms_thresh=0.5, +) +``` + +## 3D Segmentation + +For 3D data: + +```python +# Create 3D test data +image_3d = np.random.normal(0.2, 0.05, (64, 256, 256)) + +# Add some 3D structures +for i in range(5): + x, y, z = np.random.randint(20, 236), np.random.randint(20, 236), np.random.randint(10, 54) + size = np.random.randint(8, 15) + xx, yy, zz = np.meshgrid(np.arange(256), np.arange(256), np.arange(64)) + mask = ((xx - x) ** 2 + (yy - y) ** 2 + (zz - z) ** 2) < (size ** 2) + image_3d[mask] = np.random.normal(0.8, 0.1, np.sum(mask)) + +# Create ZarrNii instance for 3D +darr_3d = da.from_array(image_3d, chunks=(32, 128, 128)) +znimg_3d = ZarrNii.from_darr(darr_3d, axes_order="ZYX", orientation="RAS") + +# 3D StarDist segmentation +segmented_3d = znimg_3d.segment_stardist( + model_name="3D_demo", + prob_thresh=0.5, + use_dask_relabeling=True, + overlap=32, +) + +print(f"3D segmentation complete: {segmented_3d.shape}") +print(f"Objects found: {np.max(segmented_3d.data.compute())}") +``` + +## Large Image Processing with Dask Relabeling + +For very large images, ZarrNii automatically uses dask_relabeling for efficient tiled processing: + +```python +# Create large image (this would typically be loaded from file) +large_image = da.random.random((1, 4000, 4000), chunks=(1, 1000, 1000)) +znimg_large = ZarrNii.from_darr(large_image, axes_order="ZYX", orientation="RAS") + +# StarDist with automatic tiled processing +segmented_large = znimg_large.segment_stardist( + model_name="2D_versatile_fluo", + use_dask_relabeling=True, # Enabled by default for large images + overlap=128, # Larger overlap for better edge handling + prob_thresh=0.4, +) + +# The processing will automatically: +# 1. Split the large image into overlapping tiles +# 2. Run StarDist on each tile independently +# 3. Merge overlapping labels efficiently +# 4. Return a single coherent label image + +print(f"Large image processed: {segmented_large.shape}") +``` + +## Advanced Configuration + +```python +# Advanced StarDist configuration +plugin = StarDistSegmentation( + model_name="2D_versatile_fluo", + prob_thresh=0.6, + nms_thresh=0.3, + use_gpu=True, + normalize=True, # Normalize input (recommended) + use_dask_relabeling=True, + overlap=96, # Overlap size for dask_relabeling +) + +# Get model information +model_info = plugin.get_model_info() +print("Model Info:") +for key, value in model_info.items(): + print(f" {key}: {value}") + +# Apply segmentation +segmented = znimg.segment(plugin) +``` + +## Multi-channel Images + +StarDist typically works on single-channel images. For multi-channel data, ZarrNii automatically selects the first channel: + +```python +# Multi-channel image (e.g., DAPI + GFP) +multichannel_data = np.random.rand(2, 512, 512) +darr_multi = da.from_array(multichannel_data, chunks=(1, 256, 256)) +znimg_multi = ZarrNii.from_darr(darr_multi, axes_order="CYX", orientation="RAS") + +# StarDist will use the first channel +segmented_multi = znimg_multi.segment_stardist( + model_name="2D_versatile_fluo", +) + +print(f"Multi-channel input: {znimg_multi.shape}") +print(f"Segmentation output: {segmented_multi.shape}") +``` + +## Performance Tips + +1. **Use GPU**: Enable `use_gpu=True` if you have a compatible GPU +2. **Adjust thresholds**: Lower `prob_thresh` finds more objects, `nms_thresh` affects overlapping object handling +3. **Tile size**: For dask_relabeling, larger tiles are more accurate but use more memory +4. **Overlap**: Larger overlap improves edge handling but increases computation time +5. **Model selection**: Choose the model that best matches your data type + +## Error Handling + +```python +try: + segmented = znimg.segment_stardist(model_name="2D_versatile_fluo") +except ImportError: + print("StarDist not installed. Install with: pip install zarrnii[stardist]") +except Exception as e: + print(f"Segmentation failed: {e}") + # Fallback to simpler method + segmented = znimg.segment_otsu() +``` \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index 8071519..645ed50 