MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways

Yanzhou Dai; Qiang Wang; Shulin Cui; Yang Yin; Weibo Song

doi:10.1007/s11517-024-03252-3

MediLite3DNet: A lightweight network for segmentation of nasopharyngeal airways

Med Biol Eng Comput. 2024 Nov 29. doi: 10.1007/s11517-024-03252-3. Online ahead of print.

Authors

Yanzhou Dai¹, Qiang Wang², Shulin Cui³, Yang Yin³, Weibo Song⁴

Affiliations

¹ Dalian Jiaotong University, Dalian, China.
² Dalian Jiaotong University, Dalian, China. 04-dangqiang@163.com.
³ Central Hospital of Dalian University of Technology, Dalian, China.
⁴ Dalian Ocean University, Dalian, China.

PMID: 39612131
DOI: 10.1007/s11517-024-03252-3

Abstract

The precise segmentation and three-dimensional reconstruction of the nasopharyngeal airway are crucial for the diagnosis and treatment of adenoid hypertrophy in children. However, traditional methods face challenges such as information loss and low computational efficiency when addressing this task. To overcome these issues, this paper introduces an innovative lightweight 3D medical image segmentation network-MediLite3DNet. The core of this network is the Parallel Multi-Scale High-Resolution Network (PMHNet), which effectively retains detailed features of the airway and optimizes the fusion of multi-scale features through its parallel structure. In response to the complexity of existing networks and their reliance on vast amounts of training data, this paper presents an efficient Hierarchical Decoupled Convolution Module (EHDC) to reduce computational costs while maintaining efficient feature extraction capabilities. Furthermore, to enhance the accuracy of segmentation, a lightweight Channel and Spatial Attention Mechanism (LCSA) is proposed. This mechanism identifies and emphasizes key channels and spatial features, improving the processing of complex medical images while controlling the increase in the number of parameters. Experiments conducted on a clinical CT dataset demonstrate the network's exceptional performance, with a Dice coefficient of 97.42%, sensitivity of 98.69%, and Jaccard index of 95%. Maintaining high precision, the model has a parameter count of only 0.227M and a floating-point operation count (FLOPs) of 24.526G, proving its computational efficiency. The significance of this study is that it provides a highly efficient and accurate diagnostic tool for children with adenoid hypertrophy. Additionally, with the innovative MediLite3DNet design, it brings a new lightweight solution to the domain of medical image segmentation.

Keywords: Adenoid hypertrophy; Lightweight network; Medical image segmentation; Nasopharyngeal airway segmentation.