Allows users to classify red blood cells (RBCs). This program is a high-throughput sickle cell classification method based on the deep Convolutional Neural Networks (dCNNs). It can be used in clinical test for: (1) assessing patient’s disease severity via longitudinal tracking and patient-specific RBC mapping; and (2) intervention strategies via personalized medicine treatment monitoring.
Serves for batch processing of large series of images. Root analyzer is designed for extracting and analyzing anatomical traits from root-cross section images. It uses basic knowledge about root morphology, such as cell and tissue size and locations. It lends the program to applications on a range of species. It segments the plant root from the image's background, classifies and characterizes the cortex, stele, endodermis and metaxylem, and produces statistics about the morphological properties of the root cells and tissues.
A user-friendly image-based classification algorithm inspired by WND-CHARM in (i) its ability to capture a wide variety of morphological aspects of the image, and (ii) the absence of requirement for segmentation. In order to make such an image-based classification method easily accessible to the biological research community, CP-CHARM relies on the widely-used open-source image analysis software CellProfiler for feature extraction.
Finds and simulates cell file in light microscopy images. Cefiler permits users to avoid supervised training and allows results quality quantification. It starts by identifying cells to individualize the cells in the image. Then, this tool recognizes and individualizes the alignments of anatomical structures. It finishes by storing anatomically and typing qualitatively the cell files. The method can be applied to any images with high contrast between the walls and lumens and a clear cellular organization.
Enables users to realize batch image processing. DeepStomata is a method that provides a complete automation of stomatal aperture measurement. It processes stomata images obtained using a bright-field ocular microscope. It partially follows facial recognition and (i) detects the stomatal candidate regions, (ii) classifies the detected stomata and (iii) selects quantification of the apertures of open stomata by pore region isolation using binary-based image segmentation.
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