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A New 2.5D Representation for Lymph Node Detection Using Random Sets of Deep Convolutional Neural Network Observations

  • Holger R. Roth
  • Le Lu
  • Ari Seff
  • Kevin M. Cherry
  • Joanne Hoffman
  • Shijun Wang
  • Jiamin Liu
  • Evrim Turkbey
  • Ronald M. Summers
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8673)

Abstract

Automated Lymph Node (LN) detection is an important clinical diagnostic task but very challenging due to the low contrast of surrounding structures in Computed Tomography (CT) and to their varying sizes, poses, shapes and sparsely distributed locations. State-of-the-art studies show the performance range of 52.9% sensitivity at 3.1 false-positives per volume (FP/vol.), or 60.9% at 6.1 FP/vol. for mediastinal LN, by one-shot boosting on 3D HAAR features. In this paper, we first operate a preliminary candidate generation stage, towards ~100% sensitivity at the cost of high FP levels (~40 per patient), to harvest volumes of interest (VOI). Our 2.5D approach consequently decomposes any 3D VOI by resampling 2D reformatted orthogonal views N times, via scale, random translations, and rotations with respect to the VOI centroid coordinates. These random views are then used to train a deep Convolutional Neural Network (CNN) classifier. In testing, the CNN is employed to assign LN probabilities for all N random views that can be simply averaged (as a set) to compute the final classification probability per VOI. We validate the approach on two datasets: 90 CT volumes with 388 mediastinal LNs and 86 patients with 595 abdominal LNs. We achieve sensitivities of 70%/83% at 3 FP/vol. and 84%/90% at 6 FP/vol. in mediastinum and abdomen respectively, which drastically improves over the previous state-of-the-art work.

Keywords

Image Patch Convolutional Neural Network Lymph Node Candidate Deep Convolutional Neural Network Convolutional Neural Network Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Holger R. Roth
    • 1
  • Le Lu
    • 1
  • Ari Seff
    • 1
  • Kevin M. Cherry
    • 1
  • Joanne Hoffman
    • 1
  • Shijun Wang
    • 1
  • Jiamin Liu
    • 1
  • Evrim Turkbey
    • 1
  • Ronald M. Summers
    • 1
  1. 1.Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging SciencesNational Institutes of Health Clinical CenterBethesdaUSA

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