An automated technique for potential differentiation of ovarian mature teratomas from other benign tumours using neural networks classification of 2D ultrasound static images: a pilot study

Abstract

Ovarian cysts are a common pathology in women of all age groups. It is estimated that 5-10% of women have a surgical intervention to remove an ovarian cyst in their lifetime. Given this frequency rate, characterization of ovarian masses is essential for optimal management of patients. Patients with benign ovarian masses can be managed conservatively if they are asymptomatic. Mature teratomas are common benign ovarian cysts that occur, in most cases, in premenopausal women. These ovarian cysts can contain different types of human tissue including bone, cartilage, fat, hair, or other tissue. If they are causing no symptoms, they can be harmless and may not require surgery. Subjective assessment by ultrasound examiners has a high diagnostic accuracy when characterising mature teratomas from other types of tumours. The aim of this study is to develop a computerised technique with the potential to characterise mature teratomas and distinguish them from other types of benign ovarian tumours. Local Binary Pattern (LBP) was applied to extract texture features that are specific in distinguishing teratomas. Neural Networks (NN) was then used as a classifier for recognising mature teratomas. A pilot sample set of 130 B-mode static ovarian ultrasound images (41 mature teratomas tumours and 89 other types of benign tumours) was used to test the effectiveness of the proposed technique. Test results show an average accuracy rate of 99.4% with a sensitivity of 100%, specificity of 98.8% and positive predictive value of 98.9%. This study demonstrates that the NN and LBP techniques can accurately classify static 2D B-mode ultrasound images of benign ovarian masses into mature teratomas and other types of benign tumours.