基于机器学习方法,根据超声波数据评估卵巢卵泡储备功能

Fedor A. Laputin, Ivan V. Sidorov, Andrey S. Moshkin
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引用次数: 0

摘要

背景:卵巢储备功能反映了女性成功实现生育功能的能力。卵巢储备功能的评估是临床实践中的一项紧迫任务[1],在科学研究中也具有重要意义。计算机诊断图像处理方法的使用可加快和促进临床实践中常规任务的执行。在以科学为目的的回顾性数据分析中使用这些方法,可以提高研究的客观性并补充辅助信息[2]。关于超声图像上的卵巢定位和卵泡分割问题,此前已有其他研究。例如,Z. Chen 等人[3] 采用 U-net 模型在超声图像上识别卵泡。同样,V.K. Singh 等人[4]使用 U-net 的一个变体,即 UNet++ [5],解决了一个相关问题。目的:本研究旨在开发机器学习模型,用于分析从超声波机获得的卵巢图像。材料与方法:使用带有标记卵巢区域的开放数据集对卵巢分割和卵泡检测模型进行预训练。随后,使用包含标记卵巢和卵泡区域的数据集进行训练和测试。该数据集共包含来自 50 名患者的约 800 个实例。在超声图像中定位卵泡是一项具有挑战性的任务。为此,设计的检测系统分为两部分:卵巢分割和选定区域内的卵泡检测。这种方法可使模型专注于没有其他器官和各种超声伪影的区域,这些伪影可能会被误认为是调查对象。为了进行卵巢分割,采用了 UNet++ 架构[5]和 ResNeSt 编码器[8],其中包含 SE-Net [9] 和 SK-Net [10] 注意机制。对象检测模型用于识别卵巢内卵泡的位置,因为即使在结构重叠的情况下,它也能精确枚举卵泡的数量,而这正是分割模型所缺乏的能力。在我们的研究中,我们使用了 YOLOv8 模型[11]。此外,我们还采用了数据预处理来提高模型预测的质量。这包括识别和移除带有辅助信息的区域、减少噪音和增加数据。结果:根据这项研究的结果,提出了两个卵巢定位模型。第一个模型是一个分割模型,IoU 质量至少为 50%。第二个模型是一个检测模型,mAP 质量至少达到 65%。第三个模型是卵泡检测模型,随后进行卵泡计数。该模型的 MAPE 误差不超过 35%。结论:这项研究提出了一种将机器学习技术应用于超声图像分析任务的方法。开发的分割和检测模型减少了分析图像中卵巢和卵泡的时间和误差。注意力机制和数据预处理的使用提高了模型的质量。用于卵泡检测的神经网络即使在卵泡重叠的情况下也能进行卵泡计数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Assessment of ovarian follicular reserve according to ultrasound data based on machine learning methods
BACKGROUND: Ovarian reserve reflects a woman's ability to successfully realize reproductive function. The assessment of ovarian reserve is an urgent task for clinical practice [1] and is important in scientific research. The use of computerized diagnostic image processing methods can accelerate and facilitate the performance of routine tasks in clinical practice. Their use in retrospective data analysis for scientific purposes allows to increase the objectivity of the study and supplement it with auxiliary information [2]. The issue of ovarian localization and follicle segmentation on ultrasound images has been previously investigated in other works. For instance, Z. Chen et al. [3] employed the U-net model to identify follicles on ultrasound images. Similarly, V.K. Singh et al. [4] addressed a related problem using a variant of U-net, namely UNet++ [5], which has gained considerable traction in the field of medical image analysis [6]. AIM: The study aimed to develop machine learning models for analyzing ovarian images obtained from an ultrasound machine. MATERIALS AND METHODS: An open dataset with a labeled ovary region was used for pre-training ovarian segmentation and follicle detection models. Subsequently, the dataset, which contains marked-up ovarian and follicle regions, was employed for training and testing. It encompasses a total of approximately 800 examples from 50 unique patients. The localization of follicles in an ultrasound image is a challenging task. To address this, the designed detector system was divided into two parts: ovary segmentation and follicle detection within the selected region. This approach allows the model to focus on a region where there are no other organs and various ultrasound artifacts that can be falsely perceived as the object under investigation. For the purpose of ovarian segmentation, the UNet++ architecture [5] was employed in conjunction with the ResNeSt encoder [8], which incorporates the SE-Net [9] and SK-Net [10] attention mechanisms. The object detection model is employed to identify the location of follicles within the ovary, as it enables precise enumeration of the number of follicles, even in the presence of overlapping structures, a capability that the segmentation model lacks. In our study, we used the YOLOv8 model [11]. Furthermore, data preprocessing has been employed to enhance the quality of model predictions. This has involved the identification and removal of regions with auxiliary information, the reduction of noise, and the augmentation of data. RESULTS: Two ovarian localization models are presented based on the results of this study. The first model is a segmentation model with an IoU quality of at least 50%. The second model is a detection model with a mAP quality of at least 65%. A third model is a model for follicle detection with subsequent follicle counting. This model has an MAPE error not exceeding 35%. CONCLUSIONS: The study resulted in the proposal of a method for applying machine learning techniques to the task of analyzing ultrasound images. The developed segmentation and detection models reduce the time and errors in analyzing ovaries and follicles in the images. The use of an attention mechanism and data preprocessing improves the quality of the models. The neural network for follicle detection provides follicle counting, even when follicles overlap.
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