UniverSeg: Universal Scissor for Medical Image Segmentation
<p>In general,<a href="https://en.wikipedia.org/wiki/Image_segmentation" rel="noopener ugc nofollow" target="_blank"> image segmentation</a> is a central task of computer vision. Especially in medicine, it is the first step for a whole series of analyses. In medicine, many tools produce images that have different characteristics (different tools, normalization between different hospitals, different domains, different labels).</p>
<p><img alt="UniverSeg: Universal Medical Image Segmentation" src="https://miro.medium.com/v2/resize:fit:700/1*_t-NvLRnSlMaR-RXhpbCeQ.png" style="height:236px; width:700px" /></p>
<p>image source: here</p>
<p>Given its importance, a great many studies have been devoted to medical image segmentation. Various techniques have been used to be able to analyze images. In recent years deep learning models have been shown to be the most capable, especially one architecture that has been maintained as the standard:</p>
<p><a href="https://levelup.gitconnected.com/universeg-universal-scissor-for-medical-image-segmentation-edf90bb15922"><strong>Click Here</strong></a></p>