论文标题
通过基于知识的规则和肺结节的自我适应校正，一种粗到精细的形态学方法
A Coarse-to-fine Morphological Approach With Knowledge-based Rules and Self-adapting Correction for Lung Nodules Segmentation
论文作者
论文摘要
精确概述结节的分割模块是计算机辅助诊断（CAD）系统的关键步骤。这样一个模块中最具挑战性的部分是如何实现分割的高精度，尤其是对于并置，非固体和小结节。在这项研究中，我们提出了一种粗到精细的方法，该方法可以通过新颖的自我适应校正算法极大地提高阈值方法性能，并有效地消除了具有明确定义的基于知识的原理的嘈杂像素。与最近强大的形态基准相比，我们的算法，通过结合数据集特征，在公共LIDC-IDRI数据集（DSC 0.699）和我们的私人LC015数据集（DSC 0.760）上实现最先进的性能，这与SOTA Deep Learning基于基于基于的模型模型的表演紧密相关。此外，与大多数可用的形态学方法不同，只能准确地分割隔离和良好的结节，我们方法的精度完全独立于结节类型或直径，证明其适用性和通用性。
The segmentation module which precisely outlines the nodules is a crucial step in a computer-aided diagnosis(CAD) system. The most challenging part of such a module is how to achieve high accuracy of the segmentation, especially for the juxtapleural, non-solid and small nodules. In this research, we present a coarse-to-fine methodology that greatly improves the thresholding method performance with a novel self-adapting correction algorithm and effectively removes noisy pixels with well-defined knowledge-based principles. Compared with recent strong morphological baselines, our algorithm, by combining dataset features, achieves state-of-the-art performance on both the public LIDC-IDRI dataset (DSC 0.699) and our private LC015 dataset (DSC 0.760) which closely approaches the SOTA deep learning-based models' performances. Furthermore, unlike most available morphological methods that can only segment the isolated and well-circumscribed nodules accurately, the precision of our method is totally independent of the nodule type or diameter, proving its applicability and generality.
