A pulmonary nodule detection method based on residual learning and dense connection

Feng Zhang, Yutong Xie, Yong Xia, Yanning Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Pulmonary nodule detection using chest CT scan is an essential but challenging step towards the early diagnosis of lung cancer. Although a number of deep learning-based methods have been published in the literature, these methods still suffer from less accuracy. In this paper, we propose a novel pulmonary module detection method, which uses a 3D residual U-Net (3D RU-Net) for nodule candidate detection and a 3D densely connected CNN (3D DC-Net) for false positive reduction. 3D RU-Net contains residual blocks in both contracting and expansive paths, and 3D DC-Net leverages three dense blocks to facilitate gradients flow. We evaluated our method on the benchmark LUng Nodule Analysis 2016 (LUNA16) dataset and achieved a CPM score of 0.941, which is higher than those achieved by five competing methods. Our results suggest that the proposed method can effectively detect pulmonary nodules on chest CT.

Original languageEnglish
Title of host publicationDomain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data First MICCAI Workshop, DART 2019 and First International Workshop, MIL3ID 2019 Shenzhen, Held in Conjunction with MICCAI 2019 Shenzhen, 2019 Proceedings
EditorsQian Wang, Fausto Milletari, Nicola Rieke, Hien V. Nguyen, Badri Roysam, Shadi Albarqouni, M. Jorge Cardoso, Ziyue Xu, Konstantinos Kamnitsas, Vishal Patel, Steve Jiang, Kevin Zhou, Khoa Luu, Ngan Le
PublisherSpringer
Pages72-80
Number of pages9
ISBN (Print)9783030333904
DOIs
StatePublished - 2019
Event1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: 13 Oct 201917 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11795 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st MICCAI Workshop on Domain Adaptation and Representation Transfer, DART 2019, and the 1st International Workshop on Medical Image Learning with Less Labels and Imperfect Data, MIL3ID 2019, held in conjunction with 22nd International Conference on Medical Image Computing and Computer- Assisted Intervention, MICCAI 2019
Country/TerritoryChina
CityShenzhen
Period13/10/1917/10/19

Keywords

  • Chest CT
  • Dense connection
  • Pulmonary nodule detection
  • Residual learning

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