Nuchal Translucency Thickness Measurement in Fetal Ultrasound Images to Analyze Down Syndrome

Abstract

Down syndrome (DS) is a chromosomal condition associated with intellectual disability and developmental delay in infants. The most salient pre-natal marker to identify DS during the early stages of gestation is the Nuchal translucency (NT) thickness. Accurate NT measurement from ultrasound (US) images becomes challenging due to the presence of speckle noise, weak edges and other artifacts. This paper proposes a semi-automated approach for early identification and calibration of NT thickness by estimating the distance between the edges of NT. A comparative study is performed on six traditional filters to determine the effective denoising filter for US datasets. This paper also presents an evaluation of two popular segmentation algorithms, the Active Contour and Chan-Vese-based segmentation. The study reveals that Wiener filter outperformed other filters based on the performance metrics and achieved peak signal noise ratio of 42.88 dB and mean squared error of −3.45. The prime feature NT region has been segmented effectively using enhanced double region-based active contour segmentation and obtained Dice Similarity Index (DSI- 84%) and Jaccard Index (76%). This technique has been validated by using Bland Altman’s plot to determine the correlation between clinical and proposed method.

KEYWORDS:

• Active Contour
• Down syndrome
• Fetus
• Nuchal translucency
• Ultrasound

Acknowledgements

Authors are extremely grateful to Dr Suresh, MEDISCAN, Fetal Care Research Foundation, Chennai for providing us with the datasets required for the study; the authors also like to acknowledge Dr Mala Raj, Gynecologist and Dr Gayathri, consultant sonologist, Firm Hospitals for their constant guidance and assistance. Authors would like to acknowledge the SPARC fellowship received by the first author.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Mary Christeena Thomas

Mary Christeena Thomas obtained her Bachelor of Engineering in electronics & instrumentation from Panimalar Engineering College, India in 2015 and Master of Engineering in embedded systems from Sathyabama University, in 2017. Her areas of interest include digital image processing, signal processing, and embedded systems.

Sridhar P. Arjunan

Sridhar P Arjunan received his PhD in electronics and biomedical from RMIT University, Australia. His areas of interest are machine learning, wearable sensors, biomedical instrumentation & signal processing multispectral analysis. Email: [email protected]

Rekha Viswanathan

Rekha Viswanathan received her PhD in electronics and biomedica from RMIT University, Australia. She is currently an early career researcher at RMIT University. Her areas of interest are speech data analysis, machine learning and signal processing. Email: [email protected]