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After obtaining the boundary uncertainty map and track record excluded image, we concatenated these two forms of pictures and fed them into the segmentation network. Since the concatenated photos ended up unique from the initial illustrations or photos and contained hardly any track record info, the segmentation network can easily detect object boundaries and thereby extract The complete item areas properly making use of a straightforward experiment configuration.
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Desk 6 confirmed the outcome from the made system in extracting the left and ideal lungs from Xray photographs utilizing boundary uncertainty maps in a few other ways. As shown by the results, our formulated technique attained the lowest segmentation functionality, with the typical DS of 0.9437 when simply properly trained on boundary uncertainty maps, nonetheless it experienced improved general performance when combining the uncertainty maps with the original photographs or their history excluded version for network teaching (with here the standard DS of 0.