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Merge pull request #94 from DigitalSlideArchive/nuclei_segmentation_cli
ENH: Add CLI to segment nuclei and generate annotations of nuclei bounding boxes
2 parents 4c501c8 + 40e9feb commit 77b15c4

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‎.travis.yml‎

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@@ -6,7 +6,10 @@ cache:
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directories:
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- $HOME/.cache
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sudo: false
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sudo: required
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services:
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- docker
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compiler:
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- gcc

‎Dockerfile‎

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@@ -21,6 +21,11 @@ RUN mkdir -p $build_path && \
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chmod +x $build_path/miniconda/bin/python
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ENV PATH=$build_path/miniconda/bin:${PATH}
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# git clone install ctk-cli
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RUN git clone https://github.com/cdeepakroy/ctk-cli.git && cd ctk-cli \
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git checkout 979d8cb671060e787b725b0226332a72a551592e && \
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python setup.py install
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# copy HistomicsTK files
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ENV htk_path=$PWD/HistomicsTK
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RUN mkdir -p $htk_path
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# Install HistomicsTK and its dependencies
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RUN conda config --add channels https://conda.binstar.org/cdeepakroy && \
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conda install --yes libgfortran==1.0 openslide-python \
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conda install --yes pip libgfortran==1.0 openslide-python \
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--file requirements.txt --file requirements_c_conda.txt && \
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pip install -r requirements_c.txt && \
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# Install HistomicsTK

‎histomicstk/MaxClustering.py‎

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@@ -19,7 +19,7 @@ def MaxClustering(Response, Mask, r=10):
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Mask : array_like
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A binary image where nuclei pixels have value 1/True, and non-nuclear
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pixels have value 0/False.
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f : float
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r : float
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A scalar defining the clustering radius. Default value = 10.
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Returns
@@ -61,6 +61,7 @@ def MaxClustering(Response, Mask, r=10):
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# pad input array to simplify filtering
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I = Response.min() * np.ones((Response.shape[0]+2*r,
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Response.shape[1]+2*r))
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Response = Response.copy()
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Response[~Mask] = Response.min()
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I[r:r+Response.shape[0], r:r+Response.shape[1]] = Response
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@@ -71,8 +72,8 @@ def MaxClustering(Response, Mask, r=10):
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# define pixels for local neighborhoods
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py, px = np.nonzero(Mask)
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py = py + r
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px = px + r
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py = py + np.int(r)
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px = px + np.int(r)
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# perform max filtering
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for i in np.arange(0, px.size, 1):

‎histomicstk/__init__.py‎

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# -*- coding: utf-8 -*-
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from .BinaryMixtureCut import BinaryMixtureCut
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# from .BinaryMixtureCut import BinaryMixtureCut
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from .ChanVese import ChanVese
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from .cLoG import cLoG
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from .ColorConvolution import ColorConvolution
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__version__ = '0.1.0'
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__all__ = ('BinaryMixtureCut',
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'ChanVese',
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# Add 'BinaryMixtureCut' after pygco is deployed
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__all__ = ('ChanVese',
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'cLoG',
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'ColorConvolution',
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'ColorDeconvolution',

‎requirements.txt‎

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#
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###############################################################
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lxml>=3.4.4
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ctk-cli>=1.3
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‎requirements_c_conda.txt‎

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#
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#########################################################
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# temporarily moving this here until 1.3.1 becomes available on pypi
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ctk-cli==1.3.1
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# large_image requires numpy==1.10.2
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numpy==1.10.2
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‎server/NucleiSegmentation/NucleiSegmentation.py‎