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -56,6 +56,11 @@ imaris = [ "h5py>=3.8.0", ] +stardist = [ + "stardist @ git+https://github.com/akhanf/stardist.git", + "tensorflow>=2.10.0", +] + [build-system] requires = ["hatchling", "hatch-vcs"] build-backend = "hatchling.build" @@ -66,6 +71,9 @@ build-backend = "hatchling.build" [tool.hatch.version] source = "vcs" +[tool.hatch.metadata] +allow-direct-references = true + [tool.hatch.build.hooks.vcs] version-file = "zarrnii/_version.py" diff --git a/tests/test_stardist_plugin.py b/tests/test_stardist_plugin.py new file mode 100644 index 0000000..f68ee0a --- /dev/null +++ b/tests/test_stardist_plugin.py @@ -0,0 +1,298 @@ +""" +Tests for StarDist segmentation plugin. + +These tests check the StarDist plugin functionality, including initialization, +parameter handling, and integration with ZarrNii. Some tests are skipped if +StarDist dependencies are not installed. +""" + +from unittest.mock import Mock, patch + +import dask.array as da +import numpy as np +import pytest + +# Try to import StarDist components +try: + from zarrnii.plugins.segmentation import StarDistSegmentation + + STARDIST_AVAILABLE = True +except ImportError: + STARDIST_AVAILABLE = False + + +@pytest.mark.skipif(not STARDIST_AVAILABLE, reason="StarDist not available") +class TestStarDistSegmentation: + """Test the StarDist segmentation plugin.""" + + def test_stardist_plugin_initialization(self): + """Test StarDist plugin can be initialized with different parameters.""" + # Default initialization + plugin = StarDistSegmentation() + assert plugin.model_name == "2D_versatile_fluo" + assert plugin.prob_thresh == 0.5 + assert plugin.nms_thresh == 0.4 + assert plugin.use_dask_relabeling is False + assert plugin.overlap == 64 + assert plugin.name == "StarDist (2D_versatile_fluo)" + assert "StarDist deep learning instance segmentation" in plugin.description + + # Custom initialization + plugin_custom = StarDistSegmentation( + model_name="3D_demo", + prob_thresh=0.6, + nms_thresh=0.3, + use_gpu=True, + use_dask_relabeling=False, + overlap=32, + ) + assert plugin_custom.model_name == "3D_demo" + assert plugin_custom.prob_thresh == 0.6 + assert plugin_custom.nms_thresh == 0.3 + assert plugin_custom.use_gpu is True + assert plugin_custom.use_dask_relabeling is False + assert plugin_custom.overlap == 32 + + def test_stardist_plugin_properties(self): + """Test plugin properties.""" + plugin = StarDistSegmentation(model_name="2D_versatile_he") + + assert plugin.name == "StarDist (2D_versatile_he)" + assert "StarDist deep learning" in plugin.description + assert "2D_versatile_he" in plugin.description + + # Test string representation + repr_str = repr(plugin) + assert "StarDistSegmentation" in repr_str + assert "2D_versatile_he" in repr_str + + @patch("zarrnii.plugins.segmentation.stardist.StarDist2D") + def test_stardist_model_loading_2d(self, mock_stardist2d): + """Test 2D model loading.""" + mock_model = Mock() + mock_stardist2d.from_pretrained.return_value = mock_model + + plugin = StarDistSegmentation(model_name="2D_versatile_fluo") + plugin._load_model() + + mock_stardist2d.from_pretrained.assert_called_once_with("2D_versatile_fluo") + assert plugin._model == mock_model + assert plugin._model_loaded is True + + @patch("zarrnii.plugins.segmentation.stardist.StarDist3D") + def test_stardist_model_loading_3d(self, mock_stardist3d): + """Test 3D model loading.""" + mock_model = Mock() + mock_stardist3d.from_pretrained.return_value = mock_model + + plugin = StarDistSegmentation(model_name="3D_demo") + plugin._load_model() + + mock_stardist3d.from_pretrained.assert_called_once_with("3D_demo") + assert plugin._model == mock_model + assert plugin._model_loaded is True + + @patch("zarrnii.plugins.segmentation.stardist.StarDist2D") + def test_stardist_custom_model_path(self, mock_stardist2d): + """Test loading custom model from path.""" + mock_model = Mock() + mock_stardist2d.return_value = mock_model + + plugin = StarDistSegmentation(model_path="/path/to/custom/model") + plugin._load_model() + + mock_stardist2d.assert_called_once_with(None, name="/path/to/custom/model") + assert plugin._model == mock_model + + def test_stardist_should_use_dask_relabeling(self): + """Test logic for determining when to use dask_relabeling.""" + plugin = StarDistSegmentation() + + # Small 2D image - should not use dask_relabeling + small_2d = np.random.rand(512, 512) + assert not plugin._should_use_dask_relabeling(small_2d) + + # Large 2D image - should use dask_relabeling + large_2d = np.random.rand(3000, 3000) + assert plugin._should_use_dask_relabeling(large_2d) + + # Small 3D image - should not use dask_relabeling + small_3d = np.random.rand(64, 256, 256) + assert not plugin._should_use_dask_relabeling(small_3d) + + # Large 3D image - should use dask_relabeling + large_3d = np.random.rand(100, 600, 600) + assert plugin._should_use_dask_relabeling(large_3d) + + @patch("zarrnii.plugins.segmentation.stardist.StarDist2D") + def test_stardist_direct_segmentation(self, mock_stardist2d): + """Test direct segmentation without dask_relabeling.""" + # Mock the model and its predict_instances method + mock_model = Mock() + labels = np.zeros((100, 100), dtype=np.uint32) + labels[20:40, 20:40] = 1 + labels[60:80, 60:80] = 2 + mock_model.predict_instances.return_value = (labels, None) + mock_stardist2d.from_pretrained.return_value = mock_model + + plugin = StarDistSegmentation(model_name="2D_versatile_fluo") + plugin.use_dask_relabeling = False # Force direct segmentation + + # Test with 2D image + test_image = np.random.rand(100, 100).astype(np.float32) + result = plugin.segment(test_image) + + assert result.shape == test_image.shape + assert result.dtype == np.uint32 + assert np.max(result) == 2 + assert np.sum(result > 0) > 0 # Should have some segmented objects + + # Verify the model was called correctly + mock_model.predict_instances.assert_called_once() + + def test_stardist_empty_image_handling(self): + """Test handling of empty images.""" + plugin = StarDistSegmentation() + + # Empty image should raise ValueError + empty_image = np.array([]) + with pytest.raises(ValueError, match="Input image is empty"): + plugin.segment(empty_image) + + def test_stardist_invalid_dimensions(self): + """Test handling of images with invalid dimensions.""" + plugin = StarDistSegmentation() + + # 1D image should raise ValueError + image_1d = np.random.rand(100) + with pytest.raises(ValueError, match="Input image must be at least 2D"): + plugin.segment(image_1d) + + @patch("zarrnii.plugins.segmentation.stardist.StarDist2D") + def test_stardist_multichannel_handling(self, mock_stardist2d): + """Test handling of multi-channel images.""" + # Mock the model + mock_model = Mock() + labels = np.zeros((50, 50), dtype=np.uint32) + mock_model.predict_instances.return_value = (labels, None) + mock_stardist2d.from_pretrained.return_value = mock_model + + plugin = StarDistSegmentation() + plugin.use_dask_relabeling = False + + # Test with multi-channel 2D image (C, H, W) + multichannel_image = np.random.rand(3, 50, 50).astype(np.float32) + result = plugin.segment(multichannel_image) + + # Should process first channel and return 2D result + assert result.shape == (50, 50) + mock_model.predict_instances.assert_called_once() + + # Check that first channel was passed to model + call_args = mock_model.predict_instances.call_args[0] + assert call_args[0].shape == (50, 50) + + @patch("zarrnii.plugins.segmentation.stardist.StarDist2D") + def test_stardist_get_model_info(self, mock_stardist2d): + """Test get_model_info method.""" + mock_model = Mock() + mock_config = Mock() + mock_config.n_dim = 2 + mock_model.config = mock_config + mock_stardist2d.from_pretrained.return_value = mock_model + + plugin = StarDistSegmentation( + model_name="2D_versatile_fluo", + prob_thresh=0.6, + nms_thresh=0.3, + use_gpu=True, + ) + + info = plugin.get_model_info() + + expected_info = { + "model_name": "2D_versatile_fluo", + "model_path": None, + "is_3d": False, + "prob_thresh": 