selection

LMDSelection

class scportrait.pipeline.selection.LMDSelection(*args, **kwargs)

Bases: ProcessingStep

Select single cells from a segmented sdata file and generate cutting data for the Leica LMD microscope. This method class relies on the functionality of the pylmd library.

process(segmentation_name: str, cell_sets: list[dict], calibration_marker: array, name: str | None = None)

Process function for selecting cells and generating their XML. Under the hood this method relies on the pylmd library and utilizies its SegmentationLoader Class.

Parameters:

• segmentation_name (str) – Name of the segmentation to be used for shape generation in the sdata object.

• cell_sets (list of dict) – List of dictionaries containing the sets of cells which should be sorted into a single well. Mandatory keys for each dictionary are: name, classes. Optional keys are: well.

• calibration_marker (numpy.array) – Array of size ‘(3,2)’ containing the calibration marker coordinates in the ‘(row, column)’ format.

Example

# Calibration marker should be defined as (row, column).
marker_0 = np.array([-10, -10])
marker_1 = np.array([-10, 1100])
marker_2 = np.array([1100, 505])

# A numpy Array of shape (3, 2) should be passed.
calibration_marker = np.array([marker_0, marker_1, marker_2])


# Sets of cells can be defined by providing a name and a list of classes in a dictionary.
cells_to_select = [{"name": "dataset1", "classes": [1, 2, 3]}]

# Alternatively, a path to a csv file can be provided.
# If a relative path is provided, it is accessed relativ to the projects base directory.
cells_to_select += [{"name": "dataset2", "classes": "segmentation/class_subset.csv"}]

# If desired, wells can be passed with the individual sets.
cells_to_select += [{"name": "dataset3", "classes": [4, 5, 6], "well": "A1"}]

project.select(cells_to_select, calibration_marker)

Note

The following parameters are required in the config file:

LMDSelection:
    #the number of threads with which multithreaded tasks should be executed
    threads: 10

    # the number of parallel processes to use for generation of cell sets each set
    # will processed with the designated number of threads
    processes_cell_sets: 1

    # defines the channel used for generating cutting masks
    # segmentation.hdf5 => labels => segmentation_channel
    # When using WGA segmentation:
    #    0 corresponds to nuclear masks
    #    1 corresponds to cytosolic masks.
    segmentation_channel: 0

    # dilation of the cutting mask in pixel
    shape_dilation: 10

    # Cutting masks are transformed by binary dilation and erosion
    binary_smoothing: 3

    # number of datapoints which are averaged for smoothing
    # the number of datapoints over an distance of n pixel is 2*n
    convolution_smoothing: 25

    # fold reduction of datapoints for compression
    poly_compression_factor: 30

    # Optimization of the cutting path inbetween shapes
    # optimized paths improve the cutting time and the microscopes focus
    # valid options are ["none", "hilbert", "greedy"]
    path_optimization: "hilbert"

    # Paramter required for hilbert curve based path optimization.
    # Defines the order of the hilbert curve used, which needs to be tuned with the total cutting area.
    # For areas of 1 x 1 mm we recommend at least p = 4,  for whole slides we recommend p = 7.
    hilbert_p: 7

    # Parameter required for greedy path optimization.
    # Instead of a global distance matrix, the k nearest neighbours are approximated.
    # The optimization problem is then greedily solved for the known set of nearest neighbours until the first set of neighbours is exhausted.
    # Established edges are then removed and the nearest neighbour approximation is recursivly repeated.
    greedy_k: 20

    # The LMD reads coordinates as integers which leads to rounding of decimal places.
    # Points spread between two whole coordinates are therefore collapsed to whole coordinates.
    # This can be mitigated by scaling the entire coordinate system by a defined factor.
    # For a resolution of 0.6 um / px a factor of 100 is recommended.
    xml_decimal_transform: 100

    # Overlapping shapes are merged based on a nearest neighbour heuristic.
    # All selected shapes closer than distance_heuristic pixel are checked for overlap.
    distance_heuristic: 300