A watershed-based algorithm to segment and classify cells in fluorescence microscopy images
Imaging assays of cellular function, especially those using fluorescent stains, are ubiquitous in the biological and medical sciences. Despite advances in computer vision, such images are often analyzed using only manual or rudimentary automated processes. Watershed-based segmentation is an effective technique for identifying objects in images; it outperforms commonly used image analysis methods, but requires familiarity with computer-vision techniques to be applied successfully. In this report, we present and implement a watershed-based image analysis and classification algorithm in a GUI, enabling a broad set of users to easily understand the algorithm and adjust the parameters to their specific needs. As an example, we implement this algorithm to find and classify cells in a complex imaging assay for mitochondrial function. In a second example, we demonstrate a workflow using manual comparisons and receiver operator characteristics to optimize the algorithm parameters for finding live and dead cells in a standard viability assay. Overall, this watershed-based algorithm is more advanced than traditional thresholding and can produce optimized, automated results. By incorporating associated pre-processing steps in the GUI, the algorithm is also easily adjusted, rendering it user-friendly.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Learn to segment single cells with deep distance estimator and deep cell detector
Single cell segmentation is critical and challenging in live cell imaging data analysis. Traditional image processing methods and tools require time-consuming and labor-intensive efforts of manually fine-tuning parameter…
Cell SegmentationClassificationSegmentationSegmentation of Clustered Nuclei With Shape Markers and Marking Function
We present a method to separate clustered nuclei from fluorescence microscopy cellular images, using shape markers and marking function in a watershed-like algorithm. Shape markers are extracted using an adaptive H-mi…
Cell SegmentationSegmentationAnalysis of the performance of U-Net neural networks for the segmentation of living cells
The automated analysis of microscopy images is a challenge in the context of single-cell tracking and quantification. This work has as goals the study of the performance of deep learning for segmenting microscopy images …
Cell SegmentationCell TrackingImage SegmentationSegmentation+1A fully automated end-to-end process for fluorescence microscopy images of yeast cells: From segmentation to detection and classification
In recent years, an enormous amount of fluorescence microscopy images were collected in high-throughput lab settings. Analyzing and extracting relevant information from all images in a short time is almost impossible. De…
General ClassificationGPUCell Segmentation by Combining Marker-Controlled Watershed and Deep Learning
We propose a cell segmentation method for analyzing images of densely clustered cells. The method combines the strengths of marker-controlled watershed transformation and a convolutional neural network (CNN). We demonstr…
Cell DetectionCell SegmentationCell TrackingData Augmentation+2