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from ctk_cli import CLIArgumentParser
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import histomicstk as htk
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import numpy as np
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import json
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import scipy as sp
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import skimage.io
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import skimage.measure
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import logging
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logging.basicConfig()
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stainColorMap = {
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'hematoxylin': [0.65, 0.70, 0.29],
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'eosin': [0.07, 0.99, 0.11],
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'dab': [0.27, 0.57, 0.78],
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'null': [0.0, 0.0, 0.0]
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}
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def main(args):
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#
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# Read Input Image
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#
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print('>> Reading input image')
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imInput = skimage.io.imread(args.inputImageFile)[:, :, :3]
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#
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# Perform color normalization
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#
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print('>> Performing color normalization')
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# transform input image to LAB color space
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imInputLAB = htk.RudermanLABFwd(imInput)
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# compute mean and stddev of input in LAB color space
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Mu = np.zeros(3)
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Sigma = np.zeros(3)
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for i in range(3):
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Mu[i] = imInputLAB[:, :, i].mean()
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Sigma[i] = (imInputLAB[:, :, i] - Mu[i]).std()
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# perform reinhard normalization
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imNmzd = htk.ReinhardNorm(imInput, Mu, Sigma)
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#
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# Perform color deconvolution
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#
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print('>> Performing color deconvolution')
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stainColor_1 = stainColorMap[args.stain_1]
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stainColor_2 = stainColorMap[args.stain_2]
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stainColor_3 = stainColorMap[args.stain_3]
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W = np.array([stainColor_1, stainColor_2, stainColor_3]).T
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imDeconvolved = htk.ColorDeconvolution(imNmzd, W)
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imNucleiStain = imDeconvolved.Stains[:, :, 0].astype(np.float)
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#
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# Perform nuclei segmentation
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#
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print('>> Performing nuclei segmentation')
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# segment foreground
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imFgndMask = sp.ndimage.morphology.binary_fill_holes(
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imNucleiStain < args.foreground_threshold)
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# run adaptive multi-scale LoG filter
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imLog = htk.cLoG(imNucleiStain, imFgndMask,
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SigmaMin=args.min_radius * np.sqrt(2),
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SigmaMax=args.max_radius * np.sqrt(2))
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imNucleiSegMask, Seeds, Max = htk.MaxClustering(
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imLog, imFgndMask, args.local_max_search_radius)
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# filter out small objects
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imNucleiSegMask = htk.FilterLabel(
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imNucleiSegMask, Lower=args.min_nucleus_area).astype(np.int)
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#
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# Generate annotations
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#
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objProps = skimage.measure.regionprops(imNucleiSegMask)
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print 'Number of nuclei = ', len(objProps)
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# create basic schema
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annotation = {
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"name": "Nuclei",
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"description": "Nuclei bounding boxes from a segmentation algorithm",
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"attributes": {
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"algorithm": {
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"color_normalization": "ReinhardNorm",
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"color_deconvolution": "ColorDeconvolution",
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"nuclei_segmentation": ["cLOG",
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"MaxClustering",
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"FilterLabel"]
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}
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},
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"elements": []
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}
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# add each nucleus as an element into the annotation schema
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for i in range(len(objProps)):
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c = [objProps[i].centroid[1], objProps[i].centroid[0], 0]
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width = objProps[i].bbox[3] - objProps[i].bbox[1] + 1
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height = objProps[i].bbox[2] - objProps[i].bbox[0] + 1
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cur_bbox = {
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"type": "rectangle",
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"center": c,
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"width": width,
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"height": height,
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}
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annotation["elements"].append(cur_bbox)
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#
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# Save output segmentation mask
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#
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print('>> Outputting nuclei segmentation mask')
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skimage.io.imsave(args.outputNucleiMaskFile, imNucleiSegMask)
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#
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# Save output annotation
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#
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print('>> Outputting nuclei annotation')
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with open(args.outputNucleiAnnotationFile, 'w') as annotationFile:
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json.dump(annotation, annotationFile, indent=2, sort_keys=False)
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if __name__ == "__main__":
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main(CLIArgumentParser().parse_args())

