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DeepAAA: Clinically Applicable and Generalizable Detection of Abdominal Aortic Aneurysm Using Deep Learning

  • Jen-Tang Lu
  • Rupert Brooks
  • Stefan Hahn
  • Jin Chen
  • Varun BuchEmail author
  • Gopal Kotecha
  • Katherine P. Andriole
  • Brian Ghoshhajra
  • Joel Pinto
  • Paul Vozila
  • Mark Michalski
  • Neil A. Tenenholtz
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11765)

Abstract

We propose a deep learning-based technique for detection and quantification of abdominal aortic aneurysms (AAAs). The condition, which leads to more than 10,000 deaths per year in the United States, is asymptomatic, often detected incidentally, and often missed by radiologists. Our model architecture is a modified 3D U-Net combined with ellipse fitting that performs aorta segmentation and AAA detection. The study uses 321 abdominal-pelvic CT examinations performed by Massachusetts General Hospital Department of Radiology for training and validation. The model is then further tested for generalizability on a separate set of 57 examinations with differing patient demographics and acquisition characteristics than the original dataset. DeepAAA achieves high performance on both sets of data (sensitivity/specificity 0.91/0.95 and 0.85/1.0 respectively), on contrast and non-contrast CT scans and works with image volumes with varying numbers of images. We find that DeepAAA exceeds literature-reported performance of radiologists on incidental AAA detection. It is expected that the model can serve as an effective background detector in routine CT examinations to prevent incidental AAAs from being missed.

Keywords

Segmentation Aorta Aneurysm Deep learning U-Net 

Supplementary material

490275_1_En_80_MOESM1_ESM.pdf (125 kb)
Supplementary material 1 (pdf 125 KB)

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.MGH and BWH Center for Clinical Data ScienceBostonUSA
  2. 2.Massachusetts General Hospital (MGH)BostonUSA
  3. 3.Brigham and Women’s Hospital (BWH)BostonUSA
  4. 4.Nuance Communications Inc.BurlingtonUSA

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