segmentation
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segmentation
What has come from my previous lifetime experience is a technology of medical imaging. The subset of segmentation motivation should be mentioned forst among the numerous others. The segmentation of structure from 2D and 3D images is an important first step in analyzing medical data. For example, the segmentation of the brain is necessary in an MR image, before it can be rendered in 3D for visualization purposes. Segmentation can also be used to automatically detect the head and abdomen of a fetus from an ultrasound image. The boundaries can then be used to get quantitative estimates of organ sizes and provide aid in any necessary diagnoses. Another important application is registration. It may be easier, or at least less error prone to segment objects in multiple images prior to registration. This is especially true in images from different modalities such as CT and MRI. Image-guided surgery is one other important application of segmentation. Recent advances in technology have made it possibleto acquire images of the patient while the surgery is taking place. The goal is then to segment relevant regions of interest and overlay them on an image of the patient to help guide the surgeon in his work. Segmentation is therefore a very important task in medical imaging. However, manual segmentation is not only a tedious and time consuming process, it is also inaccurate. Segmentation by experts has shown to be variable up to 20%. It is therefore desirable to use algorithms that are accurate and require as little user interaction as possible. Surely, we should be carefull with this.
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