Deep learning fully convolution network for lumen characterization in diabetic patients using carotid ultrasound: a tool for stroke risk
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.
KeywordsStroke 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.
- 1.Ward H et al (2012) Oxford handbook of epidemiology for clinicians. OUP OxfordGoogle Scholar
- 4.“What is a stroke?”. www.nhlbi.nih.gov/health/health-topics/topics/stroke. June 22, (2016)
- 7.Tell GS, Polak JF, Ward BJ, Kittner SJ, Savage PJ, Robbins J (1994) Relation of smoking with carotid artery wall thickness and stenosis in older adults. The Cardiovascular Health Study. The Cardiovascular Health Study (CHS) Collaborative Research Group. Circulation 90(6):2905–2908CrossRefGoogle Scholar
- 8.Polak JF, O'Leary DH, Kronmal RA, Wolfson SK, Bond MG, Tracy RP, Gardin JM, Kittner SJ, Price TR, Savage PJ (1993) Sonographic evaluation of carotid artery atherosclerosis in the elderly: relationship of disease severity to stroke and transient ischemic attack. Radiology 188(2):363–370CrossRefGoogle Scholar
- 9.Nicolaides AN, Kakkos SK, Kyriacou E, Griffin M, Sabetai M, Thomas DJ, Tegos T, Geroulakos G, Labropoulos N, Doré CJ, Morris TP, Naylor R, Abbott AL, Asymptomatic Carotid Stenosis and Risk of Stroke (ACSRS) Study Group (2010) Asymptomatic internal carotid artery stenosis and cerebrovascular risk stratification. J Vasc Surg 52(6):1486–1496CrossRefGoogle Scholar
- 16.Mehra S (2010) Role of duplex Doppler sonography in arterial stenoses. Journal Indian Academy of Clinical Medicine 11(4):294–299Google Scholar
- 25.Suri JS, Laxminarayan S (2002) PDE and level sets. Springer Science & Business MediaGoogle Scholar
- 27.Kumar PK, Araki T, Rajan J, Saba L, Lavra F, Ikeda N, Sharma AM et al (2017) Accurate lumen diameter measurement in curved vessels in carotid ultrasound: an iterative scale-space and spatial transformation approach. Med Biol Eng Comput:1–20Google Scholar
- 30.Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. Proc IEEE Conf Comput Vis Pattern RecognitGoogle Scholar
- 31.Teichmann M et al (2016) MultiNet: real-time joint semantic reasoning for autonomous driving. arXiv preprint arXiv:1612.07695Google Scholar
- 34.Ciresan D et al (2012) Deep neural networks segment neuronal membranes in electron microscopy images. Advances in neural information processing systemsGoogle Scholar
- 35.Bar Y et al (2015) Chest pathology detection using deep learning with non-medical training. Biomedical Imaging (ISBI), 2015 IEEE 12th International Symposium on. IEEEGoogle Scholar
- 36.Simonyan K and Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556Google Scholar
- 39.García-Zapirain B, Elmogy M, El-Baz A, and Elmaghraby AS (2017) Classification of pressure ulcer tissues with 3D convolutional neural network. Med Biol Eng Comput 1–14Google Scholar
- 41.Molinari F, Krishnamurthi G, Rajendra Acharya U, Vinitha Sree S, Zeng G, Saba L, Nicolaides A, Suri JS (2012) Hypothesis validation of far-wall brightness in carotid-artery ultrasound for feature-based IMT measurement using a combination of level-set segmentation and registration. IEEE Trans Instrum Meas 61(4):1054–1063CrossRefGoogle Scholar
- 42.Gutierrez MA, Pilon PE, Lage SG, Kopel L, Carvalho RT and Furuie SS (2002) Automatic measurement of carotid diameter and wall thickness in ultrasound images. Comput Cardiol 359–362Google Scholar
- 43.Sahani AK, Joseph J and Sivaprakasam M (2013) Automatic measurement of lumen diameter of carotid artery in A-mode ultrasound. In Engineering in Medicine and Biology Society (EMBC), 2013 35th Annual International Conference of the IEEE, pp 3873–3876Google Scholar
- 44.Saba L, Araki T, Krishna Kumar P, Rajan J, Lavra F, Ikeda N, Sharma AM, Shafique S, Nicolaides A, Laird JR, Gupta A (2016) Carotid inter-adventitial diameter is more strongly related to plaque score than lumen diameter: an automated tool for stroke analysis. J Clin Ultrasound 44(4):210–220CrossRefGoogle Scholar