KNIME For Open-Source BioImage Analysis - A Tutorial

6Christian Dietz and Michael R. BertholdA workflow usually starts with a node, which represents a data source, e.g.connecting a database, reading a text file, or reading images. The data are transported between the connected nodes, typically organized in data tables, consistingof columns of certain (extensible) data-types and an arbitrary number of rows. Atypical data table is depicted in Figure 2, with each column of the table comprisingan arbitrary object type, e.g. numbers, text or molecules. KNIME Image Processingadds two new column types to the mix: images and labelings. Labelings representthe segmentation of an image - the partitioning of an image into segments. As opposed to images, labelings store one or more labels for each pixel, instead of numericvalues. A label associates each pixel with an object, class value, track number or anyother information.Contrary to what might be assumed initially, images and labelings stored in asingle cell of a data table can be of arbitrary dimensionality. For example, a table cellmay contain a multi-channel video or z-stack. To accomplish n-dimensional imageprocessing, KNIME uses ImgLib2 as its underlying programming framework.2.4 Image Processing Specific Dialog Components2.4.1 Dimension SelectionFig. 3 The configuration dialog of the Image Normalizer node.In order to provide the user with the flexibility to choose how images and labelings with more than just two dimensions are to be processed, most of the nodesprovided in the KNIME Image Processing Extension offer a so-called Dimension

KNIME for Open-Source BioImage Analysis - A Tutorial7Selection dialog (see Figure 3). This dialog enables users to select the dimensionson which an algorithm will operate. For instance in the case of a simple Z-Stack, theImage Normalization node can be configured so as to apply normalization to eachX,Y plane either independently or for the entire X,Y,Z cube by selecting X,Y or X,Y,Zin the dimension selection.2.4.2 Column SelectionMany KNIME Image Processing nodes, whose input is an image or labeling, operateon a row-to-row basis. This means that - given an input image - another image orlabeling is calculated based on the algorithm implemented in the node. The user candetermine the layout of the output table of these nodes with a dialog componentcalled Column Selection. Generally, a user has three options: the resulting columnwith images or labelings can either be appended to the incoming table, replace theexisting column or an entirely new table can be created.2.5 Visualization of Images and LabelingsFig. 4 The KNIME Image Processing Image Viewer allows users to inspect the images in moredetail. Users can browse through the various dimensions of an image, inspect the values of thepixels and obtain information about important meta-data.

8Christian Dietz and Michael R. BertholdKNIME Image Processing enables users to explore images and labelings in moredetail, which is especially useful if an image or labeling comprises more than twodimensions (e.g. z-stacks or videos). The user can access this view by Right Click Open Image Viewer (see Figure 4) on a KNIME Image Processing node. Another,more specific view is the Interactive Segmentation View node. It can be used tovalidate segmentation, classification or tracking results as it offers an overlay viewfor images and labelings. Additional visualization plugins can be installed to extendKNIME Image Processing. For instance the ClearVolume Integration offers fast,GPU accelerated 5D volume rendering and can easily be used within KNIME.3 Step by Step to Phenotype ClassificationIn this section we walk step by step through an image processing workflow. Theworkflow classifies cells as either positive or negative according to their phenotype(see Figure 5)7 .Fig. 5 Two images from the publicly available high-content screening image data provided in(Ljosa et al, 2012). The left image contains positive cells and the right, negative cells.The cells in this example stem from images from the publicly available highcontent screening image data provided in (Ljosa et al, 2012) (human cytoplasmnucleus translocation assay, available from the Broad Bioimage Benchmark Collection). The images were taken from stably transfected osteosarcoma cells seeded ina 96 well plate and contain the information about the translocalization of the Forkhead (FKHR-EGFP) fusion protein from the cytoplasm (Channel 2) to the nucleus(Channel 1)8 .7The entire Phenotype Classification workflow is available for download athttp://knime.imagej.net/aaec.8 For detailed information see http://www.broadinstitute.org/bbbc/BBBC013/. Please note: TheBMP images available on the website are already splitted into the individual channels.

