Source code for neuroconv.datainterfaces.ophys.basesegmentationextractorinterface
import warnings
from typing import Literal
import numpy as np
from pynwb import NWBFile
from pynwb.device import Device
from pynwb.ophys import Fluorescence, ImageSegmentation, ImagingPlane, TwoPhotonSeries
from ...baseextractorinterface import BaseExtractorInterface
from ...utils import (
DeepDict,
fill_defaults,
get_base_schema,
get_schema_from_hdmf_class,
)
[docs]
class BaseSegmentationExtractorInterface(BaseExtractorInterface):
"""Parent class for all SegmentationExtractorInterfaces."""
keywords = ("segmentation", "roi", "cells")
def __init__(self, verbose: bool = False, **source_data):
super().__init__(**source_data)
self.verbose = verbose
self.segmentation_extractor = self._extractor_instance
@property
def roi_ids(self):
"""Get all ROI IDs of the segmentation data."""
return self.segmentation_extractor.get_roi_ids()
[docs]
def get_metadata_schema(self) -> dict:
"""
Generate the metadata schema for Ophys data, updating required fields and properties.
This method builds upon the base schema and customizes it for Ophys-specific metadata, including required
components such as devices, fluorescence data, imaging planes, and two-photon series. It also applies
temporary schema adjustments to handle certain use cases until a centralized metadata schema definition
is available.
Returns
-------
dict
A dictionary representing the updated Ophys metadata schema.
Notes
-----
- Ensures that `Device` and `ImageSegmentation` are marked as required.
- Updates various properties, including ensuring arrays for `ImagingPlane` and `TwoPhotonSeries`.
- Adjusts the schema for `Fluorescence`, including required fields and pattern properties.
- Adds schema definitions for `DfOverF`, segmentation images, and summary images.
- Applies temporary fixes, such as setting additional properties for `ImageSegmentation` to True.
"""
metadata_schema = super().get_metadata_schema()
metadata_schema["required"] = ["Ophys"]
metadata_schema["properties"]["Ophys"] = get_base_schema()
metadata_schema["properties"]["Ophys"]["properties"] = dict(
Device=dict(type="array", minItems=1, items=get_schema_from_hdmf_class(Device)),
)
metadata_schema["properties"]["Ophys"]["properties"].update(
Fluorescence=get_schema_from_hdmf_class(Fluorescence),
ImageSegmentation=get_schema_from_hdmf_class(ImageSegmentation),
ImagingPlane=get_schema_from_hdmf_class(ImagingPlane),
TwoPhotonSeries=get_schema_from_hdmf_class(TwoPhotonSeries),
)
metadata_schema["properties"]["Ophys"]["required"] = ["Device", "ImageSegmentation"]
# Temporary fixes until centralized definition of metadata schemas
metadata_schema["properties"]["Ophys"]["properties"]["ImagingPlane"].update(type="array")
metadata_schema["properties"]["Ophys"]["properties"]["TwoPhotonSeries"].update(type="array")
metadata_schema["properties"]["Ophys"]["properties"]["Fluorescence"].update(required=["name"])
metadata_schema["properties"]["Ophys"]["properties"]["Fluorescence"].pop("additionalProperties")
roi_response_series_schema = metadata_schema["properties"]["Ophys"]["properties"]["Fluorescence"][
"properties"
].pop("roi_response_series")
roi_response_series_schema.pop("maxItems")
roi_response_series_schema["items"].update(required=list())
roi_response_series_per_plane_schema = dict(
type="object", patternProperties={"^[a-zA-Z0-9]+$": roi_response_series_schema["items"]}
)
metadata_schema["properties"]["Ophys"]["properties"]["Fluorescence"].update(
patternProperties={"^(?!name$)[a-zA-Z0-9]+$": roi_response_series_per_plane_schema}
)
metadata_schema["properties"]["Ophys"]["properties"]["ImageSegmentation"]["additionalProperties"] = True