‎server/NucleiSegmentation/NucleiSegmentation.xml‎

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<?xml version="1.0" encoding="UTF-8"?>
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<executable>
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<category>HistomicsTK</category>
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<title>Segments Nuclei</title>
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<description>Segments nuclei in a whole-slide image</description>
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<version>0.1.0</version>
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<documentation-url>https://histomicstk.readthedocs.org/en/latest/</documentation-url>
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<license>Apache 2.0</license>
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<contributor>Deepak Roy Chittajallu (Kitware)</contributor>
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<acknowledgements>This work is part of the HistomicsTK project.</acknowledgements>
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<parameters>
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<label>IO</label>
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<description>Input/output parameters</description>
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<image>
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<name>inputImageFile</name>
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<label>Input Image</label>
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<description>Input image to be deconvolved</description>
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<channel>input</channel>
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<index>0</index>
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</image>
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<image>
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<name>outputNucleiMaskFile</name>
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<label>Output Nuclei Segmentation Mask</label>
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<description>Output nuclei segmentation label mask</description>
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<channel>output</channel>
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<index>1</index>
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</image>
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<file fileExtensions=".anot" reference="inputImageFile">
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<name>outputNucleiAnnotationFile</name>
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<label>Output Nuclei Annotation File</label>
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<description>Output nuclei annotation file</description>
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<channel>output</channel>
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<index>2</index>
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</file>
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</parameters>
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<parameters>
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<label>Color Deconvolution</label>
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<description>Color Deconvolution parameters</description>
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<string-enumeration>
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<name>stain_1</name>
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<label>stain-1</label>
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<description>Name of stain-1</description>
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<channel>input</channel>
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<longflag>stain_1</longflag>
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<element>hematoxylin</element>
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<element>eosin</element>
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<element>dab</element>
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<default>hematoxylin</default>
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</string-enumeration>
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<string-enumeration>
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<name>stain_2</name>
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<label>stain-2</label>
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<description>Name of stain-2</description>
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<channel>input</channel>
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<longflag>stain_2</longflag>
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<element>hematoxylin</element>
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<element>eosin</element>
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<element>dab</element>
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<default>eosin</default>
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</string-enumeration>
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<string-enumeration>
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<name>stain_3</name>
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<label>stain-3</label>
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<description>Name of stain-3</description>
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<channel>input</channel>
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<longflag>stain_3</longflag>
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<element>hematoxylin</element>
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<element>eosin</element>
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<element>dab</element>
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<element>null</element>
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<default>null</default>
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</string-enumeration>
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</parameters>
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<parameters>
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<label>Nuclei segmentation</label>
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<description>Nuclei segmentation parameters</description>
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<double>
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<name>foreground_threshold</name>
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<label>Foreground Intensity Threshold</label>
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<description>Intensity value to use as threshold to segment foreground in nuclear stain image</description>
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<longflag>foreground_threshold</longflag>
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<default>160</default>
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</double>
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<double>
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<name>min_radius</name>
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<label>Minimum Radius</label>
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<description>Minimum nuclear radius (used to set min sigma of the multiscale LoG filter)</description>
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<longflag>min_radius</longflag>
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<default>4</default>
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</double>
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<double>
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<name>max_radius</name>
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<label>Maximum Radius</label>
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<description>Maximum nuclear radius (used to set max sigma of the multiscale LoG filter)</description>
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<longflag>max_radius</longflag>
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<default>7</default>
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</double>
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<double>
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<name>local_max_search_radius</name>
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<label>Local Max Search Radius</label>
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<description>Local max search radius used for detection seed points in nuclei</description>
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<longflag>local_max_search_radius</longflag>
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<default>10</default>
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</double>
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<double>
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<name>min_nucleus_area</name>
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<label>Minimum Nucleus Area</label>
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<description>Minimum area that each nucleus should have</description>
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<longflag>min_nucleus_area</longflag>
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<default>80</default>
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</double>
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</parameters>
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</executable>

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