Deep learning-based approaches to delineating 3D structure depend on accurate annotations to train the networks. Yet, in practice, people, no matter how conscientious, have trouble precisely delineating in 3D and on a large scale, in part because the data is often hard to interpret visually and in part because the 3D interfaces are awkward to use. In this paper, we introduce a method that explicitly accounts for annotation inaccuracies. To this end, we treat the annotations as active contour models that can deform themselves while preserving their topology. This enables us to jointly train the network and correct potential errors in the original annotations. The result is an approach that boosts performance of deep networks trained with potentially inaccurate annotations. Code has been released at https://github.com/doruk-oner/AdjustingAnnotationswithSnakes.

翻译：深层的基于学习的分解 3D 结构的方法取决于对网络的准确描述。 然而,在实践中,人们,无论多么认真,都很难精确地分解3D和大范围,部分原因是数据往往难以解读,部分是因为3D界面难以理解,部分是因为3D界面难以使用。在本文中,我们引入了一种明确说明注释不准确的方法。为此,我们将说明视为在保存其表层的同时可以自我变形的积极轮廓模型。这使我们能够联合培训网络,纠正原始说明中的潜在错误。其结果是,通过潜在不准确的说明,提高了受过训练的深层网络的性能。代码已在 https://github.com/doruk-oner/Adjusting Annotations with Snakes发布。