Create seg_PCNA workflows (#79)

* Create seg_PCNA workflows

Two versions of PCNA segmentation workflows which includes OTSU masking of apoptotic debris.  PCNA_lateS has fewest G1/late S/G2 errors.  PCNA_earlyS_midS detects all dim early S / mid S phase spots but picks up bright G2 pixels.

* update pcna workflow function names

* Updated PCNA lateS workflow with hole filling

3D dot filter method creates holes if spot is too large.  This workflow update addresses that issue.

* Removed vesselness filter step

* Added scipy import

---------

Co-authored-by: Benjamin Morris <benjijamorris@gmail.com>

aicssegmentation/structure_wrapper/seg_PCNA_earlyS_midS.py

@@ -0,0 +1,138 @@
+import numpy as np
+from typing import Union
+from pathlib import Path
+
+from aicssegmentation.core.vessel import vesselness3D
+from aicssegmentation.core.seg_dot import dot_3d
+from aicssegmentation.core.pre_processing_utils import (
+    intensity_normalization,
+    edge_preserving_smoothing_3d,
+)
+from skimage.morphology import remove_small_objects
+from aicssegmentation.core.output_utils import (
+    save_segmentation,
+    generate_segmentation_contour,
+)
+
+from skimage.filters import threshold_otsu
+
+
+def Workflow_PCNA_earlyS_midS(
+    struct_img: np.ndarray,
+    rescale_ratio: float = -1,
+    output_type: str = "default",
+    output_path: Union[str, Path] = None,
+    fn: Union[str, Path] = None,
+    output_func=None,
+):
+    """
+    classic segmentation workflow wrapper for structure pcna
+
+    Parameter:
+    -----------
+    struct_img: np.ndarray
+        the 3D image to be segmented
+    rescale_ratio: float
+        an optional parameter to allow rescale the image before running the
+        segmentation functions, default is no rescaling
+    output_type: str
+        select how to handle output. Currently, four types are supported:
+        1. default: the result will be saved at output_path whose filename is
+            original name without extention + "_struct_segmentaiton.tiff"
+        2. array: the segmentation result will be simply returned as a numpy array
+        3. array_with_contour: segmentation result will be returned together with
+            the contour of the segmentation
+        4. customize: pass in an extra output_func to do a special save. All the
+            intermediate results, names of these results, the output_path, and the
+            original filename (without extension) will be passed in to output_func.
+    """
+    ##########################################################################
+    # PARAMETERS:
+    #   note that these parameters are supposed to be fixed for the structure
+    #   and work well accross different datasets
+    ##########################################################################
+
+    intensity_norm_param = [0.5, 10]
+    intensity_norm_param_otsu = [0.5, 15]
+    scaling_factor = 1.0
+    vesselness_sigma = [1.0]
+    vesselness_cutoff = 0.0125
+    minArea = 20
+    dot_3d_sigma = 1.0
+    ##########################################################################
+
+    out_img_list = []
+    out_name_list = []
+
+    ###################
+    # PRE_PROCESSING
+    ###################
+    # intenisty normalization (min/max)
+    struct_img = intensity_normalization(struct_img, scaling_param=intensity_norm_param)
+    struct_img_otsu = intensity_normalization(struct_img, scaling_param=intensity_norm_param_otsu)
+
+    out_img_list.append(struct_img.copy())
+    out_name_list.append("im_norm")
+
+    # smoothing with boundary preserving smoothing
+    structure_img_smooth = edge_preserving_smoothing_3d(struct_img)
+    structure_img_smooth_otsu = edge_preserving_smoothing_3d(struct_img_otsu)
+
+    out_img_list.append(structure_img_smooth.copy())
+    out_name_list.append("im_smooth")
+    out_img_list.append(structure_img_smooth_otsu.copy())
+    out_name_list.append("im_smooth_otsu")
+
+    ###################
+    # core algorithm
+    ###################
+    otsu_thresh = threshold_otsu(structure_img_smooth_otsu)
+    global_thresh = otsu_thresh * scaling_factor
+    bw_otsu_mask = structure_img_smooth_otsu > global_thresh
+
+    response_f3 = vesselness3D(structure_img_smooth, sigmas=vesselness_sigma, tau=1, whiteonblack=True)
+    response_f3 = response_f3 > vesselness_cutoff
+
+    response_s3_1 = dot_3d(structure_img_smooth, log_sigma=dot_3d_sigma)
+    response_s3_3 = dot_3d(structure_img_smooth, log_sigma=3)
+
+    bw_small_inverse = remove_small_objects(response_s3_1 > 0.035, min_size=150)
+    bw_small = np.logical_xor(bw_small_inverse, response_s3_1 > 0.025)
+    bw_medium = np.logical_or(bw_small, response_s3_1 > 0.07)
+    bw_large = np.logical_or(response_s3_3 > 0.2, response_f3 > 0.25)
+    bw_dots = np.logical_or(np.logical_or(bw_small, bw_medium), bw_large)
+    bw = np.logical_and(bw_dots, bw_otsu_mask)
+
+    ###################
+    # POST-PROCESSING
+    ###################
+    bw = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
+    for zz in range(bw.shape[0]):
+        bw[zz, :, :] = remove_small_objects(bw[zz, :, :], min_size=3, connectivity=1, in_place=False)
+
+    seg = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
+
+    from aicssegmentation.core.utils import remove_hot_pixel
+
+    seg = seg > 0
+    seg_clean = remove_hot_pixel(seg)
+    seg = seg_clean.astype(np.uint8)
+    seg[seg > 0] = 255
+
+    out_img_list.append(seg.copy())
+    out_name_list.append("bw_final")
+
+    if output_type == "default":
+        # the default final output, simply save it to the output path
+        save_segmentation(seg, False, Path(output_path), fn)
+    elif output_type == "customize":
+        # the hook for passing in a customized output function
+        # use "out_img_list" and "out_name_list" in your hook to
+        # customize your output functions
+        output_func(out_img_list, out_name_list, Path(output_path), fn)
+    elif output_type == "array":
+        return seg
+    elif output_type == "array_with_contour":
+        return (seg, generate_segmentation_contour(seg))
+    else:
+        raise NotImplementedError("invalid output type: {output_type}")

