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We combine these uncertainty maps and original input images and feed them in to the given network for a more good segmentation. Following performing these 3 steps, the network can get a significantly enhanced segmentation efficiency.
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Table 6 showed the final results with the formulated strategy in extracting the still left and correct lungs from Xray visuals using boundary uncertainty maps in 3 alternative ways. As shown by the effects, our designed system received the bottom segmentation general performance, with the normal DS of 0.9437 when merely educated on boundary uncertainty maps, however it experienced elevated general performance when combining the uncertainty maps with the original visuals or their track record excluded Model for community education (with the average DS of 0.
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We combine these uncertainty maps and unique input visuals and feed them in the presented network for a far more fantastic segmentation. Following undertaking these 3 steps, the community can get a considerably enhanced segmentation efficiency.