Texture feature-aware consistency for semi-supervised honeycomb lung lesion segmentation

• Published in: Expert systems with applications 2024-12, Vol.258, p.125119, Article 125119
• Main Authors: Xie, Jinjie, Li, Gang, Zhang, Ling, Cheng, Guijuan, Zhang, Kairu, Bai, Mingqi
• Format: Article
• Language: English
• Subjects: Honeycomb lung segmentation; Semi-supervised learning; Texture feature extraction
• ISSN: 0957-4174
• DOI: 10.1016/j.eswa.2024.125119

Accurate segmentation of honeycomb lung lesions from lung CT images is crucial for the clinical diagnosis of a wide range of lung diseases. Existing semi-supervised segmentation methods ignore the rich texture feature information in honeycomb lung CT images, resulting in inaccurate predictions in the lesion region. Therefore, this paper proposes a semi-supervised honeycomb lung lesion segmentation framework using texture feature-aware consistency. The framework consists of a shared encoder, a main decoder, and three auxiliary decoders. The different outputs produced by the decoders are used to compute the Grey Level Co-occurrence Matrix and extract texture features, respectively, and further to compute the loss of texture feature-aware consistency. In pseudo-labeling supervision, the uncertainty of each output is computed to bootstrap the pseudo-labeling supervision loss. For the small lesion recognition omission problem, a pseudo-label refinement module is constructed to reuse the multi-scale features of the encoder, focusing on small-scale lesions, and refining the pseudo-labels. Experimental results show that the proposed method outperforms other methods in the honeycomb lung segmentation task, achieving a Dice coefficient of 77.62 using 20% labeled data. Additionally, experiments on publicly available datasets further demonstrate the generalizability of the proposed method. The code is available at https://github.com/Oran9er/TFC-Net-main.