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cell-segmentation-master

Cell Segmentation (Master)

The workflow aims to segment cell nuclei from cytoplasm. As a first step the images need to be preprocessed by splitting the channels into separate columns, nuclei and cytoplasm. To correct for background staining and reduce noise, we are performing a background subtraction of the nuclei channel and also apply Gaussian smoothing.

For segmentation, we first use the Otsu threshold method to distinguish between foreground and background. Afterwards we identify and label connected components (=nuclei) using the Connected Component Analysis node. Subsequently, we remove cell clumps and too small nuclei. As a last step, we perform a Voronoi based segmentation that identifies and labels the corresponding cytoplasm to the already labeled nuclei.

This workflow calls a cell segmentation routine on images a list of image files. The files are divided in chunks and a workerworkflow is called in parallel for each chunk of files. At the end we get paths to a KNIME Tables containing the results perchunk. Example data is provided next to the workflow under the directory name hcs-data. Adjust the path to the folder containing raw image files.List Files/Folders Call WorkflowService ParallelChunk Start Parallel Chunk End This workflow calls a cell segmentation routine on images a list of image files. The files are divided in chunks and a workerworkflow is called in parallel for each chunk of files. At the end we get paths to a KNIME Tables containing the results perchunk. Example data is provided next to the workflow under the directory name hcs-data. Adjust the path to the folder containing raw image files.List Files/Folders Call WorkflowService ParallelChunk Start Parallel Chunk End

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: Temesgen H. Dadi
Created at: 2021-04-15
On NodePit since: 2022-04-26
Last update: 2024-05-03
Created with KNIME version: v4.5.2
Tags: cell segmentationimage processingmicroscopy

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