Medical & Biological Engineering & Computing

, Volume 57, Issue 2, pp 543–564 | Cite as

Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk

  • Mainak Biswas
  • Venkatanareshbabu Kuppili
  • Luca Saba
  • Damodar Reddy Edla
  • Harman S. Suri
  • Aditya Sharma
  • Elisa Cuadrado-Godia
  • John R. Laird
  • Andrew Nicolaides
  • Jasjit S. SuriEmail author
Original Article


Manual ultrasound (US)-based methods are adapted for lumen diameter (LD) measurement to estimate the risk of stroke but they are tedious, error prone, and subjective causing variability. We propose an automated deep learning (DL)-based system for lumen detection. The system consists of a combination of two DL systems: encoder and decoder for lumen segmentation. The encoder employs a 13-layer convolution neural network model (CNN) for rich feature extraction. The decoder employs three up-sample layers of fully convolution network (FCN) for lumen segmentation. Three sets of manual tracings were used during the training paradigm leading to the design of three DL systems. Cross-validation protocol was implemented for all three DL systems. Using the polyline distance metric, the precision of merit for three DL systems over 407 US scans was 99.61%, 97.75%, and 99.89%, respectively. The Jaccard index and Dice similarity of DL lumen segmented region against three ground truth (GT) regions were 0.94, 0.94, and 0.93 and 0.97, 0.97, and 0.97, respectively. The corresponding AUC for three DL systems was 0.95, 0.91, and 0.93. The experimental results demonstrated superior performance of proposed deep learning system over conventional methods in literature.

Graphical abstract


Stroke Ultrasound Carotid Lumen diameter Deep learning CNN Performance 



The authors at the National Institute of Technology, Goa, India, would like to acknowledge MediaLab Asia, Ministry of Electronics and Information Technology, and the Government of India for their kind support.

Compliance with ethical standards

The ethics approval was granted by Toho University IRB, Japan. Informed consent was obtained from all the patients.

Supplementary material


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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  • Mainak Biswas
    • 1
  • Venkatanareshbabu Kuppili
    • 1
  • Luca Saba
    • 2
  • Damodar Reddy Edla
    • 1
  • Harman S. Suri
    • 3
    • 4
  • Aditya Sharma
    • 5
  • Elisa Cuadrado-Godia
    • 6
  • John R. Laird
    • 7
  • Andrew Nicolaides
    • 8
    • 9
  • Jasjit S. Suri
    • 4
    Email author
  1. 1.Department of Computer Science and EngineeringNIT GoaPondaIndia
  2. 2.Department of RadiologyA.O.U. CagliariCagliariItaly
  3. 3.Brown UniversityProvidenceUSA
  4. 4.Monitoring and Diagnostic DivisionAtheroPoint™RosevilleUSA
  5. 5.Cardiovascular DivisionUniversity of VirginiaCharlottesvilleUSA
  6. 6.Dept. of NeurologyIMIM - Hospital del MarBarcelonaSpain
  7. 7.Helena HospitalSt. HelenaUSA
  8. 8.Vascular Screening and Diagnostic CentreLondonUK
  9. 9.Department of Biological SciencesUniversity of CyprusNicosiaCyprus

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