OvAi Focus: A framework for multiclass segmentation and morphological features extraction in gynecological ultrasound

Ovarian cancer is recognized as the deadliest gynecological malignancy. Diagnosis at advanced stages and the lack of effective screening program lead to poor survival rates, dropping to 17-39 % in stage III-IV diseases. Ultrasound (US) is the primary imaging modality for ovarian structures evaluation, but it is strongly affected by the operator expertise due to the complexity of adnexal masses and the physiological variability of ovarian morphology throughout a womans lifecycle. The International Ovarian Tumor Analysis (IOTA) group introduced definitions and predictive tools to standardize gynecological US interpretation. However, these tools still rely on subjective interpretation, thus highlighting the need for more objective solutions. Recent studies have explored artificial intelligence (AI) algorithms for gynecological US, mainly focusing on adnexal masses classification. Conversely, a robust solution supporting the identification and description of healthy and tumoral ovarian structures is still lacking. This paper proposes OvAi Focus, a framework including (i) a segmentation module for the identification of healthy ovaries, plus solid and cystic components of adnexal masses; (ii) a morphology module for the extraction of IOTA-based keywords to describe adnexal masses morphology. Segmentation module results were compared to ground truth masks, showing DICE scores from 0.62 for functional ovary to 0.87 for the whole adnexal mass. Morphology module was tested through interobserver agreement analysis, obtaining Fleiss Kappa from 0.16 to 0.57 and Percent Agreement from 47 to 90 %, in line with existing literature. OvAi Focus represents an innovative solution which could help overcoming subjectivity in gynecological US imaging interpretation.