KNIME for Open-Source BioImage Analysis - A Tutorial9The example workflow is depicted in Figure 6. The individual parts of the workflow are organized in so-called meta nodes to reduce the complexity of the workflow.Meta nodes are nodes that contain subworkflows, i.e. in the workflow they look likea single node and yet they can contain many nodes and even more meta nodes. Thisprovides a series of advantages such as enabling the user to design much larger,more complex workflows and the encapsulation of specific actions.Fig. 6 The workflow discussed in this tutorial.A Double-Click on the meta node allows the user to have a closer look at whatis inside. In the following, the content of each meta node will be explained in moredetail. However, it is important to note that the individual parts of the workflow areeasily replaced by other nodes possibly more suitable for other image processingtasks. Besides KNIME Image Processing, integrations with for example R, Python,Weka and especially KNIME itself offer a wide range of functionality for morecomplex visualizations and advanced machine learning and data mining techniques.3.1 Loading ImagesFig. 7 Detailed look inside the Loading Images meta node.To date, the proprietary file formats of microscope image analysis software havemade it difficult for open-source platforms to load images generically. However,SCIFIO with its integration of the BioFormats (Linkert et al, 2010) library, canconvert approx. 125 file formats used by various microscope manufacturers, such as

10Christian Dietz and Michael R. BertholdZeiss LSM, Metamorph Stack, Leica LCS or DICOM, into a KNIME compatibleformat. In KNIME, this functionality can be accessed via the Image Reader node,which integrates these libraries. A user can either select the images in the ImageReader configuration dialog (Right Click Configure) or provide URLs to theimages as an input table coming from another node, e.g. the List Files node. Theresulting workflow of the latter approach is illustrated in Figure 7.The List Files node is used to list the URLs of all images of a certain folder. Connecting the node to the Image Reader node enables the user to configure the ImageReader node (Right-Click on Image Reader Configure), such that it loads allimages into KNIME from these URLs. In this configuration: Tab: Additional Options File name column in optional column has to be set to the column of theincoming table that contains the URLs to the requested images.3.2 Preprocessing ImagesFig. 8 Detailed look into the Preprocessing meta node. The images are split into Nuclei and Cytoplasm channels and renamed accordingly.KNIME Image Processing offers a range of general (pre-)processing techniquesto enhance image quality: Standard linear and non-linear filters are available as aremorphological and binary operations, pixel-wise image arithmetics, edge-detectors,background subtraction algorithms, projections or the nodes for the manipulation ofthe dimensionality, such as splitting and merging images. Additionally, the ImageJMacro node, which is part of the KNIME Image Processing - ImageJ Integration9 ,allows the execution of arbitrary ImageJ1 macros on a huge amount of image data.The image preprocessing used in this tutorial is implemented in the Preprocessing meta node (see Figure 6). First of all, the channels of the images are split, whichresults in a table with two columns, the Nuclei and Cytoplasm (see Figure 8). Next,each channel is preprocessed individually. The images in the Nuclei column suffer9For details and installation instructions see https://tech.knime.org/community/imagej.

KNIME for Open-Source BioImage Analysis - A Tutorial11Fig. 9 Detailed look into the Background Subtraction of Images in Nuclei Channel meta node.from non-uniform illumination, which makes it difficult to apply automated segmentation methods. Therefore in the Background Subtraction of Images in NucleiChannel meta node the quality of the images in the Nuclei column is enhanced.Here, a very simple background subtraction technique was chosen, especially todemonstrate how individual KNIME nodes are easily combined to create an existing or completely new processing or analysis technique without any programming.Figure 9 depicts the workflow implementing the algorithm: The images in the Nuclei column are filtered with a very large kernel (sigma 100.0) using the GaussianConvolution node and the output is appended as an additional column to the inputtable. The mean value of the pixel intensities is calculated for each of the filteredimages using the Image Feature node.Fig. 10 Configuration dialog of the Image Calculator node.

