TY - GEN
T1 - Ultrasound-based detection of lung abnormalities using single shot detection convolutional neural networks
AU - Kulhare, Sourabh
AU - Zheng, Xinliang
AU - Mehanian, Courosh
AU - Gregory, Cynthia
AU - Zhu, Meihua
AU - Gregory, Kenton
AU - Xie, Hua
AU - McAndrew Jones, James
AU - Wilson, Benjamin
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2018.
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Convolutional neural networks
KW - Deep learning
KW - Lung ultrasound
UR - http://www.scopus.com/inward/record.url?scp=85054392404&partnerID=8YFLogxK
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U2 - 10.1007/978-3-030-01045-4_8
DO - 10.1007/978-3-030-01045-4_8
M3 - Conference contribution
AN - SCOPUS:85054392404
SN - 9783030010447
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 65
EP - 73
BT - 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
A2 - Aylward, Stephen
A2 - Simpson, Amber
A2 - Maier-Hein, Lena
A2 - Martel, Anne
A2 - Chabanas, Matthieu
A2 - Tavares, João Manuel
A2 - Reinertsen, Ingerid
A2 - Taylor, Zeike
A2 - Xiao, Yiming
A2 - Farahani, Keyvan
A2 - Stoyanov, Danail
A2 - Li, Shuo
A2 - Rivaz, Hassan
PB - Springer-Verlag
T2 - International 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
Y2 - 16 September 2018 through 20 September 2018
ER -