Medical image segmentation is a fundamental task in medical image analysis. Despite that deep convolutional neural networks have gained stellar performance in this challenging task, they typically rely on large labeled datasets, which have limited their extension to customized applications. By revisiting the superiority of atlas based segmentation methods, we present a new framework of One-pass aligned Atlas Set for Images Segmentation (OASIS). To address the problem of time-consuming iterative image registration used for atlas warping, the proposed method takes advantage of the power of deep learning to achieve one-pass image registration. In addition, by applying label constraint, OASIS also makes the registration process to be focused on the regions to be segmented for improving the performance of segmentation. Furthermore, instead of using image based similarity for label fusion, which can be distracted by the large background areas, we propose a novel strategy to compute the label similarity based weights for label fusion. Our experimental results on the challenging task of prostate MR image segmentation demonstrate that OASIS is able to significantly increase the segmentation performance compared to other state-of-the-art methods.
翻译:医学图像分割是医学图像分析的一项基本任务。 尽管深层进化神经网络在这项具有挑战性的任务中取得了惊人的性能,但它们通常依赖大型标签数据集,这些数据集的扩展仅限于定制应用程序。我们通过重新审视基于地图集的分解方法的优越性,提出了一个新的图集图象分解(OASIS)新框架。为了解决用于图谱扭曲的耗时的迭代图像登记问题,拟议方法利用深层学习的力量实现一通图像登记。此外,通过应用标签限制,OASIS还使注册过程侧重于要分解的区域,以改进分解功能。此外,我们不使用基于类似图集图集的图集,而这种图集可能会被大背景地区分解。我们提出了一个新的战略,用以计算标签混合的比重相似的标签。我们在具有挑战性的标集MR图像分解任务上的实验结果显示,OASIS能够显著提高分解性,而与其他最先进的方法相比。