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- use lower resolution pixel classifier
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| This file might no be up to date nor complete. Please check the Releases page for more information on versions. | ||
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| ## Version 2024.12.10 | ||
| - Fixed offset writing geojson file in segmentation module. | ||
| - Added original_pixelsize parameters to rescale coordinates to match final image size. This allows the use of QuPath pixel classifier trained on resized image, eg. with a lower Resolution parameter (higher pixel size). | ||
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| ## Version 2024.11.19 | ||
| - Initial public release. |
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| [project] | ||
| name = "histoquant" | ||
| version = "2024.11.27" | ||
| version = "2024.12.10" | ||
| authors = [{ name = "Guillaume Le Goc", email = "[email protected]" }] | ||
| description = "Quantification of objects in histological slices" | ||
| readme = "README.md" | ||
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@@ -18,8 +18,8 @@ | |
| To exclude objects near the edges of an ROI, specify the path to masks stored as images | ||
| with the same names as probabilities images (without their suffix). | ||
| Author : Guillaume Le Goc ([email protected]) @ NeuroPSI | ||
| Version : 2024.11.27 | ||
| author : Guillaume Le Goc ([email protected]) @ NeuroPSI | ||
| version : 2024.12.10 | ||
| """ | ||
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@@ -37,17 +37,22 @@ | |
| pd.options.mode.copy_on_write = True # prepare for pandas 3 | ||
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| # --- Parameters | ||
| IMAGES_DIR = "/path/to/images/to/segment" | ||
| IMAGES_DIR = "/path/to/images" | ||
| """Full path to the images to segment.""" | ||
| MASKS_DIR = "path/to/corresponding/masks" | ||
| """Full path to the masks, to exclude objects near the brain edges (set to None or empty | ||
| string to disable this feature).""" | ||
| MASKS_EXT = "tiff" | ||
| """Masks files extension.""" | ||
| SEGTYPE = "fibers" | ||
| SEGTYPE = "boutons" | ||
| """Type of segmentation.""" | ||
| IMG_SUFFIX = "_Probabilities.tiff" | ||
| """Images suffix, including extension. Masks must be the same name without the suffix.""" | ||
| ORIGINAL_PIXELSIZE = 0.4500 | ||
| """Original images pixel size in microns. This is in case the pixel classifier uses | ||
| a lower resolution, yielding smaller probability maps, so output objects coordinates | ||
| need to be rescaled to the full size images. The pixel size is written in the "Image" | ||
| tab in QuPath.""" | ||
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| CHANNELS_PARAMS = [ | ||
| { | ||
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@@ -60,7 +65,7 @@ | |
| { | ||
| "name": "dsred", | ||
| "target_channel": 1, | ||
| "proba_threshold": 0.85, | ||
| "proba_threshold": 0.65, | ||
| "qp_class": "Fibers: DsRed", | ||
| "qp_color": [224, 153, 18], | ||
| }, | ||
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@@ -80,13 +85,13 @@ | |
| - qp_class: str, name of QuPath classification | ||
| - qp_color: list of RGB values, associated color""" | ||
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| EDGE_DIST = 50 | ||
| EDGE_DIST = 0 | ||
| """Distance to brain edge to ignore, in µm. 0 to disable.""" | ||
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| FILTERS = { | ||
| "length_low": 1.5, # minimal length in microns - for lines | ||
| "area_low": 1.1, # minimal area in µm² - for polygons and points | ||
| "area_high": 10, # maximal area in µm² - for polygons and points | ||
| "area_low": 10, # minimal area in µm² - for polygons and points | ||
| "area_high": 1000, # maximal area in µm² - for polygons and points | ||
| "ecc_low": 0.0, # minimal eccentricity - for polygons and points (0 = circle) | ||
| "ecc_high": 0.9, # maximal eccentricity - for polygons and points (1 = line) | ||
| "dist_thresh": 30, # maximal inter-point distance in µm - for points | ||
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@@ -237,7 +242,9 @@ def parameters_as_dict( | |
| } | ||
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| def write_parameters(outfile: str, parameters: dict, filters: dict): | ||
