Deep learning-based pneumothorax detection in ultrasound videos

Courosh Mehanian, Sourabh Kulhare, Rachel Millin, Xinliang Zheng, Cynthia Gregory, Meihua Zhu, Hua Xie, James Jones, Jack Lazar, Amber Halse, Todd Graham, Mike Stone, Kenton Gregory, Ben Wilson

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

Abstract

Pneumothorax (PTX) is a medical and surgical emergency that can lead to hemodynamic instability and life-threatening collapse of the lung. PTX is usually detected using chest X-ray but can be detected using lung ultrasound, which requires interpretation by an expert radiologist. We are developing an AI based algorithm for the automated interpretation of lung ultrasound video to enable fast diagnosis of pneumothorax at the point of care by health care providers without extensive training in ultrasound. In this work, we developed and compared several deep learning methods for identifying pneumothoraces in 3-s ultrasound videos collected with a handheld ultrasound system. The first group of methods were based on convolutional neural networks (CNNs) paired with time-mapping preprocessing algorithms, including reconstructed M-mode and the proposed simplified optical flow transform (SOFT). These preprocessing methods were either used alone or in combination in a single “fusion” CNN. The second class of algorithm used a Deep Learning architecture that combines a CNN for processing spatial information (Inception V3) with a recurrent network (long-short-term-memory, or LSTM) for temporal analysis, enabling raw video to be fed directly into the neural network without preprocessing. We used data from a swine pneumothorax model to train and test the proposed algorithms, comparing their performance. Despite limited data, all algorithms achieved an AUC for pneumothorax detection greater than 0.83.

Original languageEnglish (US)
Title of host publicationSmart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis - 1st International Workshop, SUSI 2019, and 4th International Workshop, PIPPI 2019, Held in Conjunction with MICCAI 2019, Proceedings
EditorsQian Wang, Alberto Gomez, Jana Hutter, Alberto Gomez, Veronika Zimmer, Jana Hutter, Emma Robinson, Daan Christiaens, Andrew Melbourne, Kristin McLeod, Oliver Zettinig, Roxane Licandro, Esra Abaci Turk
PublisherSpringer
Pages74-82
Number of pages9
ISBN (Print)9783030328740
DOIs
StatePublished - 2019
Event1st International Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019 - Shenzhen, China
Duration: Oct 17 2019Oct 17 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11798 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference1st International Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019
CountryChina
CityShenzhen
Period10/17/1910/17/19

Keywords

  • Deep Learning
  • Lung ultrasound
  • Pneumothorax

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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