2D image classification for 3D anatomy localization: Employing deep convolutional neural networks

Bob D. De Vos, Jelmer M. Wolterink, Pim A. De Jong, Max A. Viergever, Ivana Išgum

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

51 Citations (Scopus)

Abstract

Localization of anatomical regions of interest (ROIs) is a preprocessing step in many medical image analysis tasks. While trivial for humans, it is complex for automatic methods. Classic machine learning approaches require the challenge of hand crafting features to describe differences between ROIs and background. Deep convolutional neural networks (CNNs) alleviate this by automatically finding hierarchical feature representations from raw images. We employ this trait to detect anatomical ROIs in 2D image slices in order to localize them in 3D. In 100 low-dose non-contrast enhanced non-ECG synchronized screening chest CT scans, a reference standard was defined by manually delineating rectangular bounding boxes around three anatomical ROIs - heart, aortic arch, and descending aorta. Every anatomical ROI was automatically identified using a combination of three CNNs, each analyzing one orthogonal image plane. While single CNNs predicted presence or absence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. Classification performance of each CNN, expressed in area under the receiver operating characteristic curve, was ≥0.988. Additionally, the performance of ROI localization was evaluated. Median Dice scores for automatically determined bounding boxes around the heart, aortic arch, and descending aorta were 0.89, 0.70, and 0.85 respectively. The results demonstrate that accurate automatic 3D localization of anatomical structures by CNN-based 2D image classification is feasible.

Original languageEnglish
Title of host publicationMedical Imaging 2016
Subtitle of host publicationImage Processing
EditorsMartin A. Styner, Elsa D. Angelini, Elsa D. Angelini
PublisherSPIE Press
ISBN (Electronic)9781510600195
DOIs
Publication statusPublished - 2016
Externally publishedYes
EventSPIE Medical Imaging 2016 - San Diego, United States
Duration: 28 Feb 20162 Mar 2016

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume9784
ISSN (Print)1605-7422

Conference

ConferenceSPIE Medical Imaging 2016
CountryUnited States
CitySan Diego
Period28/02/162/03/16

Fingerprint Dive into the research topics of '2D image classification for 3D anatomy localization: Employing deep convolutional neural networks'. Together they form a unique fingerprint.

Cite this