Merge pull request #734 from DigitalSlideArchive/mtageld-dev

Multiple additions and improvements to preprocessing, saliency workflows, and documentation

Author: kheffah

docs/examples.rst

@@ -8,11 +8,14 @@ Examples
 
    examples/wsi-io-using-large-image.ipynb
    examples/color-deconvolution.ipynb
+   examples/color-normalization-and-augmentation.ipynb
    examples/nuclei-segmentation.ipynb
    examples/positive-pixel-count.ipynb
    examples/annotations_to_masks_handler.ipynb
    examples/masks_to_annotations_handler.ipynb
    examples/polygon_merger.ipynb
    examples/polygon_merger_v2.ipynb
    examples/tissue_detection.ipynb
-   examples/cellularity_detection.ipynb
+   examples/cellularity_detection_thresholding.ipynb
+   examples/cellularity_detection_superpixels.ipynb
+   examples/workflows.ipynb

docs/examples/cellularity_detection.ipynb renamed to docs/examples/cellularity_detection_superpixels.ipynb

@@ -4,7 +4,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Cellularity detection module\n",
+    "# Cellularity detection using superpixels\n",
     "\n",
     "**Overview:** \n",
     "Detect cellular regions in a slides by classifying superpixels. This uses Simple Linear Iterative Clustering (SLIC) to get superpixels at a low slide magnification to detect cellular regions. The first step of this pipeline detects tissue regions (i.e. individual tissue pieces) using the get_tissue_mask method of the histomicstk.saliency module. Then, each tissue piece is processed separately for accuracy and disk space efficiency. It is important to keep in mind that this does NOT rely on a tile iterator, but loads the entire tissue region (but NOT the whole slide) in memory and passes it on to skimage.segmentation.slic method. Not using a tile iterator helps keep the superpixel sizes large enough to correspond to tissue boundaries.\n",