Medical image slice interpolation is an active field of research. The methods for this task can be categorized into two broad groups: intensity-based and object-based interpolation methods. While intensity-based methods are generally easier to perform and less computationally expensive, object-based methods are capable of producing more accurate results and account for deformable changes in the objects within the slices. In this paper, performance of two well-known object-based interpolation methods is analyzed and compared. Here, a deformable registration-based method specifically designed for medical applications and a learning-based method, trained for video frame interpolation, are considered. While the deformable registration-based technique is capable of accurate modeling of the changes in the shapes of the objects within slices, the learning-based method is able to produce results with similar accuracy, but with a much sharper appearance in a fraction of the time. This is despite the fact that the learning-based approach is not trained on medical images and rather is trained using regular video footage. However, experiments show that the method is capable of accurate slice interpolation results.

中文翻译：

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用于医学切片插值的卷积神经网络与可变形图像配准

医学图像切片插值是一个活跃的研究领域。该任务的方法可以分为两大类：基于强度的插值方法和基于对象的插值方法。虽然基于强度的方法通常更容易执行且计算成本更低，但基于对象的方法能够产生更准确的结果并考虑切片内对象的可变形变化。在本文中，分析和比较了两种著名的基于对象的插值方法的性能。在这里，考虑了专为医学应用设计的基于可变形配准的方法和针对视频帧插值训练的基于学习的方法。虽然基于可变形配准的技术能够对切片内物体形状的变化进行精确建模，基于学习的方法能够产生具有相似精度的结果，但在很短的时间内具有更清晰的外观。尽管事实上基于学习的方法不是针对医学图像进行训练，而是使用常规视频片段进行训练。然而，实验表明该方法能够得到准确的切片插值结果。