HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification

Abstract Accurate segmentation and classification of pulmonary nodules are of great significance to early detection and diagnosis of lung diseases, which can reduce the risk of developing lung cancer and improve patient survival rate. In this paper, we propose an effective network for pulmonary nodu...

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Autores principales:	Ling Zhu, Hongqing Zhu, Suyi Yang, Pengyu Wang, Yang Yu
Formato:	article
Lenguaje:	EN
Publicado:	SpringerOpen 2021
Materias:	Pulmonary nodule Segmentation and classification High-resolution network Multi-scale progressive fusion Generative adversarial network Electronics TK7800-8360
Acceso en línea:	https://doaj.org/article/e046543880dd48388f1df46816893c73
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spelling	oai:doaj.org-article:e046543880dd48388f1df46816893c732021-11-14T12:08:44ZHR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification10.1186/s13640-021-00574-21687-5281https://doaj.org/article/e046543880dd48388f1df46816893c732021-11-01T00:00:00Zhttps://doi.org/10.1186/s13640-021-00574-2https://doaj.org/toc/1687-5281Abstract Accurate segmentation and classification of pulmonary nodules are of great significance to early detection and diagnosis of lung diseases, which can reduce the risk of developing lung cancer and improve patient survival rate. In this paper, we propose an effective network for pulmonary nodule segmentation and classification at one time based on adversarial training scheme. The segmentation network consists of a High-Resolution network with Multi-scale Progressive Fusion (HR-MPF) and a proposed Progressive Decoding Module (PDM) recovering final pixel-wise prediction results. Specifically, the proposed HR-MPF firstly incorporates boosted module to High-Resolution Network (HRNet) in a progressive feature fusion manner. In this case, feature communication is augmented among all levels in this high-resolution network. Then, downstream classification module would identify benign and malignant pulmonary nodules based on feature map from PDM. In the adversarial training scheme, a discriminator is set to optimize HR-MPF and PDM through back propagation. Meanwhile, a reasonably designed multi-task loss function optimizes performance of segmentation and classification overall. To improve the accuracy of boundary prediction crucial to nodule segmentation, a boundary consistency constraint is designed and incorporated in the segmentation loss function. Experiments on publicly available LUNA16 dataset show that the framework outperforms relevant advanced methods in quantitative evaluation and visual perception.Ling ZhuHongqing ZhuSuyi YangPengyu WangYang YuSpringerOpenarticlePulmonary noduleSegmentation and classificationHigh-resolution networkMulti-scale progressive fusionGenerative adversarial networkElectronicsTK7800-8360ENEURASIP Journal on Image and Video Processing, Vol 2021, Iss 1, Pp 1-26 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Pulmonary nodule Segmentation and classification High-resolution network Multi-scale progressive fusion Generative adversarial network Electronics TK7800-8360
spellingShingle	Pulmonary nodule Segmentation and classification High-resolution network Multi-scale progressive fusion Generative adversarial network Electronics TK7800-8360 Ling Zhu Hongqing Zhu Suyi Yang Pengyu Wang Yang Yu HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification
description	Abstract Accurate segmentation and classification of pulmonary nodules are of great significance to early detection and diagnosis of lung diseases, which can reduce the risk of developing lung cancer and improve patient survival rate. In this paper, we propose an effective network for pulmonary nodule segmentation and classification at one time based on adversarial training scheme. The segmentation network consists of a High-Resolution network with Multi-scale Progressive Fusion (HR-MPF) and a proposed Progressive Decoding Module (PDM) recovering final pixel-wise prediction results. Specifically, the proposed HR-MPF firstly incorporates boosted module to High-Resolution Network (HRNet) in a progressive feature fusion manner. In this case, feature communication is augmented among all levels in this high-resolution network. Then, downstream classification module would identify benign and malignant pulmonary nodules based on feature map from PDM. In the adversarial training scheme, a discriminator is set to optimize HR-MPF and PDM through back propagation. Meanwhile, a reasonably designed multi-task loss function optimizes performance of segmentation and classification overall. To improve the accuracy of boundary prediction crucial to nodule segmentation, a boundary consistency constraint is designed and incorporated in the segmentation loss function. Experiments on publicly available LUNA16 dataset show that the framework outperforms relevant advanced methods in quantitative evaluation and visual perception.
format	article
author	Ling Zhu Hongqing Zhu Suyi Yang Pengyu Wang Yang Yu
author_facet	Ling Zhu Hongqing Zhu Suyi Yang Pengyu Wang Yang Yu
author_sort	Ling Zhu
title	HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification
title_short	HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification
title_full	HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification
title_fullStr	HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification
title_full_unstemmed	HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification
title_sort	hr-mpf: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification
publisher	SpringerOpen
publishDate	2021
url	https://doaj.org/article/e046543880dd48388f1df46816893c73
work_keys_str_mv	AT lingzhu hrmpfhighresolutionrepresentationnetworkwithmultiscaleprogressivefusionforpulmonarynodulesegmentationandclassification AT hongqingzhu hrmpfhighresolutionrepresentationnetworkwithmultiscaleprogressivefusionforpulmonarynodulesegmentationandclassification AT suyiyang hrmpfhighresolutionrepresentationnetworkwithmultiscaleprogressivefusionforpulmonarynodulesegmentationandclassification AT pengyuwang hrmpfhighresolutionrepresentationnetworkwithmultiscaleprogressivefusionforpulmonarynodulesegmentationandclassification AT yangyu hrmpfhighresolutionrepresentationnetworkwithmultiscaleprogressivefusionforpulmonarynodulesegmentationandclassification
_version_	1718429438074421248

HR-MPF: high-resolution representation network with multi-scale progressive fusion for pulmonary nodule segmentation and classification

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