PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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 ( R HD - D ) 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.
UNASSIGNED: The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], and R HD - D 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.
UNASSIGNED: 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.
UNASSIGNED: 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 ( R HD - D ) 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.
UNASSIGNED: The DSC (median [95% confidence interval]) was 0.91 [0.78, 0.96], and R HD - D 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.
UNASSIGNED: 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.
摘要：
■在超声图像上从周围组织分割卵巢/附件肿块是一项具有挑战性的任务。将质量分离成不同的分量对于放射学特征提取也是重要的。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（1）勾勒出附件肿块的外部边界，（2）分离内部成分。
■对附件肿块的回顾性超声成像数据库进行了审查，以确定患者的排除标准，质量,和图像级别，每个质量一个图像。将53例患者的54个附件肿块（36个良性/18个恶性）按患者分为训练组（26个良性/12个恶性）和独立测试组（10个良性/6个恶性）。使用Dice相似性系数（DSC）和Hausdorff距离与每个质量的轮廓的有效直径（RHD-D）之比，测量了与专家详细轮廓相比的测试图像上的U网分割性能。随后，在发现模式下，使用两级模糊c均值（FCM）无监督聚类方法来分离属于低回声或高回声成分的质量内的像素。
■DSC（中位数[95％置信区间]）为0.91[0.78，0.96]，RHD-D为0.04[0.01，0.12]，表明与专家大纲有很强的一致性。对团块内部分离为回声成分的临床回顾表明，与团块特征密切相关。
■一种用于自动分割附件肿块及其内部组件的U-net和FCM组合算法，与专家概述和审查相比，取得了出色的效果，支持未来基于放射学特征的质量分类。
■对附件肿块的回顾性超声成像数据库进行了审查，以确定患者的排除标准，质量,和图像级别，每个质量一个图像。将53例患者的54个附件肿块（36个良性/18个恶性）按患者分为训练组（26个良性/12个恶性）和独立测试组（10个良性/6个恶性）。使用Dice相似性系数（DSC）和Hausdorff距离与每个质量的轮廓的有效直径（RHD-D）之比，测量了与专家详细轮廓相比的测试图像上的U网分割性能。随后，在发现模式下，使用两级模糊c均值（FCM）无监督聚类方法来分离属于低回声或高回声成分的质量内的像素。
■DSC（中位数[95％置信区间]）为0.91[0.78，0.96]，RHD-D为0.04[0.01，0.12]，表明与专家大纲有很强的一致性。对团块内部分离为回声成分的临床回顾表明，与团块特征密切相关。
■一种用于自动分割附件肿块及其内部组件的U-net和FCM组合算法，与专家概述和审查相比，取得了出色的效果，支持未来基于放射学特征的质量分类。
