Characterization of lung nodule malignancy using hybrid shape and appearance features

Mario Buty, Ziyue Xu*, Mingchen Gao, Ulas Bagci, Aaron Wu, Daniel J. Mollura

*Corresponding author for this work

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

44 Scopus citations

Abstract

Computed tomography imaging is a standard modality for detecting and assessing lung cancer. In order to evaluate the malignancy of lung nodules,clinical practice often involves expert qualitative ratings on several criteria describing a nodule’s appearance and shape. Translating these features for computer-aided diagnostics is challenging due to their subjective nature and the difficulties in gaining a complete description. In this paper,we propose a computerized approach to quantitatively evaluate both appearance distinctions and 3D surface variations. Nodule shape was modeled and parameterized using spherical harmonics,and appearance features were extracted using deep convolutional neural networks. Both sets of features were combined to estimate the nodule malignancy using a random forest classifier. The proposed algorithm was tested on the publicly available Lung Image Database Consortium dataset,achieving high accuracy. By providing lung nodule characterization,this method can provide a robust alternative reference opinion for lung cancer diagnosis.

Original languageEnglish (US)
Title of host publicationMedical Image Computing and Computer-Assisted Intervention - MICCAI 2016 - 19th International Conference, Proceedings
EditorsSebastian Ourselin, Leo Joskowicz, Mert R. Sabuncu, William Wells, Gozde Unal
PublisherSpringer Verlag
Pages662-670
Number of pages9
ISBN (Print)9783319467191
DOIs
StatePublished - 2016
Externally publishedYes
Event1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016 - Athens, Greece
Duration: Oct 21 2016Oct 21 2016

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9900 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Simulation and Synthesis in Medical Imaging, SASHIMI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016
Country/TerritoryGreece
CityAthens
Period10/21/1610/21/16

Keywords

  • Conformal mapping
  • Deep convolutional neural network
  • Nodule characterization
  • Random forest
  • Spherical harmonics

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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