Arivis Vision4D

Step 1: Import the 3D microscopy image stack of the organoid into arivis Pro.

Step 2: Use the segmentation tools to delineate the boundaries of the organoid and its lumen.

Step 3: Apply automated segmentation algorithms to refine the segmentation results.

Step 4: Utilize the volume measurement tools to calculate the volume of the organoid and its lumen.

Step 5: Export the quantitative data for further analysis and reporting.

Step 1: Import the FIB-SEM volume electron microscopy data into arivis Pro.

Step 2: Use the object detection tools to identify individual NPCs based on their characteristic morphology.

Step 3: Apply machine learning algorithms to improve the accuracy of NPC detection.

Step 4: Measure the size, shape, and spatial distribution of the detected NPCs.

Step 5: Visualize the NPC distribution in 3D and generate statistical reports.

Step 1: Import the time-lapse microscopy image sequence into arivis Pro.

Step 2: Use the cell segmentation tools to identify individual cells in each frame.

Step 3: Apply cell tracking algorithms to link cells across consecutive frames.

Step 4: Manually correct any tracking errors using the interactive editing tools.

Step 5: Extract quantitative data on cell movement, division, and morphology.

Step 1: Design an image analysis pipeline in arivis Pro to quantify relevant cellular features.

Step 2: Use arivis Hub to automatically process the HCS image data in parallel.

Step 3: Implement quality control measures to ensure the accuracy of the analysis.

Step 4: Identify compounds that induce specific changes in cellular phenotypes.

Step 5: Generate hit lists and statistical reports for further investigation.

Step 1: Train a deep learning model in arivis Cloud to remove noise and artifacts from microscopy images.

Step 2: Integrate the trained model into an image analysis pipeline in arivis Pro.

Step 3: Apply the model to restore the image quality of microscopy images.

Step 4: Perform downstream analysis on the restored images.

Step 5: Compare the results with and without image restoration to assess the improvement.