A fast weak-supervised pulmonary nodule segmentation method based on modified self-adaptive FCM algorithm
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One of the key problems of computer-aided diagnosis is to segment specific anatomy structures in tomographic images as fast and accurately as possible, which is an important step toward identifying pathologically changed tissues. The segmentation accuracy has a significant impact on diseases diagnosis as well as the therapeutic efficacy. This paper presents a fast and robust weak-supervised pulmonary nodule segmentation method based on a modified self-adaptive FCM algorithm. To improve the traditional FCM, we firstly introduce an enhanced objective function, which computes the membership value according to both the grayscale similarity and spatial similarity between central pixels and neighbors. Then, a probability relation matrix between clusters and categories is constructed by using a small amount of prior knowledge learned from training samples. Based on this matrix, we realize a weak-supervised pulmonary nodules segmentation for unlabeled lung CT images. More specifically, the proposed method utilizes the relation matrix to calculate the category index of every pixel by Bayesian theory and PSOm algorithm. The quantitative experimental results on a test dataset, including 115 2-D clinical CT data, demonstrate the accuracy, efficiency and generality of the proposed weak-supervised strategy in pulmonary nodules segmentation.
KeywordsModified self-adaptive FCM Probability relation matrix PSOm algorithm Unlabeled CT images Weak-supervised segmentation
This work is supported by National Natural Science Foundation (61572286 and 61472220), Natural Science Foundation of Shandong Province (2016ZRB01143), University Independent Innovation Plan (201401216), Science and Technology Development Plan of Shandong province (2014GGX101037), and also supported by Fostering Project of Dominant Discipline and Talent Team of Shandong Province Higher Education Institutions. The authors also gratefully acknowledge the helpful comments and suggestions of the reviewers, which have improved the presentation significantly.
Compliance with ethical standards
Conflict of interest
Hui Liu declares that she has no conflict of interest. Fenghuan Geng declares that he has no conflict of interest. Qiang Guo declares that he has no conflict of interest. Caiqing Zhang declares that he has no conflict of interest. Caiming Zhang declares that he has no conflict of interest.
This article does not contain any studies with human participants or animals performed by any of the authors.
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