metadata_schema["properties"]["Ophys"]["properties"]["DfOverF"] = metadata_schema["properties"]["Ophys"][
"properties"
]["Fluorescence"]
# NOTE: Would prefer to remove in favor of simply using the up-to-date metadata_schema.json
images_inner_schema = dict(
type="object",
properties=dict(name=dict(type="string"), description=dict(type="string")),
)
summary_images_per_plane_schema = dict(type="object", patternProperties={"^[a-zA-Z0-9]+$": images_inner_schema})
metadata_schema["properties"]["Ophys"]["properties"]["SegmentationImages"] = dict(
type="object",
required=["name"],
properties=dict(
name=dict(type="string", default="SegmentationImages"),
description=dict(type="string"),
),
patternProperties={
"^(?!(name|description)$)[a-zA-Z0-9]+$": summary_images_per_plane_schema,
},
)
fill_defaults(metadata_schema, self.get_metadata())
return metadata_schema
[docs]
def get_metadata(self) -> DeepDict:
from ...tools.roiextractors.roiextractors_pending_deprecation import (
_get_default_ophys_metadata_old_metadata_list,
)
metadata = super().get_metadata()
# Get the default ophys metadata (single source of truth)
ophys_defaults = _get_default_ophys_metadata_old_metadata_list()
# Only include the fields relevant to segmentation (not imaging series)
metadata["Ophys"] = {
"Device": ophys_defaults["Ophys"]["Device"],
"ImagingPlane": ophys_defaults["Ophys"]["ImagingPlane"],
"Fluorescence": ophys_defaults["Ophys"]["Fluorescence"],
"DfOverF": ophys_defaults["Ophys"]["DfOverF"],
"ImageSegmentation": ophys_defaults["Ophys"]["ImageSegmentation"],
"SegmentationImages": ophys_defaults["Ophys"]["SegmentationImages"],
}
return metadata
[docs]
def get_original_timestamps(self) -> np.ndarray:
reinitialized_extractor = self._initialize_extractor(self.source_data)
return reinitialized_extractor.get_timestamps()
[docs]
def set_aligned_timestamps(self, aligned_timestamps: np.ndarray):
self.segmentation_extractor.set_times(times=aligned_timestamps)
[docs]
def add_to_nwbfile(
self,
nwbfile: NWBFile,
metadata: dict | None = None,
*args, # TODO: change to * (keyword only) on or after August 2026
stub_test: bool = False,
include_background_segmentation: bool = False,
include_roi_centroids: bool = True,
include_roi_acceptance: bool = True,
mask_type: Literal["image", "pixel", "voxel"] = "image",
plane_segmentation_name: str | None = None,
iterator_options: dict | None = None,
stub_samples: int = 100,
roi_ids_to_add: list[str | int] | None = None,
):
"""
Add segmentation data to the NWB file.
Parameters
----------
nwbfile : NWBFile
The NWBFile to add the plane segmentation to.
metadata : dict, optional
The metadata for the interface
stub_test : bool, default: False
include_background_segmentation : bool, default: False
Whether to include the background plane segmentation and fluorescence traces in the NWB file. If False,
neuropil traces are included in the main plane segmentation rather than the background plane segmentation.
include_roi_centroids : bool, default: True
Whether to include the ROI centroids on the PlaneSegmentation table.
If there are a very large number of ROIs (such as in whole-brain recordings),
you may wish to disable this for faster write speeds.
include_roi_acceptance : bool, default: True
Whether to include if the detected ROI was 'accepted' or 'rejected'.
If there are a very large number of ROIs (such as in whole-brain recordings), you may wish to disable this for
faster write speeds.
mask_type : str, default: 'image'
There are three types of ROI masks in NWB, 'image', 'pixel', and 'voxel'.
* 'image' masks have the same shape as the reference images the segmentation was applied to, and weight each pixel
by its contribution to the ROI (typically boolean, with 0 meaning 'not in the ROI').
