get num tissues

histoqc/BaseImage.py

@@ -8,7 +8,7 @@
 from typing import Union, Tuple
 #os.environ['PATH'] = 'C:\\research\\openslide\\bin' + ';' + os.environ['PATH'] #can either specify openslide bin path in PATH, or add it dynamically
 from histoqc.import_wrapper.openslide import openslide
-
+from skimage import measure, morphology
 # there is no branch reset group in re
 # compatible with the previous definition of valid input: leading zero and leading decimals are supported
 _REGEX_MAG = r"^(\d?\.?\d*X?)"
@@ -206,6 +206,9 @@ def getImgThumb(self, size: str):
  self[key] = getBestThumb(self, bx, by, target_dims, target_sampling_factor)
  return self[key]
 
+'''
+the following are helper functions 
+'''
 def getBestThumb(s: BaseImage, x: int, y: int, dims: Tuple[int, int], target_sampling_factor: float):
  osh = s["os_handle"]
 
@@ -224,9 +227,7 @@ def getBestThumb(s: BaseImage, x: int, y: int, dims: Tuple[int, int], target_sam
  else:
  return resizeTileDownward(s, target_sampling_factor, level)
 
-'''
-the followings are helper functions 
-'''
+
 def resizeTileDownward(self, target_downsampling_factor, level):
  osh = self["os_handle"]
  (bx, by, bwidth, bheight) = self["img_bbox"]
@@ -341,3 +342,22 @@ def getDimensionsByOneDim(s: BaseImage, dim: int) -> Tuple[int, int]:
  else:
  w = int(dim * width / height)
  return w, dim
+
+
+def getMaskRegionsStats(img)-> dict:
+
+ rps = measure.regionprops(morphology.label(img))
+ if rps:
+ areas = np.asarray([rp.area for rp in rps])
+ num = len(rps)
+ area_min = areas.min()
+ area_max = areas.max()
+ area_mean = areas.mean()
+ else:
+ num = area_min = area_max = area_mean = 0
+ return {
+ 'num': num,
+ 'area_min': area_min,
+ 'area_max': area_max,
+ 'area_mean': area_mean
+ }

histoqc/BasicModule.py

@@ -1,6 +1,6 @@
 import logging
 import os
-from histoqc.BaseImage import printMaskHelper
+from histoqc.BaseImage import printMaskHelper, getMaskRegionsStats
 from skimage.morphology import remove_small_objects, binary_opening, disk
 from skimage import io, color, img_as_ubyte
 
@@ -69,3 +69,9 @@ def finalProcessingArea(s, params):
  f"{s['filename']} - After BasicModule.finalProcessingArea NO tissue remains detectable! Downstream modules likely to be incorrect/fail")
  s["warnings"].append(
  f"After BasicModule.finalProcessingArea NO tissue remains detectable! Downstream modules likely to be incorrect/fail")
+
+
+def countTissuePieces(s):
+ mask = s["img_mask_use"]
+ stats = getMaskRegionsStats(mask)
+ s.addToPrintList("#pieces_of_tissue", str(stats.get('num', 0)))

histoqc/BlurDetectionModule.py

@@ -2,7 +2,7 @@
 import os
 
 import skimage
-from histoqc.BaseImage import printMaskHelper
+from histoqc.BaseImage import printMaskHelper, getMaskRegionsStats
 from skimage import io, img_as_ubyte, morphology, measure
 from skimage.color import rgb2gray
 from skimage.filters import rank
@@ -37,19 +37,20 @@ def identifyBlurryRegions(s, params):
  prev_mask = s["img_mask_use"]
  s["img_mask_use"] = s["img_mask_use"] & ~s["img_mask_blurry"]
 
- rps = measure.regionprops(morphology.label(mask))
- if rps:
- areas = np.asarray([rp.area for rp in rps])
- nobj = len(rps)
- area_max = areas.max()
- area_mean = areas.mean()
- else:
- nobj = area_max = area_mean = 0
-
-
- s.addToPrintList("blurry_removed_num_regions", str(nobj))
- s.addToPrintList("blurry_removed_mean_area", str(area_mean))
- s.addToPrintList("blurry_removed_max_area", str(area_max))
+ # rps = measure.regionprops(morphology.label(mask))
+ # if rps:
+ # areas = np.asarray([rp.area for rp in rps])
+ # nobj = len(rps)
+ # area_max = areas.max()
+ # area_mean = areas.mean()
+ # else:
+ # nobj = area_max = area_mean = 0
+ stat_rs = getMaskRegionsStats(mask)
+
+
+ s.addToPrintList("blurry_removed_num_regions", str(stat_rs.get('num', 0)))
+ s.addToPrintList("blurry_removed_mean_area", str(stat_rs.get('area_mean',0)))
+ s.addToPrintList("blurry_removed_max_area", str(stat_rs.get('area_max',0)))
 
 
  s.addToPrintList("blurry_removed_percent",

histoqc/MorphologyModule.py

@@ -1,7 +1,7 @@
 import logging
 import os
 import numpy as np
-from histoqc.BaseImage import printMaskHelper
+from histoqc.BaseImage import printMaskHelper, getMaskRegionsStats
 from skimage import io, morphology, img_as_ubyte, measure
 
 from scipy import ndimage as ndi
@@ -23,19 +23,23 @@ def removeSmallObjects(s, params):
  s["img_mask_use"] = img_reduced
 
