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- CELLPROFILER WORM TOOLBOX PYTHON SOURCE CODE INSTALL
- CELLPROFILER WORM TOOLBOX PYTHON SOURCE CODE SOFTWARE
- CELLPROFILER WORM TOOLBOX PYTHON SOURCE CODE SERIES
In the case of WormLength, since the worm detection performance was highly dependent on image analysis parameters, appropriate parameter values were used. Three sample images (WL1.jpeg, WL2.jpeg, and W元.jpeg) included in AniLength were analyzed using AniLength and WormLength with the image analysis parameters presented in Table 2. WormLength detects worms using several image analysis parameters but does not use artificial intelligence. The user can first attempt to use the default values without alterations, considering they are supposed to cover various imaging conditions. Second, to quickly exclude the nonworms from the objects detected through the region extraction and speed up the analysis, the user must specify the minimum and maximum values of the worms’ area in pixels, as well as the minimum and maximum values of the width or height of the rectangular bounding box in pixels. Except in special cases, the user can use default values without complications. The larger the threshold value, the better the detection of the worm but the more severe the noise. In general, the processing box size can be set larger than the thickness of the worm to be detected. First, it is important to use an appropriate processing box size and threshold value for adaptive thresholding binarization to correctly detect the worms and increase the processing speed.
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Then, the true length in μm is calculated by multiplying the total distance by the conversion factor, which is the ratio between the length in pixels and the length in μm.Īfter the file is specified, the user needs to set various image processing parameters. After dividing the skeleton curve into 15 equal parts, the total distance in pixels of the path connecting the 16 points of the skeleton curve is calculated. The actual length of the worm is measured while the skeleton curve of the object is tracked in pixels. This image is used as an input to the DNN to determine whether it is a worm. An image for prediction is created by overwriting the outline and skeleton curves over the original color image. Sometimes, the skeleton curve does not reach the end of the worm’s head or tail therefore, it is extended to the ends of the head and tail by the software. Any objects with more than two end points are excluded from the analysis. Only objects with two end points without any other branches are analyzed.
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The method utilized is as follows: The outline of the object is extracted from the detected binary object, and the skeleton curve running through the middle of the binary object is extracted by skeletonizing the binary object. The next step is to create an image for the DNN-based image classification prediction, via several steps, to detect the worms.
CELLPROFILER WORM TOOLBOX PYTHON SOURCE CODE SOFTWARE
Sample images included in this software were images previously taken from the author’s previous nanomaterial toxicity studies [
CELLPROFILER WORM TOOLBOX PYTHON SOURCE CODE INSTALL
External libraries including TensorFlow (for example, ) are provided in the form of ML.NET packages and are already included in AniLength therefore, it is not necessary to install them separately. However, even if no GPU is available, the central processing unit (CPU) can still perform training and predictions albeit slowly.
CELLPROFILER WORM TOOLBOX PYTHON SOURCE CODE SERIES
Installing an Nvidia series graphics processing unit (GPU) card that uses compute unified device architecture (CUDA) cores on the computer, as well as properly installing both CUDA 10.0, and NVIDIA CUDA Deep Neural Network (cuDNN) 7.6 can expedite the DNN training and prediction with the GPU. AniLength was developed to perform DNN-based image classification learning and prediction using ML.NET’s TensorFlow interface. ], a machine-learning platform of Microsoft, was used for DNN-based image classification.