aicssegmentation/structure_wrapper/seg_PCNA_lateS.py

@@ -0,0 +1,154 @@
+import numpy as np
+from typing import Union
+from pathlib import Path
+
+from aicssegmentation.core.vessel import vesselness3D
+from aicssegmentation.core.seg_dot import dot_3d
+from aicssegmentation.core.pre_processing_utils import (
+    intensity_normalization,
+    edge_preserving_smoothing_3d,
+)
+from skimage.morphology import remove_small_objects
+from aicssegmentation.core.output_utils import (
+    save_segmentation,
+    generate_segmentation_contour,
+)
+
+from skimage.filters import threshold_otsu
+
+
+def Workflow_PCNA_lateS(
+    struct_img: np.ndarray,
+    rescale_ratio: float = -1,
+    output_type: str = "default",
+    output_path: Union[str, Path] = None,
+    fn: Union[str, Path] = None,
+    output_func=None,
+):
+    """
+    classic segmentation workflow wrapper for structure pcna
+
+    Parameter:
+    -----------
+    struct_img: np.ndarray
+        the 3D image to be segmented
+    rescale_ratio: float
+        an optional parameter to allow rescale the image before running the
+        segmentation functions, default is no rescaling
+    output_type: str
+        select how to handle output. Currently, four types are supported:
+        1. default: the result will be saved at output_path whose filename is
+            original name without extention + "_struct_segmentaiton.tiff"
+        2. array: the segmentation result will be simply returned as a numpy array
+        3. array_with_contour: segmentation result will be returned together with
+            the contour of the segmentation
+        4. customize: pass in an extra output_func to do a special save. All the
+            intermediate results, names of these results, the output_path, and the
+            original filename (without extension) will be passed in to output_func.
+    """
+    ##########################################################################
+    # PARAMETERS:
+    #   note that these parameters are supposed to be fixed for the structure
+    #   and work well accross different datasets
+    ##########################################################################
+
+    intensity_norm_param = [0.5, 15]
+    intensity_norm_param_otsu = [0.5, 15]
+    scaling_factor = 1.0
+    vesselness_sigma = [1.0]
+    vesselness_cutoff = 0.15
+    minArea = 30
+    dot_3d_sigma = 1.0
+    otsumask_min_area = 50
+    ##########################################################################
+
+    out_img_list = []
+    out_name_list = []
+
+    ###################
+    # PRE_PROCESSING
+    ###################
+    # intenisty normalization (min/max)
+    struct_img = intensity_normalization(struct_img, scaling_param=intensity_norm_param)
+    struct_img_otsu = intensity_normalization(struct_img, scaling_param=intensity_norm_param_otsu)
+
+    out_img_list.append(struct_img.copy())
+    out_name_list.append("im_norm")
+
+    # smoothing with boundary preserving smoothing
+    structure_img_smooth = edge_preserving_smoothing_3d(struct_img)
