nmPLS-Net: Segmenting Pulmonary Lobes Using nmODE

Peizhi Dong, Hao Niu, Zhang Yi and Xiuyuan Xu ()
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Peizhi Dong: College of Computer Science, Sichuan University, Chengdu 610065, China
Hao Niu: College of Computer Science, Sichuan University, Chengdu 610065, China
Zhang Yi: College of Computer Science, Sichuan University, Chengdu 610065, China
Xiuyuan Xu: College of Computer Science, Sichuan University, Chengdu 610065, China

Mathematics, 2023, vol. 11, issue 22, 1-17

Abstract: Pulmonary lobe segmentation is vital for clinical diagnosis and treatment. Deep neural network-based pulmonary lobe segmentation methods have seen rapid development. However, there are challenges that remain, e.g., pulmonary fissures are always not clear or incomplete, especially in the complex situation of the trilobed right pulmonary, which leads to relatively poor results. To address this issue, this study proposes a novel method, called nmPLS-Net, to segment pulmonary lobes effectively using nmODE. Benefiting from its nonlinear and memory capacity, we construct an encoding network based on nmODE to extract features of the entire lung and dependencies between features. Then, we build a decoding network based on edge segmentation, which segments pulmonary lobes and focuses on effectively detecting pulmonary fissures. The experimental results on two datasets demonstrate that the proposed method achieves accurate pulmonary lobe segmentation.

Keywords: pulmonary lobe segmentation; neural memory ordinary differential equation; multi-task learning (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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