 
- rps = measure.regionprops(morphology.label(img_small))
- if rps:
- areas = np.asarray([rp.area for rp in rps])
- nobj = len(rps)
- area_max = areas.max()
- area_mean = areas.mean()
- else:
- nobj = area_max = area_mean = 0
+ # rps = measure.regionprops(morphology.label(img_small))
+ # if rps:
+ # areas = np.asarray([rp.area for rp in rps])
+ # nobj = len(rps)
+ # area_max = areas.max()
+ # area_mean = areas.mean()
+ # else:
+ # nobj = area_max = area_mean = 0
+ stats = getMaskRegionsStats(img_small)
 
- s.addToPrintList("small_tissue_removed_num_regions", str(nobj))
- s.addToPrintList("small_tissue_removed_mean_area", str(area_mean))
- s.addToPrintList("small_tissue_removed_max_area", str(area_max))
 
+ # s.addToPrintList("small_tissue_removed_num_regions", str(nobj))
+ # s.addToPrintList("small_tissue_removed_mean_area", str(area_mean))
+ # s.addToPrintList("small_tissue_removed_max_area", str(area_max))
+ s.addToPrintList("small_tissue_removed_num_regions", str(stats.get('num',0)))
+ s.addToPrintList("small_tissue_removed_mean_area", str(stats.get('area_mean',0)))
+ s.addToPrintList("small_tissue_removed_max_area", str(stats.get('area_max',0)))
 
 
 
@@ -87,18 +91,22 @@ def removeFatlikeTissue(s, params):
  prev_mask = s["img_mask_use"]
  s["img_mask_use"] = prev_mask & ~mask_fat
 
- rps = measure.regionprops(morphology.label(mask_fat))
- if rps:
- areas = np.asarray([rp.area for rp in rps])
- nobj = len(rps)
- area_max = areas.max()
- area_mean = areas.mean()
- else:
- nobj = area_max = area_mean = 0
+ # rps = measure.regionprops(morphology.label(mask_fat))
+ # if rps:
+ # areas = np.asarray([rp.area for rp in rps])
+ # nobj = len(rps)
+ # area_max = areas.max()
+ # area_mean = areas.mean()
+ # else:
+ # nobj = area_max = area_mean = 0
+ stats = getMaskRegionsStats(mask_fat)
 
- s.addToPrintList("fatlike_tissue_removed_num_regions", str(nobj))
- s.addToPrintList("fatlike_tissue_removed_mean_area", str(area_mean))
- s.addToPrintList("fatlike_tissue_removed_max_area", str(area_max))
+ # s.addToPrintList("fatlike_tissue_removed_num_regions", str(nobj))
+ # s.addToPrintList("fatlike_tissue_removed_mean_area", str(area_mean))
+ # s.addToPrintList("fatlike_tissue_removed_max_area", str(area_max))
+ s.addToPrintList("fatlike_tissue_removed_num_regions", str(stats.get('num',0)))
+ s.addToPrintList("fatlike_tissue_removed_mean_area", str(stats.get('area_mean',0)))
+ s.addToPrintList("fatlike_tissue_removed_max_area", str(stats.get('area_max',0)))
 
 
 
@@ -125,18 +133,22 @@ def fillSmallHoles(s, params):
  prev_mask = s["img_mask_use"]
  s["img_mask_use"] = img_reduced
 
- rps = measure.regionprops(morphology.label(img_small))
- if rps:
- areas = np.asarray([rp.area for rp in rps])
- nobj = len(rps)
- area_max = areas.max()
- area_mean = areas.mean()
- else:
- nobj = area_max = area_mean = 0
-
- s.addToPrintList("small_tissue_filled_num_regions", str(nobj))
- s.addToPrintList("small_tissue_filled_mean_area", str(area_mean))
- s.addToPrintList("small_tissue_filled_max_area", str(area_max))
+ # rps = measure.regionprops(morphology.label(img_small))
+ # if rps:
+ # areas = np.asarray([rp.area for rp in rps])
+ # nobj = len(rps)
+ # area_max = areas.max()
+ # area_mean = areas.mean()
+ # else:
+ # nobj = area_max = area_mean = 0
+ stats = getMaskRegionsStats(img_small)
+
+ # s.addToPrintList("small_tissue_filled_num_regions", str(nobj))
+ # s.addToPrintList("small_tissue_filled_mean_area", str(area_mean))
+ # s.addToPrintList("small_tissue_filled_max_area", str(area_max))
+ s.addToPrintList("small_tissue_filled_num_regions", str(stats.get('num', 0)))
+ s.addToPrintList("small_tissue_filled_mean_area", str(stats.get('area_mean', 0)))
+ s.addToPrintList("small_tissue_filled_max_area", str(stats.get('area_max', 0)))
 
  s.addToPrintList("small_tissue_filled_percent",
  printMaskHelper(params.get("mask_statistics", s["mask_statistics"]), prev_mask, s["img_mask_use"]))

histoqc/config/config_v2.1.ini

@@ -27,8 +27,9 @@ steps= BasicModule.getBasicStats
  SaveModule.saveFinalMask
  SaveModule.saveMacro
  SaveModule.saveThumbnails
+ BasicModule.countTissuePieces
  BasicModule.finalComputations
-
+ 
 
 [BaseImage.BaseImage]
 image_work_size = 1.25x
@@ -252,3 +253,5 @@ mask_name: img_mask_use
 disk_size: 5
 upper_threshold: 210
 invert: True
+
+[BasicModule.countTissuePieces]