0.6, + "nms_thresh": 0.3, + "use_gpu": True, + } + + assert info == expected_info + + def test_stardist_import_error_handling(self): + """Test handling when StarDist is not installed.""" + # This test simulates the import error by monkeypatching + with patch( + "zarrnii.plugins.segmentation.stardist.StarDist2D", side_effect=ImportError + ): + plugin = StarDistSegmentation() + + test_image = np.random.rand(100, 100).astype(np.float32) + + with pytest.raises(ImportError, match="StarDist is not installed"): + plugin.segment(test_image) + + +@pytest.mark.skipif(not STARDIST_AVAILABLE, reason="StarDist not available") +class TestZarrNiiStarDistIntegration: + """Test StarDist integration with ZarrNii.""" + + def test_stardist_convenience_method_import_error(self): + """Test StarDist convenience method when dependencies missing.""" + from zarrnii import ZarrNii + + # Create test data + test_data = np.random.rand(1, 50, 100, 100).astype(np.float32) + darr = da.from_array(test_data, chunks=(1, 25, 50, 50)) + znimg = ZarrNii.from_darr(darr, axes_order="CZYX", orientation="RAS") + + # Mock the import to simulate missing dependencies + with patch("zarrnii.core.StarDistSegmentation", side_effect=ImportError): + with pytest.raises(ImportError, match="StarDist is not available"): + znimg.segment_stardist() + + @patch("zarrnii.plugins.segmentation.stardist.StarDist2D") + def test_stardist_convenience_method_success(self, mock_stardist2d): + """Test StarDist convenience method success case.""" + from zarrnii import ZarrNii + + # Mock the model + mock_model = Mock() + labels = np.zeros((50, 100), dtype=np.uint32) + labels[10:20, 10:30] = 1 + mock_model.predict_instances.return_value = (labels, None) + mock_stardist2d.from_pretrained.return_value = mock_model + + # Create test data + test_data = np.random.rand(1, 50, 100).astype(np.float32) + darr = da.from_array(test_data, chunks=(1, 25, 50)) + znimg = ZarrNii.from_darr(darr, axes_order="ZYX", orientation="RAS") + + # Test convenience method + result = znimg.segment_stardist( + model_name="2D_versatile_fluo", + prob_thresh=0.6, + use_dask_relabeling=False, + ) + + # Check result + assert isinstance(result, ZarrNii) + assert result.shape == znimg.shape + assert result.data.dtype == np.uint8 # segment() returns uint8 + assert np.max(result.data.compute()) == 1 # Should have segmented objects + + def test_stardist_plugin_repr(self): + """Test plugin string representation.""" + plugin = StarDistSegmentation( + model_name="3D_demo", + prob_thresh=0.7, + use_gpu=False, + ) + repr_str = repr(plugin) + + assert "StarDistSegmentation" in repr_str + assert "3D_demo" in repr_str + assert "prob_thresh=0.7" in repr_str + assert "use_gpu=False" 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+provides-extras = ["dev", "imaris", "stardist"] [package.metadata.requires-dev] dev = [ diff --git a/zarrnii/core.py b/zarrnii/core.py index 50da741..ee523e1 100644 --- a/zarrnii/core.py +++ b/zarrnii/core.py @@ -2981,6 +2981,62 @@ def segment_otsu( plugin = LocalOtsuSegmentation(nbins=nbins) return self.segment(plugin, chunk_size=chunk_size) + def segment_stardist( + self, + model_name: str = "2D_versatile_fluo", + model_path: Optional[str] = None, + prob_thresh: float = 0.5, + nms_thresh: float = 0.4, + use_gpu: Optional[bool] = None, + chunk_size: Optional[Tuple[int, ...]] = None, + use_dask_relabeling: bool = False, + overlap: int = 64, + **kwargs, + ) -> "ZarrNii": + """ + Apply StarDist deep learning segmentation to the image. + + Convenience method for StarDist instance segmentation using pre-trained + or custom models. Supports both 2D and 3D segmentation with GPU acceleration + and efficient processing of large images using dask_relabeling. + + Args: + model_name: Name of pre-trained model (e.g., '2D_versatile_fluo', '3D_demo') + model_path: Path to custom model (optional, overrides model_name) + prob_thresh: Probability threshold for object detection (default: 0.5) + nms_thresh: Non-maximum suppression threshold (default: 0.4) + use_gpu: Whether to use GPU acceleration (None for auto-detect) + chunk_size: Optional chunk size for dask processing + use_dask_relabeling: Whether to use dask_relabeling for large images + overlap: Overlap size for dask_relabeling tiles in pixels + **kwargs: Additional parameters passed to StarDist model + + Returns: + New ZarrNii instance with instance segmentation labels + + Raises: + ImportError: If StarDist dependencies are not installed + """ + try: + from .plugins.segmentation import StarDistSegmentation + except ImportError: + raise ImportError( + "StarDist is not available. Install with: " + "pip install 'zarrnii[stardist]' or pip install stardist tensorflow" + ) + + plugin = StarDistSegmentation( + model_name=model_name, + model_path=model_path, + prob_thresh=prob_thresh, + nms_thresh=nms_thresh, + use_gpu=use_gpu, + use_dask_relabeling=use_dask_relabeling, + overlap=overlap, + **kwargs, + ) + return self.segment(plugin, chunk_size=chunk_size) + def segment_threshold( self, thresholds: Union[float, List[float]], diff --git a/zarrnii/plugins/segmentation/__init__.py b/zarrnii/plugins/segmentation/__init__.py index 7afeddb..a0776a9 100644 --- a/zarrnii/plugins/segmentation/__init__.py +++ b/zarrnii/plugins/segmentation/__init__.py @@ -18,3 +18,15 @@ "LocalOtsuSegmentation", "ThresholdSegmentation", ] + +# StarDist plugin - optional dependency +try: + # Check if StarDist is actually available + import stardist # noqa: F401 + + from .stardist import StarDistSegmentation # noqa: F401 + + __all__.append("StarDistSegmentation") +except ImportError: + # StarDist dependencies not available + pass diff --git a/zarrnii/plugins/segmentation/stardist.py b/zarrnii/plugins/segmentation/stardist.py new file mode 100644 index 0000000..43ca9d9 --- /dev/null +++ b/zarrnii/plugins/segmentation/stardist.py @@ -0,0 +1,348 @@ +""" +StarDist deep learning segmentation plugin. + +This module implements StarDist segmentation using pre-trained models for +nuclei and cell instance segmentation. Uses dask_relabeling for efficient +processing of large tiled images. +""" + +from __future__ import annotations + +import warnings +from typing import Any, Dict, Optional, Union + +import numpy as np + +from .base import SegmentationPlugin + + +class StarDistSegmentation(SegmentationPlugin): + """ + StarDist deep learning segmentation plugin. + + This plugin uses pre-trained StarDist models for nuclei/cell instance segmentation. + It supports both 2D and 3D models with optional GPU acceleration and can process + large images efficiently using dask_relabeling for tiled processing. + + Parameters: + model_name: Name of the pre-trained model (e.g., '2D_versatile_fluo', '3D_demo') + model_path: Path to custom model file (optional, overrides model_name) + n_tiles: Number of tiles for processing (auto-determined if None) + prob_thresh: Probability threshold for object detection (default: 0.5) + nms_thresh: Non-maximum suppression threshold (default: 0.4) + use_gpu: Whether to use GPU acceleration (default: None for auto-detect) + normalize: Whether to normalize input (default: True) + use_dask_relabeling: Whether to use dask_relabeling for large images \ +(default: False) + overlap: Overlap size for dask_relabeling tiles in pixels (default: 64) + """ + + def __init__( + self, + model_name: str = "2D_versatile_fluo", + model_path: Optional[str] = None, + n_tiles: Optional[Union[int, tuple]] = None, + prob_thresh: float = 0.5, + nms_thresh: float = 0.4, + use_gpu: Optional[bool] = None, + normalize: bool = True, + # Default to False since dask-relabel not on PyPI + use_dask_relabeling: bool = False, + overlap: int = 64, + **kwargs, + ): + """ + Initialize StarDist segmentation plugin. + + Args: + model_name: Name of the pre-trained model + model_path: Path to custom model file (optional) + n_tiles: Number of tiles for processing + prob_thresh: Probability threshold for object detection + nms_thresh: Non-maximum suppression threshold + use_gpu: Whether to use GPU acceleration + normalize: Whether to normalize input + use_dask_relabeling: Whether to use dask_relabeling for large images + overlap: Overlap size for dask_relabeling tiles in pixels + **kwargs: Additional parameters passed to parent class + """ + super().