A Coarse-to-fine Morphological Approach With Knowledge-based Rules and Self-adapting Correction for Lung Nodules Segmentation
This work addresses the need for precise lung nodule segmentation in computer-aided diagnosis systems, particularly for difficult nodule types, offering an incremental improvement over existing morphological methods.
The paper tackles the problem of accurately segmenting lung nodules, especially challenging types like juxtapleural, non-solid, and small nodules, by proposing a coarse-to-fine morphological approach with knowledge-based rules and self-adapting correction, achieving state-of-the-art performance with Dice scores of 0.699 on a public dataset and 0.760 on a private dataset.
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.