AI精准分割妇科超声中的卵巢与肿块，助力卵巢癌早期诊断
OvAi Focus: AI-based Multi-class Segmentation of Functional Ovaries and Adnexal Masses in Gynecological Ultrasound
- 基于深度学习的多类别语义分割，区分囊性与实性成分
- 在1081例多中心数据上，病变整体分割DICE达0.87
- 适用于临床医生辅助诊断，尤其适合超声图像判读不一致场景
卵巢癌是致死率最高的妇科恶性肿瘤；由于操作者差异和形态复杂性，超声中对附件肿块及功能卵巢的精准、客观分割仍具挑战。本文提出OvAi Focus（SynDiag s.r.l., 意大利），一款独立的AI医疗软件设备，可实现对功能卵巢和附件肿块的多类别语义分割，并区分囊性和实性成分。该系统在来自意大利和以色列6个中心的1,081名成年女性多中心数据集上进行训练与独立验证。分割性能达到：完整病灶DICE为0.87，囊性成分0.85，实性成分0.68，功能卵巢0.62，在异质采集条件下表现与或优于现有最佳方法。
原文摘要 · Abstract (English)
Ovarian cancer is the deadliest gynecological malignancy; accurate and objective segmentation of adnexal masses and functional ovaries in ultrasound (US) remains challenging due to operator variability and morphological complexity. We present OvAi Focus (SynDiag s.r.l., Italy), a stand-alone AI software medical device that performs multi-class semantic segmentation of functional ovaries and adnexal masses, distinguishing cystic from solid components. The system was trained and independently validated on a multicenter dataset of 1,081 adult women from 6 centers across Italy and Israel. Segmentation achieved DICE scores of 0.87 (complete lesion), 0.85 (cystic), 0.68 (solid), and 0.62 (functional ovary), in line with or superior to state-of-the-art approaches across heterogeneous acquisition settings.
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