* 'pixel' masks are instead indexed by ROI, with the data at each index being the shape of the image by the number
of pixels in each ROI.
* 'voxel' masks are instead indexed by ROI, with the data at each index being the shape of the volume by the number
of voxels in each ROI.
Specify your choice between these two as mask_type='image', 'pixel', 'voxel'
plane_segmentation_name : str, optional
The name of the plane segmentation to be added.
iterator_options : dict, optional
Options for controlling the iterative write process (buffer size, progress bars) when
writing image masks and traces.
Note: To configure chunk size and compression, use the backend configuration system
via ``get_default_backend_configuration()`` and ``configure_backend()`` after calling
this method. See the backend configuration documentation for details.
stub_samples : int, default: 100
The number of samples (frames) to use for testing.
roi_ids_to_add : list of str or int, optional
The ROI IDs to include in the NWB file. If ``None`` (default), all ROIs are included.
Use this to filter out rejected or unwanted ROIs and reduce file size.
Neuropil traces (e.g., from Suite2p) share the same IDs as their corresponding cells
and are automatically included when those cell IDs are selected.
The IDs must be a subset of the IDs returned by ``self.roi_ids``.
Returns
-------
"""
from ...tools.roiextractors import add_segmentation_to_nwbfile
# TODO: Remove this block in August 2026 or after when positional arguments are no longer supported.
if args:
parameter_names = [
"stub_test",
"include_background_segmentation",
"include_roi_centroids",
"include_roi_acceptance",
"mask_type",
"plane_segmentation_name",
"iterator_options",
"stub_samples",
"roi_ids_to_add",
]
num_positional_args_before_args = 2 # nwbfile, metadata
if len(args) > len(parameter_names):
raise TypeError(
f"add_to_nwbfile() takes at most {len(parameter_names) + num_positional_args_before_args} positional arguments but "
f"{len(args) + num_positional_args_before_args} were given. "
"Note: Positional arguments are deprecated and will be removed in August 2026 or after. Please use keyword arguments."
)
positional_values = dict(zip(parameter_names, args))
passed_as_positional = list(positional_values.keys())
warnings.warn(
f"Passing arguments positionally to add_to_nwbfile is deprecated "
f"and will be removed in August 2026 or after. "
f"The following arguments were passed positionally: {passed_as_positional}. "
"Please use keyword arguments instead.",
FutureWarning,
stacklevel=2,
)
stub_test = positional_values.get("stub_test", stub_test)
include_background_segmentation = positional_values.get(
"include_background_segmentation", include_background_segmentation
)
include_roi_centroids = positional_values.get("include_roi_centroids", include_roi_centroids)
include_roi_acceptance = positional_values.get("include_roi_acceptance", include_roi_acceptance)
mask_type = positional_values.get("mask_type", mask_type)
plane_segmentation_name = positional_values.get("plane_segmentation_name", plane_segmentation_name)
iterator_options = positional_values.get("iterator_options", iterator_options)
stub_samples = positional_values.get("stub_samples", stub_samples)
roi_ids_to_add = positional_values.get("roi_ids_to_add", roi_ids_to_add)
segmentation_extractor = self.segmentation_extractor
if roi_ids_to_add is not None:
segmentation_extractor = segmentation_extractor.select_rois(roi_ids=roi_ids_to_add)
if stub_test:
stub_samples = min([stub_samples, segmentation_extractor.get_num_samples()])
segmentation_extractor = segmentation_extractor.slice_samples(start_sample=0, end_sample=stub_samples)
metadata = metadata or self.get_metadata()
add_segmentation_to_nwbfile(
segmentation_extractor=segmentation_extractor,
nwbfile=nwbfile,
metadata=metadata,
include_background_segmentation=include_background_segmentation,
include_roi_centroids=include_roi_centroids,
include_roi_acceptance=include_roi_acceptance,
mask_type=mask_type,
plane_segmentation_name=plane_segmentation_name,
iterator_options=iterator_options,
)