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infer_subc/organelles/lipid

fixed_infer_LD(in_img)

Procedure to infer cellmask from linearly unmixed input, with a fixed set of parameters for each step in the procedure. i.e. "hard coded"

Parameters

in_img

a 3d image containing all the channels

Returns

LD_body_object mask defined extent of liipid body

Source code in infer_subc/organelles/lipid.py 
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def fixed_infer_LD(in_img: np.ndarray) -> np.ndarray:
    """
    Procedure to infer cellmask from linearly unmixed input, with a *fixed* set of parameters for each step in the procedure.  i.e. "hard coded"

    Parameters
    ------------
    in_img:
        a 3d image containing all the channels

    Returns
    -------------
    LD_body_object
        mask defined extent of liipid body

    """
    median_sz = 0
    gauss_sig = 2.34
    method = "otsu"
    threshold_factor = 0.99  # from cellProfiler
    thresh_min = 0.5
    thresh_max = 1.0
    max_hole_w = 2.5
    small_obj_w = 4

    return infer_LD(
        in_img, median_sz, gauss_sig, method, threshold_factor, thresh_min, thresh_max, max_hole_w, small_obj_w
    )

get_LD(in_img, meta_dict, out_data_path)

load lipid if it exists, otherwise calculate and write to ome.tif file

Parameters

in_img

a 3d np.ndarray image of the inferred organelle (labels or boolean)

meta_dict

dictionary of meta-data (ome)

out_data_path

Path object where tiffs are written to

Returns

exported file name

Source code in infer_subc/organelles/lipid.py 
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def get_LD(in_img: np.ndarray, meta_dict: Dict, out_data_path: Path) -> np.ndarray:
    """
    load lipid if it exists, otherwise calculate and write to ome.tif file

    Parameters
    ------------
    in_img:
        a 3d  np.ndarray image of the inferred organelle (labels or boolean)
    meta_dict:
        dictionary of meta-data (ome)
    out_data_path:
        Path object where tiffs are written to

    Returns
    -------------
    exported file name

    """

    try:
        lipid = import_inferred_organelle("LD", meta_dict, out_data_path)
    except:
        start = time.time()
        print("starting segmentation...")
        lipid = infer_and_export_LD(in_img, meta_dict, out_data_path)
        end = time.time()
        print(f"inferred (and exported) lipid in ({(end - start):0.2f}) sec")

    return lipid

infer_LD(in_img, median_sz, gauss_sig, method, thresh_factor, thresh_min, thresh_max, max_hole_w, small_obj_w)

Procedure to infer peroxisome from linearly unmixed input.

Parameters

in_img

a 3d image containing all the channels

median_sz

width of median filter for signal

gauss_sig

sigma for gaussian smoothing of signal

method

method for applying threshold. "otsu" or "li", "triangle", "median", "ave", "sauvola","multi_otsu","muiltiotsu"

thresh_factor

scaling value for threshold

thresh_min

absolute minumum for threshold

thresh_max

absolute maximum for threshold

max_hole_w

hole filling cutoff for lipid post-processing

small_obj_w

minimu object size cutoff for lipid post-processing

Returns

peroxi_object mask defined extent of peroxisome object

Source code in infer_subc/organelles/lipid.py 
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def infer_LD(
    in_img: np.ndarray,
    median_sz: int,
    gauss_sig: float,
    method: str,
    thresh_factor: float,
    thresh_min: float,
    thresh_max: float,
    max_hole_w: int,
    small_obj_w: int,
) -> np.ndarray:
    """
    Procedure to infer peroxisome from linearly unmixed input.

    Parameters
    ------------
    in_img:
        a 3d image containing all the channels
    median_sz:
        width of median filter for signal
    gauss_sig:
        sigma for gaussian smoothing of  signal
    method:
        method for applying threshold.  "otsu"  or "li", "triangle", "median", "ave", "sauvola","multi_otsu","muiltiotsu"
    thresh_factor:
        scaling value for threshold
    thresh_min:
        absolute minumum for threshold
    thresh_max:
        absolute maximum for threshold
    max_hole_w:
        hole filling cutoff for lipid post-processing
    small_obj_w:
        minimu object size cutoff for lipid post-processing
    Returns
    -------------
    peroxi_object
        mask defined extent of peroxisome object
    """
    LD_ch = LD_CH
    ###################
    # EXTRACT
    ###################
    lipid = select_channel_from_raw(in_img, LD_ch)
    ###################
    # PRE_PROCESSING
    ###################
    lipid = scale_and_smooth(lipid, median_sz=median_sz, gauss_sig=gauss_sig)

    ###################
    # CORE_PROCESSING
    ###################
    bw = apply_threshold(
        lipid, method=method, thresh_factor=thresh_factor, thresh_min=thresh_min, thresh_max=thresh_max
    )

    ###################
    # POST_PROCESSING
    ###################
    # min_hole_w = 0
    struct_obj = fill_and_filter_linear_size(bw, hole_min=0, hole_max=max_hole_w, min_size=small_obj_w)

    return label_uint16(struct_obj)

infer_and_export_LD(in_img, meta_dict, out_data_path)

infer lipid bodies and write inferred lipid to ome.tif file

Parameters

in_img

a 3d np.ndarray image of the inferred organelle (labels or boolean)

meta_dict

dictionary of meta-data (ome)

out_data_path

Path object where tiffs are written to

Returns

exported file name

Source code in infer_subc/organelles/lipid.py 
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def infer_and_export_LD(in_img: np.ndarray, meta_dict: Dict, out_data_path: Path) -> np.ndarray:
    """
    infer lipid bodies and write inferred lipid to ome.tif file

    Parameters
    ------------
    in_img:
        a 3d  np.ndarray image of the inferred organelle (labels or boolean)
    meta_dict:
        dictionary of meta-data (ome)
    out_data_path:
        Path object where tiffs are written to

    Returns
    -------------
    exported file name

    """
    lipid = fixed_infer_LD(in_img)
    out_file_n = export_inferred_organelle(lipid, "LD", meta_dict, out_data_path)
    print(f"inferred lipid. wrote {out_file_n}")
    return lipid