Manual segmentation


Before starting this lesson, you should be familiar with:

Learning Objectives

After completing this lesson, learners should be able to:
  • Manually segment parts of a 2-D (3-D) image.


Manual segmentation is useful in many ways. If the dataset of interest is small, manual segmentation may be faster than designing an automated segmentation workflow, or automated segmentation may be very difficult. In addition, manual segmentation can serve as training and validation data for (deep-learning based) automated segmentation algorithms.

Concept map

graph TD I("Image") --> MS("Manual segmentation") MS --> LM("Label mask image")


Manual segmentation in ITK-SNAP and corresponding label mask image.

Manual segmentation considerations

How to deal with objects that are not fully in the image?

Should objects be separated by background pixels?


Show activity for:



Fill in the blanks

  1. Manual segmentations are often stored as ___.


  1. label mask images

Follow-up material

Recommended follow-up modules:

Learn more: