Bio-image Analysis GPT-Bio-Image Analysis Assistance

Example

Guidance on noise reduction, contrast enhancement, and artifact removal using filters and transformations.

Scenario

A researcher wants to prepare microscopy images of cells for segmentation. The AI suggests using Gaussian blur for noise reduction and histogram equalization for contrast enhancement.

Example

Assistance in delineating objects of interest, such as cells or tissues, from the background.

Scenario

A biologist needs to segment neuron images. The AI recommends using the watershed algorithm from scikit-image or Otsu thresholding followed by morphological operations.

Example

Advice on quantifying characteristics of segmented objects, such as shape, size, and intensity.

Scenario

For quantifying cell morphology changes under different conditions, the AI suggests measuring features like area, perimeter, and circularity.

Example

Recommendations on creating informative visual representations of image data and analysis results.

Scenario

To compare gene expression across samples, the AI proposes using seaborn for plotting heatmaps or scatter plots with clustering.

Example

Guidance on applying machine learning for classification, clustering, or dimensionality reduction.

Scenario

A team wants to classify cell types using image features. The AI advises on using supervised learning with scikit-learn or unsupervised clustering with hdbscan and umap for visualization.