AI-based automated segmentation for ovarian/adnexal masses and their internal components on ultrasound imaging.

IF 1.9 Q3 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Medical Imaging Pub Date : 2024-07-01 Epub Date: 2024-08-06 DOI:10.1117/1.JMI.11.4.044505
Heather M Whitney, Roni Yoeli-Bik, Jacques S Abramowicz, Li Lan, Hui Li, Ryan E Longman, Ernst Lengyel, Maryellen L Giger
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Abstract

Purpose: 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.

Approach: 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.

Results: 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.

Conclusion: 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.

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基于人工智能的超声成像卵巢/附件肿块及其内部组件自动分割。
目的:在超声图像上将卵巢/附件肿块从周围组织中分离出来是一项具有挑战性的任务。将肿块分离成不同的组成部分对于放射学特征提取也很重要。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,该方法可(1)勾勒出附件肿块的外部边界,(2)分离内部成分:方法:对附件包块的回顾性超声成像数据库进行审查,以确定患者、包块和图像层面的排除标准,每个包块一张图像。将 53 名患者的 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)。使用戴斯相似系数(DSC)和豪斯多夫距离与每个肿块轮廓的有效直径之比(R HD - D)来衡量 U 网在测试图像上与专家详细轮廓相比的分割性能。随后,在发现模式下,使用两级模糊均值(FCM)无监督聚类方法将肿块内属于低回声或高回声成分的像素分开:DSC(中位数[95%置信区间])为 0.91 [0.78,0.96],R HD - D 为 0.04 [0.01,0.12],表明与专家轮廓非常一致。对肿块内部回声成分的临床分析表明,肿块内部回声成分与肿块特征密切相关:结论:U-net 和 FCM 算法相结合用于附件肿块及其内部成分的自动分割,与专家轮廓和复查结果相比取得了极佳的效果,支持未来基于放射学特征的肿块成分分类。
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来源期刊
Journal of Medical Imaging
Journal of Medical Imaging RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
4.10
自引率
4.20%
发文量
0
期刊介绍: JMI covers fundamental and translational research, as well as applications, focused on medical imaging, which continue to yield physical and biomedical advancements in the early detection, diagnostics, and therapy of disease as well as in the understanding of normal. The scope of JMI includes: Imaging physics, Tomographic reconstruction algorithms (such as those in CT and MRI), Image processing and deep learning, Computer-aided diagnosis and quantitative image analysis, Visualization and modeling, Picture archiving and communications systems (PACS), Image perception and observer performance, Technology assessment, Ultrasonic imaging, Image-guided procedures, Digital pathology, Biomedical applications of biomedical imaging. JMI allows for the peer-reviewed communication and archiving of scientific developments, translational and clinical applications, reviews, and recommendations for the field.
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