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Abstract：
Accurate image segmentation plays a crucial role in computer vision and medical image analysis. In this study, we developed a novel uncertainty guided deep learning strategy (UGLS) to enhance the performance of an existing neural network (i.e., U-Net) in segmenting multiple objects of interest from images with varying modalities. In the developed UGLS, a boundary uncertainty map was introduced for each object based on its coarse segmentation (obtained by the U-Net) and then combined with input images for the fine segmentation of the objects. We validated the developed method by segmenting optic cup (OC) regions from color fundus images and left and right lung regions from Xray images. Experiments on public fundus and Xray image datasets showed that the developed method achieved a average Dice Score (DS) of 0.8791 and a sensitivity (SEN) of 0.8858 for the OC segmentation, and 0.9605, 0.9607, 0.9621, and 0.9668 for the left and right lung segmentation, respectively. Our method significantly improved the segmentation performance of the U-Net, making it comparable or superior to five sophisticated networks (i.e., AU-Net, BiO-Net, AS-Net, Swin-Unet, and TransUNet).
摘要：
准确的图像分割在计算机视觉和医学图像分析中起着至关重要的作用。在这项研究中,我们开发了一种新颖的不确定性引导深度学习策略(UGLS)来增强现有神经网络的性能(即，U-Net）从具有不同模态的图像中分割多个感兴趣的对象。在发达的UGLS中，根据每个对象的粗分割（由U-Net获得）引入了边界不确定性图，然后将其与输入图像组合以进行对象的精细分割。我们通过从彩色眼底图像中分割光学杯（OC）区域以及从X射线图像中分割左右肺区域来验证所开发的方法。在公共眼底和X射线图像数据集上的实验表明，所开发的方法对OC分割的平均Dice评分（DS）为0.8791，灵敏度（SEN）为0.8858，左、右肺分割为0.9605、0.9607、0.9621和0.9668，分别。我们的方法显着提高了U-Net的分割性能，使其与五个复杂网络(即，AU-Net,BiO-Net,AS-Net,Swin-Unet,和TransUNet）。
