Ultrasound-based detection of lung abnormalities using single shot detection convolutional neural networks

Sourabh Kulhare, Xinliang Zheng, Courosh Mehanian, Cynthia Gregory, Meihua Zhu, Kenton Gregory, Hua Xie, James McAndrew Jones, Benjamin Wilson

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

3 Scopus citations

Abstract

Ultrasound imaging can be used to identify a variety of lung pathologies, including pneumonia, pneumothorax, pleural effusion, and acute respiratory distress syndrome (ARDS). Ultrasound lung images of sufficient quality are relatively easy to acquire, but can be difficult to interpret as the relevant features are mostly non-structural and require expert interpretation. In this work, we developed a convolutional neural network (CNN) algorithm to identify five key lung features linked to pathological lung conditions: B-lines, merged B-lines, lack of lung sliding, consolidation and pleural effusion. The algorithm was trained using short ultrasound videos of in vivo swine models with carefully controlled lung conditions. Key lung features were annotated by expert radiologists and snonographers. Pneumothorax (absence of lung sliding) was detected with an Inception V3 CNN using simulated M-mode images. A single shot detection (SSD) framework was used to detect the remaining features. Our results indicate that deep learning algorithms can successfully detect lung abnormalities in ultrasound imagery. Computer-assisted ultrasound interpretation can place expert-level diagnostic accuracy in the hands of low-resource health care providers.

Original languageEnglish (US)
Title of host publicationSimulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation - International Workshops, POCUS 2018, BIVPCS 2018, CuRIOUS 2018, and CPM 2018, Held in Conjunction with MICCAI 2018, Proceedings
EditorsStephen Aylward, Amber Simpson, Lena Maier-Hein, Anne Martel, Matthieu Chabanas, João Manuel Tavares, Ingerid Reinertsen, Zeike Taylor, Yiming Xiao, Keyvan Farahani, Danail Stoyanov, Shuo Li, Hassan Rivaz
PublisherSpringer Verlag
Pages65-73
Number of pages9
ISBN (Print)9783030010447
DOIs
StatePublished - Jan 1 2018
EventInternational Workshop on Point-of-Care Ultrasound, POCUS 2018, the International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017, the International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2018, and the International Workshop on Computational Precision Medicine, CPM 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018 - Granada, Spain
Duration: Sep 16 2018Sep 20 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11042 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

OtherInternational Workshop on Point-of-Care Ultrasound, POCUS 2018, the International Workshop on Bio-Imaging and Visualization for Patient-Customized Simulations, BIVPCS 2017, the International Workshop on Correction of Brainshift with Intra-Operative Ultrasound, CuRIOUS 2018, and the International Workshop on Computational Precision Medicine, CPM 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018
CountrySpain
CityGranada
Period9/16/189/20/18

Keywords

  • Convolutional neural networks
  • Deep learning
  • Lung ultrasound

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'Ultrasound-based detection of lung abnormalities using single shot detection convolutional neural networks'. Together they form a unique fingerprint.

  • Cite this

    Kulhare, S., Zheng, X., Mehanian, C., Gregory, C., Zhu, M., Gregory, K., Xie, H., McAndrew Jones, J., & Wilson, B. (2018). Ultrasound-based detection of lung abnormalities using single shot detection convolutional neural networks. In S. Aylward, A. Simpson, L. Maier-Hein, A. Martel, M. Chabanas, J. M. Tavares, I. Reinertsen, Z. Taylor, Y. Xiao, K. Farahani, D. Stoyanov, S. Li, & H. Rivaz (Eds.), Simulation, Image Processing, and Ultrasound Systems for Assisted Diagnosis and Navigation - International Workshops, POCUS 2018, BIVPCS 2018, CuRIOUS 2018, and CPM 2018, Held in Conjunction with MICCAI 2018, Proceedings (pp. 65-73). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11042 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-030-01045-4_8