Atlas‐based segmentation is a high level segmentation technique which has become a standard paradigm for exploiting prior knowledge in image segmentation. Recent multiatlas‐based methods have provided greatly accurate segmentations of different parts of the human body by propagating manual delineations from multiple atlases in a data set to a query subject and fusing them. The female pelvic region is known to be of high variability which makes the segmentation task difficult. We propose, here, an approach for the segmentation of magnetic resonance imaging (MRI) called multiatlas‐based segmentation using online machine learning (OML). The proposed approach allows separating regions which may be affected by cervical cancer in a female pelvic MRI. The suggested approach is based on an online learning method for the construction of the dataset of atlases. The experiments demonstrate the higher accuracy of the suggested approach compared to a segmentation technique based on a fixed dataset of atlases and single‐atlas‐based segmentation technique.

中文翻译：

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基于多图谱的女性盆腔器官分割：在宫颈癌的计算机辅助诊断中的应用

基于图集的分割是一种高级分割技术，已成为利用图像分割中先验知识的标准范例。最近的基于多图集的方法通过将数据集中的多个图集的手动描述传播到查询主题并将其融合，从而提供了人体不同部位的非常精确的分割。已知女性骨盆区域具有高可变性，这使得分割任务变得困难。我们在这里提出一种使用在线机器学习（OML）的基于多图谱的分割磁共振成像（MRI）的方法。所提出的方法允许在女性骨盆MRI中分离可能受宫颈癌影响的区域。建议的方法基于在线学习方法来构建地图集的数据集。与基于固定地图集和基于单图集的分割技术的分割技术相比，实验证明了该方法的准确性更高。