Automatic Identification of 
Miscarriage Cases Supported by
Decision Strength Using Ultrasound 
Images of the Gestational Sac

Khazendar, Shan; Farren, Jessica; Al-Assam, Hisham; Du, Hongbo; Sayasneh, Ahmed; Bourne, Tom; Jassim, Sabah A.

Automatic Identification of Miscarriage Cases Supported by Decision Strength Using Ultrasound Images of the Gestational Sac

Khazendar, Shan and Farren, Jessica and Al-Assam, Hisham and Du, Hongbo and Sayasneh, Ahmed and Bourne, Tom and Jassim, Sabah A. (2015) Automatic Identification of Miscarriage Cases Supported by Decision Strength Using Ultrasound Images of the Gestational Sac. Annals of the BMVA, 2015 (5). pp. 1-16.

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Official URL: http://www.bmva.org/annals/2015/2015-0005.pdf

Abstract

Ultrasound imaging is one of the most widely used multipurpose imaging
modalities for monitoring and diagnosing early pregnancy events. The first
sign and measurable element of an early pregnancy is the appearance of the
Gestational Sac (GS). Currently, the size of the GS is manually estimated from
ultrasound images. The manual measurements tend to result in inter- and intraobserver variations, which may lead to difficulties in diagnosis. This paper
proposes a new method for automatic identification of miscarriage cases in the
first trimester of pregnancy. The proposed method automatically segments the
GS and calculates the Mean Sac Diameter (MSD) and other geometric features
of the segmented sac. After classifying the image based on the extracted
features into either a pregnancy of unknown viability (PUV) or a possible
miscarriage case, we assign the decision with a strength level to reflect its
reliability. The paper argues that the level of decision strength gives more
insight into decision making than other classical alternatives and makes the
automated decision process closer to the diagnosis practice by expert

Item Type:	Article
Uncontrolled Keywords:	Medical imaging techniques; miscarriage; ultrasound imaging
Subjects:	Q Science > Q Science (General) Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General)
Divisions:	School of Computing
Depositing User:	Hisham Al Assam
Date Deposited:	26 May 2020 10:00
Last Modified:	26 May 2020 10:00
URI:	https://bear.buckingham.ac.uk/id/eprint/454

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