| def write_parameters( | ||
| outfile: str, parameters: dict, filters: dict, original_pixelsize: float | ||
| ): | ||
| """ | ||
| Write parameters to `outfile`. | ||
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@@ -252,12 +259,16 @@ def write_parameters(outfile: str, parameters: dict, filters: dict): | |
| General parameters. | ||
| filters : dict | ||
| Filters parameters. | ||
| original_pixelsize : float | ||
| Size of pixels in original image. | ||
| """ | ||
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| with open(outfile, "w") as fid: | ||
| fid.writelines(f"date = {datetime.now().strftime('%d-%B-%Y %H:%M:%S')}\n") | ||
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| fid.writelines(f"original_pixelsize = {original_pixelsize}\n") | ||
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| for key, value in parameters.items(): | ||
| fid.writelines(f"{key} = {value}\n") | ||
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@@ -271,6 +282,7 @@ def process_directory( | |
| images_dir: str, | ||
| img_suffix: str = "", | ||
| segtype: str = "", | ||
| original_pixelsize: float = 1.0, | ||
| target_channel: int = 0, | ||
| proba_threshold: float = 0.0, | ||
| qupath_class: str = "Object", | ||
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@@ -292,6 +304,8 @@ def process_directory( | |
| Images suffix, including extension. | ||
| segtype : str | ||
| Segmentation type. | ||
| original_pixelsize : float | ||
| Original images pixel size in microns. | ||
| target_channel : int | ||
| Index of the channel containning the objects of interest (eg. not the | ||
| background), in the probability map (*not* the original images channels). | ||
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@@ -339,13 +353,16 @@ def process_directory( | |
| if os.path.isfile(param_file): | ||
| raise FileExistsError("Parameters file already exists.") | ||
| else: | ||
| write_parameters(param_file, parameters, filters) | ||
| write_parameters(param_file, parameters, filters, original_pixelsize) | ||
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| # convert parameters to pixels | ||
| # convert parameters to pixels in probability map | ||
| pixelsize = hq.seg.get_pixelsize(images_list[0]) # get pixel size | ||
| edge_dist = int(edge_dist / pixelsize) | ||
| filters = hq.seg.convert_to_pixels(filters, pixelsize) | ||
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| # get rescaling factor | ||
| rescale_factor = pixelsize / original_pixelsize | ||
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| # get GeoJSON properties | ||
| geojson_props = get_geojson_properties( | ||
| qupath_class, qupath_color, objtype=QUPATH_TYPE | ||
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@@ -385,7 +402,10 @@ def process_directory( | |
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| if seg_method == "lines": | ||
| collection = hq.seg.segment_lines( | ||
| img, geojson_props, minsize=filters["length_low"] | ||
| img, | ||
| geojson_props, | ||
| minsize=filters["length_low"], | ||
| rescale_factor=rescale_factor, | ||
| ) | ||
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| elif seg_method == "polygons": | ||
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@@ -396,6 +416,7 @@ def process_directory( | |
| area_max=filters["area_high"], | ||
| ecc_min=filters["ecc_low"], | ||
| ecc_max=filters["ecc_high"], | ||
| rescale_factor=rescale_factor, | ||
| ) | ||
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| elif seg_method == "points": | ||
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@@ -407,6 +428,7 @@ def process_directory( | |
| ecc_min=filters["ecc_low"], | ||
| ecc_max=filters["ecc_high"], | ||
| dist_thresh=filters["dist_thresh"], | ||
| rescale_factor=rescale_factor, | ||
| ) | ||
| else: | ||
| # we already printed an error message | ||
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@@ -445,6 +467,7 @@ def make_suffix(s): | |
| IMAGES_DIR, | ||
| img_suffix=IMG_SUFFIX, | ||
| segtype=SEGTYPE, | ||
| original_pixelsize=ORIGINAL_PIXELSIZE, | ||
| target_channel=param["target_channel"], | ||
| proba_threshold=param["proba_threshold"], | ||
| qupath_class=param["qp_class"], | ||
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