Whitney Heather M, Yoeli-Bik Roni, Abramowicz Jacques S, Lan Li, Li Hui, Longman Ryan E, Lengyel Ernst, Giger Maryellen L
The University of Chicago, Department of Radiology, Chicago, Illinois, United States.
The University of Chicago, Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, Chicago, Illinois, United States.
J Med Imaging (Bellingham). 2024 Jul;11(4):044505. doi: 10.1117/1.JMI.11.4.044505. Epub 2024 Aug 6.
Segmentation of ovarian/adnexal masses from surrounding tissue on ultrasound images is a challenging task. The separation of masses into different components may also be important for radiomic feature extraction. Our study aimed to develop an artificial intelligence-based automatic segmentation method for transvaginal ultrasound images that (1) outlines the exterior boundary of adnexal masses and (2) separates internal components.
A retrospective ultrasound imaging database of adnexal masses was reviewed for exclusion criteria at the patient, mass, and image levels, with one image per mass. The resulting 54 adnexal masses (36 benign/18 malignant) from 53 patients were separated by patient into training (26 benign/12 malignant) and independent test (10 benign/6 malignant) sets. U-net segmentation performance on test images compared to expert detailed outlines was measured using the Dice similarity coefficient (DSC) and the ratio of the Hausdorff distance to the effective diameter of the outline ( ) for each mass. Subsequently, in discovery mode, a two-level fuzzy c-means (FCM) unsupervised clustering approach was used to separate the pixels within masses belonging to hypoechoic or hyperechoic components.
The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], and was 0.04 [0.01, 0.12], indicating strong agreement with expert outlines. Clinical review of the internal separation of masses into echogenic components demonstrated a strong association with mass characteristics.
A combined U-net and FCM algorithm for automatic segmentation of adnexal masses and their internal components achieved excellent results compared with expert outlines and review, supporting future radiomic feature-based classification of the masses by components.
在超声图像上从周围组织中分割出卵巢/附件肿块是一项具有挑战性的任务。将肿块分离为不同成分对于提取放射组学特征可能也很重要。我们的研究旨在开发一种基于人工智能的经阴道超声图像自动分割方法，该方法能够（1）勾勒出附件肿块的外部边界，以及（2）分离内部成分。
对一个附件肿块的回顾性超声成像数据库进行审查，以确定患者、肿块和图像层面的排除标准，每个肿块选取一张图像。从53名患者中得到的54个附件肿块（36个良性/18个恶性）按患者分为训练集（26个良性/12个恶性）和独立测试集（10个良性/6个恶性）。使用Dice相似系数（DSC）以及每个肿块的豪斯多夫距离与轮廓有效直径的比值（ ）来衡量测试图像上U-net分割性能与专家详细轮廓的对比情况。随后，在发现模式下，采用两级模糊c均值（FCM）无监督聚类方法来分离肿块内属于低回声或高回声成分的像素。
DSC（中位数[95%置信区间]）为0.91[0.78, 0.96]， 为0.04[0.01, 0.12]，表明与专家轮廓高度一致。对肿块内部回声成分分离情况的临床评估显示与肿块特征密切相关。
与专家轮廓和评估相比，用于自动分割附件肿块及其内部成分的U-net和FCM组合算法取得了优异结果，为未来基于放射组学特征的肿块成分分类提供了支持。
