Cell-Segmentation/cellSegmentation.py at main · SarderLab/Cell-Segmentation · GitHub

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from types import SimpleNamespace
from get_dapi import extract_dapi_channel
from segment_nuclei import segment_whole_image
from number_of_nuclei import count_nuclei
from mask_segmentation_with_dapi import apply_tissue_mask
from nuclei_size_filtering import filter_nuclei_by_size
from skimage import io, color
import tifffile as tiff
from convert_tif_to_xml import segmentation_to_polygon_xml
from convert_xml_to_json import xml_to_json
import tiffslide
import numpy as np
from skimage.color import rgb2gray
import time

def main(args):

    # Step 1: Extract DAPI channel
    if args.image_type == 'DAPI':
        try:
            start_time = time.time()
            dapi = extract_dapi_channel(args.reg_path, args.dapi_channel)
            end_time = time.time()
            print(f"DAPI loading time: {end_time - start_time:.2f} seconds")
        except (FileNotFoundError, ValueError) as e:
            print(f"Error: {e}")

    # Step 2: Run segmentation on dapi
    segmentation = segment_whole_image(
        image = dapi,
        n_gpus= args.n_gpus,
        channels= args.channels,
        diameter= args.diameter,
        batch_size= args.batch_size,
        flow_threshold= args.flow_threshold,
        cellprob_threshold= args.cellprob_threshold,
        min_size= args.min_size
    )

    # Step 3: Mask segmentation with DAPI to remove the nuclei outside the tissue section
    if args.maskSeg:
        segmentation = apply_tissue_mask(dapi, segmentation)

    # Step 4: Filter nuclei by size
    if args.filterBySize:
        filter_nuclei_by_size(segmentation, args.outputdir + 'nuclei.xml', args.lower_area_percentile, args.upper_area_percentile)

    else:
        segmentation_to_polygon_xml(segmentation, args.outputdir + 'nuclei.xml')

    # Step 5: Filter nuclei by intensity
    if args.filterByIntensity:
        filtered_tree, removed_tree = process_intensity_filtering(
        xml_file= args.outputdir + 'nuclei.xml',
        tif_file= args.reg_path,
        top_percentage= args.top_percentage,
        px= args.px_padding
    )

        # Output XML paths
        filtered_output_xml = args.outputdir + "filtered_output.xml"  # Replace with desired output path
        removed_output_xml = args.outputdir + "removed_output.xml"  # Replace with desired output path

        # Save the filtered XML file
        filtered_tree.write(filtered_output_xml, pretty_print=True)
        print(f"Filtered XML saved to: {filtered_output_xml}")

        # Save the removed XML file
        removed_tree.write(removed_output_xml, pretty_print=True)
        print(f"Removed XML saved to: {removed_output_xml}")

        print("Intensity filtering process completed.")

        # Convert and update JSON
        xml_to_json(filtered_output_xml, output_json = args.outputdir + "nuclei.json")

    else:
        xml_to_json(args.outputdir + 'nuclei.xml', args.outputdir + "nuclei.json")

if __name__ == '__main__':
    args_dict = {
        'image_type': 'DAPI',
        'reg_path': '/blue/pinaki.sarder/cell-segmentation/Data/Lung/D016-RLL-11B2-6/D016-RLL-11B2-6_Reg.tiff',
        'dapi_channel': 0,
        'n_gpus' : 1,
        'channels' : [0, 0],
        'diameter' : None,  # Auto-estimate object diameter
        'batch_size' : 256,  # Batch size for Cellpose
        'flow_threshold' : 0.4,
        'cellprob_threshold' : 0.0,
        'min_size' : 17,
        'maskSeg' : False,
        'filterBySize': False,
        'filterByIntensity' : False,
        'lower_area_percentile': 0.0,
        'upper_area_percentile': 100.0,
        'outputdir': '/blue/pinaki.sarder/cell-segmentation/Data/Lung/D016-RLL-11B2-6/outputtest/',
        'top_percentage' : 5,
        'px_padding' : 5
    }

    args = SimpleNamespace(**args_dict)

    #print(type(args))
    #print(args.reg_path)
    main(args)
    #main(CLIArgumentParser().parse_args())