Manual segmentation is used as the gold-standard for evaluating neural networks on automated image segmentation tasks. Due to considerable heterogeneity in shapes, colours and textures, demarcating object boundaries is particularly difficult in biomedical images, resulting in significant inter and intra-rater variability. Approaches, such as soft labelling and distance penalty term, apply a global transformation to the ground truth, redefining the loss function with respect to uncertainty. However, global operations are computationally expensive, and neither approach accurately reflects the uncertainty underlying manual annotation. In this paper, we propose the Boundary Uncertainty, which uses morphological operations to restrict soft labelling to object boundaries, providing an appropriate representation of uncertainty in ground truth labels, and may be adapted to enable robust model training where systematic manual segmentation errors are present. We incorporate Boundary Uncertainty with the Dice loss, achieving consistently improved performance across three well-validated biomedical imaging datasets compared to soft labelling and distance-weighted penalty. Boundary Uncertainty not only more accurately reflects the segmentation process, but it is also efficient, robust to segmentation errors and exhibits better generalisation.
翻译:手工分割是评价自动图像分割任务神经网络的黄金标准。由于形状、颜色和纹理方面的差异性很大,生物医学图像中标定物体边界特别困难,造成重大的跨河和跨河之间的差异性。软标签和距离惩罚术语等方法将全球变异应用于地面真相,重新界定不确定性方面的损失功能。然而,全球作业在计算上费用很高,而且没有一种方法准确地反映人工人工注释的不确定性。在本文中,我们提议边界不确定性,它使用形态操作限制软标签到物体边界,在地面真相标签中适当表示不确定性,并可能进行调整,以便能够在出现系统人工分割错误的情况下进行强有力的示范培训。我们将边界不确定性与Dice损失结合起来,在三个经过充分验证的生物成像数据集之间不断提高性能,而采用软标签和距离加权刑罚。边界不确定性不仅更准确地反映分解过程,而且还能有效、稳健地应对分解错误,并更好地展示一般化。