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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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

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