Dense volumetric detection and segmentation of mediastinal lymph nodes in chest CT images

Hirohisa Oda, Holger R. Roth, Kanwal K. Bhatia, Masahiro Oda, Takayuki Kitasaka, Shingo Iwano, Hirotoshi Homma, Hirotsugu Takabatake, Masaki Mori, Hiroshi Natori, Julia A. Schnabel, Kensaku Mori

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

25 Scopus citations

Abstract

We propose a novel mediastinal lymph node detection and segmentation method from chest CT volumes based on fully convolutional networks (FCNs). Most lymph node detection methods are based on filters for blob-like structures, which are not specific for lymph nodes. The 3D U-Net is a recent example of the state-of-the-art 3D FCNs. The 3D U-Net can be trained to learn appearances of lymph nodes in order to output lymph node likelihood maps on input CT volumes. However, it is prone to oversegmentation of each lymph node due to the strong data imbalance between lymph nodes and the remaining part of the CT volumes. To moderate the balance of sizes between the target classes, we train the 3D U-Net using not only lymph node annotations but also other anatomical structures (lungs, airways, aortic arches, and pulmonary arteries) that can be extracted robustly in an automated fashion. We applied the proposed method to 45 cases of contrast-enhanced chest CT volumes. Experimental results showed that 95.5% of lymph nodes were detected with 16.3 false positives per CT volume. The segmentation results showed that the proposed method can prevent oversegmentation, achieving an average Dice score of 52.3 ± 23.1%, compared to the baseline method with 49.2 ± 23.8%, respectively.

Original languageEnglish
Title of host publicationMedical Imaging 2018
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Nicholas Petrick
PublisherSPIE
ISBN (Electronic)9781510616394
DOIs
StatePublished - 2018
Externally publishedYes
EventMedical Imaging 2018: Computer-Aided Diagnosis - Houston, United States
Duration: 12 Feb 201815 Feb 2018

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume10575
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2018: Computer-Aided Diagnosis
Country/TerritoryUnited States
CityHouston
Period12/02/1815/02/18

Keywords

  • Computer-Aided Diagnosis
  • Fully convolutional network
  • Imbalance weighting

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