12Christian Dietz and Michael R. BertholdFinally, the resulting background corrected images are obtained by subtractingthe sum of the filtered image and the mean of the filtered image at each pixel positionfrom the original image using the Image Calculator node.The configuration of the Image Calculator node is shown in Figure 10. The lastnode in the Background Subtraction of Images in Nuclei Channel meta node is theImage Converter. This node can be used to normalize and scale the intensity valuesof the images to a certain range. In this tutorial we normalize and scale the valuesbetween 0 and 255 ( UnsignedByteType) to reduce the amount of required memory.Finally, both the background-corrected images from the Nuclei column andthe images from the Cytoplasm column are filtered with a small Gaussian kernel(sigma 2.0) in the Preprocessing meta node. The results are appended to the table.3.3 SegmentationIn order to classify the cells into positive and negative ones according to their phenotypes, both the nuclei and their cytoplasm have to be segmented. The subworkflowfor this segmentation is encapsulated in the Segmentation meta node and is shownin Figure 11.Fig. 11 Workflow to segment the nuclei and cytoplasm, respectively.The images in the column Nuclei are segmented using the well-known Otsu(Otsu, 1975) thresholding algorithm, which is implemented in the Global Thresholder node. The output of the node is an image consisting only of black and whitepixels. At each position of this binary image indication is given of whether the pixelbelongs to a nucleus or to the background of the image. In order to split potentialtouching objects into the individual nuclei, the ImageJ1 Watershed Macro is executed on the binary images using the ImageJ1 Macro node, which is part of theKNIME Image Processing - ImageJ Integration. The subsequent Connected Component Analysis node derives a labeling from the binary images, which determineswhether each pixel belongs to an individual nuclei as opposed to determining merelywhether the pixel belongs to the nuclei or the background. The result is appended

KNIME for Open-Source BioImage Analysis - A Tutorial13to the table. Thanks to the connected Labeling Filter node, objects that are eithertoo small or too big can be removed from the labeling by manually defining theexpected size of the nuclei. In this workflow we set the minimum size of nuclei to50. The remaining nuclei now serve as seeding points for the segmentation of thecytoplasm. Starting at each nucleus in parallel, the region growing algorithm implemented in the Voronoi Segmentation node extends the seeding segments until nomore pixels can be added to the individual segments. This is the case if a pixel hasalready been added to another segment or the intensity value of a pixel is lower thana manually defined threshold. The Voronoi Segmentation was configured to returnthe segmentation of the cytoplasm without the seeds, obtained with a threshold of25 and Fill Holes activated.Figure 12 shows the resulting segmentation of the images in the Cytoplasm channel, using the Interactive Segmentation View node.Fig. 12 Results of the Voronoi Segmentation.For other segmentation tasks, KNIME Image Processing offers a wide range ofsimple and more advanced segmentation techniques. Besides established algorithmssuch as Graph Cuts or Local Thresholding, the KNIME Image Processing - Supervised Image Segmentation (SUISE) extension comprises nodes for supervised pixeland segment classification10 .10For details see the example workflows on n.

14Christian Dietz and Michael R. Berthold3.4 Feature ExtractionAfter certain objects have been identified and segmented, features can be calculatedfrom either the derived labelings alone or the combination with their source images.Features numerically describe the individual objects and are instrumental in drawingconclusions from the acquired images. These features are therefore part of most ofthe image processing and analysis tasks.Fig. 13 Detailed look into the Feature Extraction meta node. First Order Statistics, Geometric andHaralick Features are extracted individually for the Cytoplasm and the Nuclei channel.KNIME Image Processing provides several feature implementations, for example simple first order statistics of the intensity values of a segment (mean intensity,standard deviation, kurtosis etc.) or geometric properties of a segment (roundness,size, convexity, Zernike Moments (Khotanzad and Hong, 1990), Fourier shape descriptors, etc.), as well as more complex texture measurements (Haralick (Haralicket al, 1973), Tamura (Tamura et al, 1978), etc.).Figure 14 shows the output table of the Joiner node in Figure 13, which combinesthe results from the preceding Image Segment Feature nodes by joining the rows ofthe individual tables according to their RowId. Given the nuclei (Channel 1), thecytoplasm (Channel 2) and their corresponding labelings, which were derived in theprevious Segmentation meta node, these nodes calculate for each identified objectthe first order statistics, haralick texture features and several geometric properties.Each row of the output table of the Feature Extraction meta node corresponds to thenumerical descriptions of a single object.