+    structure_img_smooth_otsu = edge_preserving_smoothing_3d(struct_img_otsu)
+
+
+    out_img_list.append(structure_img_smooth.copy())
+    out_name_list.append("im_smooth")
+    out_img_list.append(structure_img_smooth_otsu.copy())
+    out_name_list.append("im_smooth_otsu")
+
+
+    ###################
+    # core algorithm
+    ###################
+    otsu_thresh = threshold_otsu(structure_img_smooth_otsu)
+    global_thresh = otsu_thresh * scaling_factor
+    bw_otsu_mask = structure_img_smooth_otsu > global_thresh
+    bw_otsu_mask_removesmallobjects = remove_small_objects(
+        bw_otsu_mask, min_size=otsumask_min_area, connectivity=1, in_place=False
+    )
+
+    response_f3 = vesselness3D(structure_img_smooth, sigmas=vesselness_sigma, tau=1, whiteonblack=True)
+    response_f3 = response_f3 > vesselness_cutoff
+
+    response_s3_1 = dot_3d(structure_img_smooth, log_sigma=dot_3d_sigma)
+    response_s3_3 = dot_3d(structure_img_smooth, log_sigma=3)
+
+    #bw_small_inverse = remove_small_objects(response_s3_1 > 0.035, min_size=150)
+    #bw_small = np.logical_xor(bw_small_inverse, response_s3_1 > 0.025)
+    #bw_small = response_s3_1 > 0.12
+    bw_medium = response_s3_1 > 0.05
+    #bw_large =  response_s3_3 > 0.45
+    #bw_large = np.logical_or(response_s3_3 > 0.45, response_f3 > 0.5)
+    #bw_dots = np.logical_or(bw_medium, bw_large)
+    #bw = np.logical_and(bw_dots, bw_otsu_mask_removesmallobjects)
+    bw = np.logical_and(bw_medium, bw_otsu_mask_removesmallobjects)
+
+    out_img_list.append(bw_otsu_mask.copy())
+    out_name_list.append("bw_otsu_mask")
+    out_img_list.append(bw_otsu_mask_removesmallobjects.copy())
+    out_name_list.append("bw_otsu_mask_removesmallobjects")
+    out_img_list.append(bw.copy())
+    out_name_list.append("bw")
+
+    ###################
+    # POST-PROCESSING
+    ###################
+    bw = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
+    for zz in range(bw.shape[0]):
+        bw[zz, :, :] = remove_small_objects(bw[zz, :, :], min_size=3, connectivity=1, in_place=False)
+
+    seg = remove_small_objects(bw > 0, min_size=minArea, connectivity=1, in_place=False)
+
+    from aicssegmentation.core.utils import remove_hot_pixel
+
+    seg = seg > 0
+    seg_clean = remove_hot_pixel(seg)
+    seg = seg_clean.astype(np.uint8)
+    seg[seg > 0] = 255
+
+    out_img_list.append(seg.copy())
+    out_name_list.append("bw_final")
+
+    if output_type == "default":
+        # the default final output, simply save it to the output path
+        save_segmentation(seg, False, Path(output_path), fn)
+    elif output_type == "customize":
+        # the hook for passing in a customized output function
+        # use "out_img_list" and "out_name_list" in your hook to
+        # customize your output functions
+        output_func(out_img_list, out_name_list, Path(output_path), fn)
+    elif output_type == "array":
+        return seg
+    elif output_type == "array_with_contour":
+        return (seg, generate_segmentation_contour(seg))
+    else:
+        raise NotImplementedError("invalid output type: {output_type}")