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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 isolate objects and regions in the images.
There are many methods for image segmentation, ranging from traditional to deep-learning based ones. All of them require high accuracy and precision, as any errors in the annotation or model-building stage can have serious implications for patients, treatments, and healthcare providers.
This guide is for medical ML, DataOps, and annotation teams and leaders who want to learn how to use image segmentation for their computer vision projects.
Medical Image Segmentation In Healthcare Computer Vision Models
Image segmentation is a technique that separates regions of interest (ROIs) from medical images and videos. It is helpful for many computer vision use cases in healthcare, such as the following:
Radiology