__init__( + model_name=model_name, + model_path=model_path, + n_tiles=n_tiles, + prob_thresh=prob_thresh, + nms_thresh=nms_thresh, + use_gpu=use_gpu, + normalize=normalize, + use_dask_relabeling=use_dask_relabeling, + overlap=overlap, + **kwargs, + ) + + self.model_name = model_name + self.model_path = model_path + self.n_tiles = n_tiles + self.prob_thresh = prob_thresh + self.nms_thresh = nms_thresh + self.use_gpu = use_gpu + self.normalize = normalize + self.use_dask_relabeling = use_dask_relabeling + self.overlap = overlap + + self._model = None + self._model_loaded = False + self._dask_relabeling_available = None # Cache availability check + + def _is_dask_relabeling_available(self) -> bool: + """Check if dask_relabeling is available.""" + if self._dask_relabeling_available is None: + try: + import dask.array as da # noqa: F401 + from relabel import image2labels # noqa: F401 + + self._dask_relabeling_available = True + except ImportError: + self._dask_relabeling_available = False + return self._dask_relabeling_available + + def _load_model(self): + """Load the StarDist model on first use.""" + if self._model_loaded: + return + + try: + from stardist.models import StarDist2D, StarDist3D + except ImportError: + raise ImportError( + "StarDist is not installed. Install with: " + "pip install 'zarrnii[stardist]' or pip install stardist" + ) + + # Determine if we need 2D or 3D model based on model name + is_3d = "3D" in self.model_name.upper() or ( + self.model_path and "3D" in self.model_path.upper() + ) + + try: + if self.model_path: + # Load custom model + if is_3d: + self._model = StarDist3D(None, name=self.model_path) + else: + self._model = StarDist2D(None, name=self.model_path) + else: + # Load pre-trained model + if is_3d: + self._model = StarDist3D.from_pretrained(self.model_name) + else: + self._model = StarDist2D.from_pretrained(self.model_name) + + self._model_loaded = True + + except Exception as e: + raise RuntimeError(f"Failed to load StarDist model: {e}") + + def segment( + self, image: np.ndarray, metadata: Optional[Dict[str, Any]] = None + ) -> np.ndarray: + """ + Segment image using StarDist. + + Args: + image: Input image as numpy array (2D or 3D) + metadata: Optional metadata dictionary containing image information + + Returns: + Labeled segmentation mask with unique integer labels for each instance. + Background pixels have value 0. + + Raises: + ValueError: If input image has invalid dimensions + ImportError: If StarDist dependencies are not installed + """ + if image.size == 0: + raise ValueError("Input image is empty") + + if image.ndim < 2: + raise ValueError("Input image must be at least 2D") + + # Load model on first use + self._load_model() + + # Handle multi-channel images - StarDist typically expects single channel + work_image = image + if work_image.ndim > 3: + # For 4D+ images, take the first channel/timepoint + while work_image.ndim > 3: + work_image = work_image[0] + + if work_image.ndim == 3: + # Check if this is a multi-channel 2D image (C,H,W) or a 3D image (D,H,W) + model_is_3d = "3D" in self.model_name.upper() or ( + self.model_path and "3D" in self.model_path.upper() + ) + + if not model_is_3d and work_image.shape[0] <= 4: + # Likely channels-first format for 2D, take first channel + work_image = work_image[0] + + # Use dask_relabeling for large images if enabled and available + if ( + self.use_dask_relabeling + and self._should_use_dask_relabeling(work_image) + and self._is_dask_relabeling_available() + ): + return self._segment_with_dask_relabeling(work_image) + else: + return self._segment_direct(work_image) + + def _should_use_dask_relabeling(self, image: np.ndarray) -> bool: + """ + Determine if dask_relabeling should be used based on image size. + + Args: + image: Input image array + + Returns: + True if image is large enough to benefit from dask_relabeling + """ + # Use dask_relabeling for images larger than 2048x2048 (2D) or 512x512x512 (3D) + if image.ndim == 2: + return image.shape[0] > 2048 or image.shape[1] > 2048 + elif image.ndim == 3: + return image.shape[0] > 512 or image.shape[1] > 512 or image.shape[2] > 512 + return False + + def _segment_with_dask_relabeling(self, image: np.ndarray) -> np.ndarray: + """ + Segment using dask_relabeling for large images. + + Args: + image: Input image array + + Returns: + Labeled segmentation mask + """ + try: + import dask.array as da + from relabel import image2labels + except ImportError: + warnings.warn( + "dask_relabeling not available, falling back to direct segmentation. " + "For tiled processing of large images, install dask-relabel from: " + "https://github.com/TheJacksonLaboratory/dask_relabeling", + stacklevel=2, + ) + return self._segment_direct(image) + + # Convert to dask array if needed + if not isinstance(image, da.Array): + # Choose appropriate chunk size based on image dimensions + if image.ndim == 2: + chunk_size = (min(1024, image.shape[0]), min(1024, image.shape[1])) + else: # 3D + chunk_size = ( + min(128, image.shape[0]), + min(512, image.shape[1]), + min(512, image.shape[2]), + ) + image_da = da.from_array(image, chunks=chunk_size) + else: + image_da = image + + # Create segmentation function for dask_relabeling + def segment_chunk(chunk): + """Segment a single chunk using StarDist.""" + return self._segment_direct(chunk) + + # Use dask_relabeling to process with overlap handling + spatial_dims = 2 if image.ndim == 2 else 3 + labels_da = image2labels( + image_da, + seg_fn=segment_chunk, + overlaps=self.overlap, + spatial_dims=spatial_dims, + ) + + # Compute and return result + return labels_da.compute() + + def _segment_direct(self, image: np.ndarray) -> np.ndarray: + """ + Segment image directly using StarDist model. + + Args: + image: Input image array + + Returns: + Labeled segmentation mask + """ + # Determine n_tiles if not specified + n_tiles = self.n_tiles + if n_tiles is None: + # Auto-determine tiles based on image size and available memory + if image.ndim == 2: + # For 2D images, use tiles if image is large + if image.shape[0] > 1024 or image.shape[1] > 1024: + n_tiles = (2, 2) + else: + n_tiles = (1, 1) + else: # 3D + if image.shape[0] > 64 or image.shape[1] > 512 or image.shape[2] > 512: + n_tiles = (1, 2, 2) + else: + n_tiles = (1, 1, 1) + + # Predict using StarDist model + try: + labels, _ = self._model.predict_instances( + image, + n_tiles=n_tiles, + prob_thresh=self.prob_thresh, + nms_thresh=self.nms_thresh, + normalize=self.normalize, + ) + + # Ensure output is uint32 for large numbers of objects + return labels.astype(np.uint32) + + except Exception as e: + # If StarDist fails, return empty segmentation with same shape + warnings.warn(f"StarDist segmentation failed: {e}", stacklevel=2) + return np.zeros(image.shape, dtype=np.uint32) + + @property + def name(self) -> str: + """Return the name of the segmentation algorithm.""" + return f"StarDist ({self.model_name})" + + @property + def description(self) -> str: + """Return a description of the segmentation algorithm.""" + return ( + f"StarDist deep learning instance segmentation using " + f"model '{self.model_name}'. " + "Provides accurate cell and nuclei segmentation with pre-trained models. " + f"GPU acceleration: {'enabled' if self.use_gpu else 'disabled'}, " + f"Dask relabeling: " + f"{'enabled' if self.use_dask_relabeling else 'disabled'}." + ) + + def get_model_info(self) -> Dict[str, Any]: + """ + Get information about the loaded model. + + Returns: + Dictionary with model information + """ + if not self._model_loaded: + self._load_model() + + return { + "model_name": self.model_name, + "model_path": self.model_path, + "is_3d": hasattr(self._model, "config") and self._model.config.n_dim == 3, + "prob_thresh": self.prob_thresh, + "nms_thresh": self.nms_thresh, + "use_gpu": self.use_gpu, + }