Introduction to Microscopy and Image Segmenation

  • Recieve raw images (tiffs? convert to? alignment)
  • Segment regions of interest
  • Analysis
  • Publish

Terms of Reference

  • Image Preprocessing:

Techniques applied to raw microscopy images before segmentation, including normalization, denoising, and contrast enhancement, to improve the model’s ability to extract meaningful features.

  • Instance Segmentation:

Identifying and delineating individual instances of objects within an image. In microscopy, this could involve distinguishing and outlining individual cells.

  • Semantic Segmentation:

Assigning a label to each pixel in an image, categorizing regions based on their semantic content. In microscopy, this could involve labeling different cellular structures or organelles.

  • Annotation/Labeling:

The process of manually marking and identifying specific features or objects of interest in an image dataset. In the context of image segmentation for microscopy, annotation involves creating pixel-level labels for regions like cells or organelles to train machine learning models.

  • AI-assisted Labeling

The process of utilizing artificial intelligence (AI) algorithms to automate or enhance the manual annotation or labeling of data. In the context of image segmentation for microscopy, AI-assisted labeling involves the collaboration between human annotators and machine learning models. The algorithm assists human annotators by providing initial or suggested annotations, reducing the manual workload and potentially improving the efficiency and consistency of the labeling process. This symbiotic approach leverages the strengths of both human expertise and machine learning capabilities to accelerate the creation of labeled datasets for training segmentation models. AI-assisted labeling is particularly valuable in scenarios where large volumes of annotated data are required, and it helps bridge the gap between the capabilities of human annotators and the demands of training sophisticated machine learning models.

  • Training Set:

The subset of a dataset used to train a machine learning model. In image segmentation, this involves using annotated images to teach the model how to identify and delineate specific structures.

  • Testing Set:

The subset of a dataset used to assess the performance and generalization of a trained model. In image segmentation, this involves evaluating how well the model can accurately segment unseen data.

  • Validation Set:

An additional subset of the dataset used during the training phase to fine-tune model parameters and avoid overfitting. It helps ensure that the model generalizes well to new, unseen data.

  • Masks:

Binary images or pixel-level annotations that precisely define the boundaries of segmented objects in the original image. In the context of microscopy image segmentation, masks indicate the exact location and shape of identified structures, such as cells or subcellular components, facilitating quantitative analysis and interpretation.

  • Convolutional Neural Network (CNN):

A type of deep neural network designed for processing grid-structured data, such as images. CNNs are widely used in image segmentation tasks in microscopy.

  • U-Net:

A specific architecture of a convolutional neural network commonly used for biomedical image segmentation, including tasks in microscopy. Its distinctive U-shape allows for effective feature extraction and spatial resolution preservation.

  • Transfer Learning:

Leveraging pre-trained models on large datasets for related tasks to improve the performance of a model on a specific segmentation task in microscopy, where data may be limited.

  • Data Augmentation:

Techniques to artificially increase the diversity of training data by applying transformations like rotation, flipping, or scaling. This is crucial for training robust segmentation models in microscopy.

  • Post-processing:

Refining segmentation results using techniques like morphological operations, contour smoothing, or merging to enhance the accuracy and coherence of the segmented regions.