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infer_subc/organelles/nuclei

fixed_infer_nuclei(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

nuclei_object inferred nuclei

Source code in infer_subc/organelles/nuclei.py 
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def fixed_infer_nuclei(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
    -------------
    nuclei_object
        inferred nuclei

    """
    nuc_ch = NUC_CH
    median_sz = 4
    gauss_sig = 1.34
    thresh_factor = 0.9
    thresh_min = 0.1
    thresh_max = 1.0
    max_hole_w = 25
    small_obj_w = 15

    return infer_nuclei_fromlabel(
        in_img, nuc_ch, median_sz, gauss_sig, thresh_factor, thresh_min, thresh_max, max_hole_w, small_obj_w
    )

get_nuclei(in_img, meta_dict, out_data_path)

load nucleus 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/nuclei.py 
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def get_nuclei(in_img: np.ndarray, meta_dict: Dict, out_data_path: Path) -> np.ndarray:
    """
    load nucleus 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:
        nuclei = import_inferred_organelle("nuc", meta_dict, out_data_path)
    except:
        start = time.time()
        print("starting segmentation...")
        nuclei = infer_and_export_nuclei(in_img, meta_dict, out_data_path)
        end = time.time()
        print(f"inferred nuclei in ({(end - start):0.2f}) sec")

    return nuclei

infer_and_export_nuclei(in_img, meta_dict, out_data_path)

infer nuclei and write inferred nuclei 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/nuclei.py 
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def infer_and_export_nuclei(in_img: np.ndarray, meta_dict: Dict, out_data_path: Path) -> np.ndarray:
    """
    infer nuclei and write inferred nuclei 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

    """
    nuclei = fixed_infer_nuclei(in_img)

    out_file_n = export_inferred_organelle(nuclei, "nuc", meta_dict, out_data_path)
    print(f"inferred nuclei. wrote {out_file_n}")
    return nuclei

infer_nuclei_fromlabel(in_img, nuc_ch, median_sz, gauss_sig, thresh_factor, thresh_min, thresh_max, max_hole_w, small_obj_w)

Procedure to infer nuclei from linearly unmixed input.

Parameters

in_img

a 3d image containing all the channels; np.ndarray

median_sz

width of median filter for signal

gauss_sig

sigma for gaussian smoothing of signal

thresh_factor

adjustment factor for log Li threholding

thresh_min

abs min threhold for log Li threholding

thresh_max

abs max threhold for log Li threholding

max_hole_w

hole filling cutoff for nuclei post-processing0

small_obj_w

minimu object size cutoff for nuclei post-processing

Returns

nuclei_object mask defined extent of NU

Source code in infer_subc/organelles/nuclei.py 
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def infer_nuclei_fromlabel(
    in_img: np.ndarray,
    nuc_ch: Union[int, None],
    median_sz: int,
    gauss_sig: float,
    thresh_factor: float,
    thresh_min: float,
    thresh_max: float,
    max_hole_w: int,
    small_obj_w: int,
) -> np.ndarray:
    """
    Procedure to infer nuclei from linearly unmixed input.

    Parameters
    ------------
    in_img:
        a 3d image containing all the channels; np.ndarray
    median_sz:
        width of median filter for signal
    gauss_sig:
        sigma for gaussian smoothing of  signal
    thresh_factor:
        adjustment factor for log Li threholding
    thresh_min:
        abs min threhold for log Li threholding
    thresh_max:
        abs max threhold for log Li threholding
    max_hole_w:
        hole filling cutoff for nuclei post-processing0
    small_obj_w:
        minimu object size cutoff for nuclei post-processing

    Returns
    -------------
    nuclei_object
        mask defined extent of NU

    """

    ###################
    # PRE_PROCESSING
    ###################
    if nuc_ch is None:
        nuc_ch = NUC_CH

    nuclei = select_channel_from_raw(in_img, nuc_ch)

    nuclei = scale_and_smooth(nuclei, median_sz=median_sz, gauss_sig=gauss_sig)

    ###################
    # CORE_PROCESSING
    ###################
    nuclei_object = apply_log_li_threshold(
        nuclei, thresh_factor=thresh_factor, thresh_min=thresh_min, thresh_max=thresh_max
    )

    ###################
    # POST_PROCESSING
    ###################
    nuclei_object = fill_and_filter_linear_size(nuclei_object, hole_min=0, hole_max=max_hole_w, min_size=small_obj_w)

    return label_uint16(nuclei_object)