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infer_subc/organelles/mitochondria

fixed_infer_mito(in_img)

Procedure to infer mitochondria from linearly unmixed input

Parameters

in_img

a 3d image containing all the channels

Returns

mito_object mask defined extent of mitochondria

Source code in infer_subc/organelles/mitochondria.py 
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def fixed_infer_mito(in_img: np.ndarray) -> np.ndarray:
    """
    Procedure to infer mitochondria from linearly unmixed input

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

    Returns
    -------------
    mito_object
        mask defined extent of mitochondria
    """
    median_sz = 3
    gauss_sig = 1.4
    vesselness_scale = 1.5
    vesselness_cut = 0.05
    small_obj_w = 3

    return infer_mito(in_img, median_sz, gauss_sig, vesselness_scale, vesselness_cut, small_obj_w)

get_mito(in_img, meta_dict, out_data_path)

load mitochondria 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/mitochondria.py 
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def get_mito(in_img: np.ndarray, meta_dict: Dict, out_data_path: Path) -> np.ndarray:
    """
    load mitochondria 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:
        mitochondria = import_inferred_organelle("mito", meta_dict, out_data_path)
    except:
        start = time.time()
        print("starting segmentation...")
        mitochondria = infer_and_export_mito(in_img, meta_dict, out_data_path)
        end = time.time()
        print(f"inferred (and exported) mitochondria in ({(end - start):0.2f}) sec")

    return mitochondria

infer_and_export_mito(in_img, meta_dict, out_data_path)

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

    """
    mitochondria = fixed_infer_mito(in_img)
    out_file_n = export_inferred_organelle(mitochondria, "mito", meta_dict, out_data_path)
    print(f"inferred mitochondria. wrote {out_file_n}")
    return mitochondria

infer_mito(in_img, median_sz, gauss_sig, vesselness_scale, vesselness_cut, small_obj_w)

Procedure to infer mitochondria 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

vesselness_scale

scale (log_sigma) for vesselness filter

vesselness_cut

threshold for vesselness fitered threshold

small_obj_w

minimu object size cutoff for nuclei post-processing

Returns

mito_object mask defined extent of mitochondria

Source code in infer_subc/organelles/mitochondria.py 
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def infer_mito(
    in_img: np.ndarray,
    median_sz: int,
    gauss_sig: float,
    vesselness_scale: float,
    vesselness_cut: float,
    small_obj_w: int,
) -> np.ndarray:
    """
    Procedure to infer mitochondria 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
    vesselness_scale:
        scale (log_sigma) for vesselness filter
    vesselness_cut:
        threshold for vesselness fitered threshold
    small_obj_w:
        minimu object size cutoff for nuclei post-processing

    Returns
    -------------
    mito_object
        mask defined extent of mitochondria
    """
    mito_ch = MITO_CH
    ###################
    # EXTRACT
    ###################
    mito = select_channel_from_raw(in_img, MITO_CH)

    ###################
    # PRE_PROCESSING
    ###################
    struct_img = scale_and_smooth(mito, median_sz=median_sz, gauss_sig=gauss_sig)

    ###################
    # CORE_PROCESSING
    ###################
    struct_img = vesselness_slice_by_slice(struct_img, sigma=vesselness_scale, cutoff=vesselness_cut, tau=0.75)

    ###################
    # POST_PROCESSING
    ###################
    struct_obj = size_filter_linear_size(struct_img, min_size=small_obj_w)

    return label_uint16(struct_obj)