A Coarse-to-fine Morphological Approach With Knowledge-based Rules and Self-adapting Correction for Lung Nodules Segmentation
Abstract
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.
Cite
@article{arxiv.2202.03433,
title = {A Coarse-to-fine Morphological Approach With Knowledge-based Rules and Self-adapting Correction for Lung Nodules Segmentation},
author = {Xinliang Fu and Jiayin Zheng and Juanyun Mai and Yanbo Shao and Minghao Wang and Linyu Li and Zhaoqi Diao and Yulong Chen and Jianyu Xiao and Jian You and Airu Yin and Yang Yang and Xiangcheng Qiu and Jinsheng Tao and Bo Wang and Hua Ji},
journal= {arXiv preprint arXiv:2202.03433},
year = {2022}
}