page_title: Bioimage informatics
section_title: Segmentation
hierarchical_section_title: Bioimage informatics / Important Problems / Segmentation
caption_attribution_description: English: This is a hand-segmented image of the mouse fibroblasts (NIH 3T3). Nuclear channel is shown.
context_page_description: Bioimage informatics is a subfield of bioinformatics and computational biology. It focuses on the use of computational techniques to analyze bioimages, especially cellular and molecular images, at large scale and high throughput. The goal is to obtain useful knowledge out of complicated and heterogeneous image and related metadata.
  Automated microscopes are able to collect large numbers of images with minimal intervention. This has led to a data explosion, which absolutely requires automatic processing. Additionally, and surprisingly, for several of these tasks, there is evidence that automated systems can perform better than humans. In addition, automated systems are unbiased, unlike human based analysis whose evaluation may be influenced by the desired outcome.
  There has been an increasing focus on developing novel image processing, computer vision, data mining, database and visualization techniques to extract, compare, search and manage the biological knowledge in these data-intensive problems.
context_section_description: Segmentation of cells is an important sub-problem in many of the fields below (and sometimes useful on its own if the goal is only to obtain a cell count in a viability assay). The goal is to identify the boundaries of cells in a multi-cell image. This allows for processing each cell individually to measure parameters. In 3D data, segmentation must be performed in 3D space.
  As the imaging of a nuclear marker is common across many images, a widely used protocol is to segment the nuclei. This can be useful by itself if nuclear measurements are needed or it can serve to seed a watershed which extends the segmentation to the whole image.
  All major segmentation methods have been reported on cell images, from simple thresholding to level set methods. Because there are multiple image modalities and different cell types, each of which implies different tradeoffs, there is no single accepted solution for this problem.
  Cell image segmentation as an important procedure is often used to study gene expression and colocalization relationship etc. of individual cells. In such cases of single-cell analysis it is often needed to uniquely determine the identities of cells while segmenting the cells. Such a recognition task is often non-trivial computationally. For model organisms such as C. elegans that have well-defined cell lineages, it is possible to explicitly recognize the cell identities via image analysis, by combining both image segmentation and pattern recognition methods. Simultaneous segmentation and recognition of cells  has also been proposed as a more accurate solution for this problem when an "atlas" or other prior information of cells is available. Since gene expression at single cell resolution can be obtained using these types of imaging based approaches, it is possible to combine these methods with other single cell gene expression quantification methods such as RNAseq.
caption: Example image for segmentation problem. Shown are nuclei of mouse NIH 3T3, stained with Hoechst and a  segmentation in red.[8]
exif: {}