KNIME for Open-Source BioImage Analysis - A Tutorial15Fig. 14 Output table of Image Segment Feature node. Each row corresponds to the numericalmeasurements of a cells nucleus and cytoplasm.3.5 Model LearningThe output of the Feature Extraction meta node can now be connected to KNIMEnodes, which allow operations to be performed on numerical data.Fig. 15 Detailed look into the Model Learning meta node.Typical examples include nodes for statistical testing, machine learning and datamining or visualization. In this example, a supervised classification of the nucleiand cytoplasm is performed based on the calculated features, in order to determinewhether it is considered positive or negative (see Figure 15). Therefore, as a first

16Christian Dietz and Michael R. Bertholdstep, the ground-truth data, which is part of the publicly available high-contentscreening image data set, is read into KNIME as a text file and joined with thealready loaded image data. The ground-truth data contains indications of the classesfor several cells, which then serve as the training data for a supervised learningalgorithm11 .However, if this ground-truth data is not available, users can manually createthis information by using the Interactive Labeling Editor node. Given the groundtruth and the numerical description of the nuclei and cytoplasm, the Decision TreeLearner node can be used to train a decision tree model, which in turn can be applied to cells as yet unseen using the Decision Tree Predictor node (see 15). Theoutput of the Decision Tree Predictor node comprises an additional column withthe classification result.The Decision Tree model with default configuration settings is used in this example. However, other machine learning techniques can easily be applied instead, forexample Support Vector Machines (Scholkopf and Smola, 2001), Random Forests(Breiman, 2001) or any other algorithm, which are either included in KNIME oravailable as a KNIME extension, such as Weka, R or Python.Fig. 16 Boosting of a Naive-Bayes learner.Figure 16, for instance, depicts the well-known Boosting algorithm, which isoffered with the KNIME Ensemble Learning plugin and also comprises nodes forBagging or Stacking.3.6 Evaluation and ValidationUsers often want to manually explore and validate the information extracted fromthe raw images, as the features or the results of a classification task. KNIME itselfoffers a wide range of functionalities to visualize numerical data. Scatter plots, line11For details see Phenotype Classification workflow at http://knime.imagej.net/aaec.

KNIME for Open-Source BioImage Analysis - A Tutorial17Fig. 17 Detailed look into the Evaluation and Validation meta node.plots, bar plots or histograms are just some examples of those that are offered. Evenmore plots are available with the R and Python extensions in KNIME. Furthermore,KNIME provides nodes for statistical significance testing, for example T-Tests orANOVA Testing.Fig. 18 Line-Plot visualizing the classification results of the Model Learning meta node.The Evaluation and Validation meta node comprises one example of how to visualize the results from the classification conducted in the Model Learning (see Figure17). The resulting line-plot (see Figure 18) contains the counts of positive and negative cells of images which are part of row D in the well-plate. First, the Row Filternode removes all images that are not part of row D. The subsequent Group By nodecounts the number of positive and negative cells for each image in row D, while thePivoting node arranges the KNIME table, such that the cell counts appear next to

18Christian Dietz and Michael R. Bertholdeach other. It can be observed that the number of positive cells increases over thecolumns of row D of the well-plate, which meets the expecations 12 .4 ConclusionsIn this tutorial the basic concepts of KNIME Image Processing are introduced andthe advantages of combining different software packages in a single understandable,multi-domain workflow through KNIME are demonstrated by means of an exampleworkflow for phenotype classification. The applied techniques in this use-case aresimple and exemplary. However, the already published workflows in (Saha et al,2013; Lodermeyer et al, 2013; Gunkel et al, 2014; Strauch et al, 2014; Aligeti et al,2014) solve simple problems like counting cells or measuring the intensity of segmented cells, as well as more complex tasks involving machine learning and datamining techniques. For instance, in (Gunkel et al, 2014) the entire image acquisitionprocess was controlled by a KNIME workfl

covers the most fundamental concepts of KNIME Image Processing, which are im-portant for understanding the image processing workflow explained in Section 3. 2.1 Download and Installation The open analytics platform KNIME can be downloaded and installed from the KNIME website5. KNIME co