3D fully convolutional network-based segmentation of lung nodules in CT images with a clinically inspired data synthesis method

Atsushi Yaguchi, Kota Aoyagi, Akiyuki Tanizawa, Yoshiharu Ohno

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)


In the management of lung nodules, it is important to precisely assess nodule size on computed tomography (CT) images. Given that the malignancy of nodules varies according to their composition, component-wise assessment is useful for diagnosing lung cancer. To improve the accuracy of volumetric measurement of lung nodules, we propose a deep learning-based method for segmenting nodules into multiple components, namely, solid, ground glass opacity (GGO), and cavity. We train a 3D fully convolutional network (FCN) with component-wise dice loss and apply a conditional random field (CRF) to refine the segmentation boundaries. To further gain the accuracy, we artificially generate synthetic cavitary nodules based on clinical observations and then augment the dataset for training the network. In experiments using about 300 CT images of clinical nodules, we evaluated our method in terms of mean absolute percentage error of volumetric measurement. We confirmed that our method achieved 15.84% lower error (averaged over 2 components of solid and GGO) compared with a conventional method based on image processing, and the error for cavity was decreased by 2.87% with our data-synthesis method.

Original languageEnglish
Title of host publicationMedical Imaging 2019
Subtitle of host publicationComputer-Aided Diagnosis
EditorsKensaku Mori, Horst K. Hahn
ISBN (Electronic)9781510625471
Publication statusPublished - 2019
Externally publishedYes
EventMedical Imaging 2019: Computer-Aided Diagnosis - San Diego, United States
Duration: 17-02-201920-02-2019

Publication series

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


ConferenceMedical Imaging 2019: Computer-Aided Diagnosis
Country/TerritoryUnited States
CitySan Diego

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Biomaterials
  • Atomic and Molecular Physics, and Optics
  • Radiology Nuclear Medicine and imaging


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