Medical Image Segmentation Types and Applications
Image segmentation is a technique that separates regions of interest (ROIs) from medical images and videos. It can help you label and annotate data more efficiently and effectively when you train computer vision models for healthcare problems. This can enhance the quality and performance of your models.
By applying image segmentation, you can detect and isolate objects, divide pixels into groups, and use these groups as labels for your computer vision models.
In this story, you will learn about image segmentation in medical imaging, how it can benefit your healthcare computer vision projects, what are some of its applications, and how to implement it.
What is Medical Segmentation
Image segmentation is a technique that separates regions of interest (ROIs) from medical images and videos. It is a crucial step for annotating and labeling data, which is needed to train computer vision models (CV, AI, ML, etc.) for medical diagnostics.
This article will explain image segmentation in more detail and how it can help you with your computer vision projects.
Image segmentation can be applied to various types of medical images, such as DICOM and NIfTI images, CT scans, X-Rays, and MRI files. It can help you identify and…