Organ segmentation is a fundamental task in medical imaging, and it is useful for many clinical automation pipelines. Typically, the process involves segmenting the entire volume, which can be unnecessary when the points of interest are limited. In those cases, a classifier could be used instead of segmentation. However, there is an inherent trade-off between the context size and the speed of classifiers. To address this issue, we propose a new method that employs a data selection strategy with sparse sampling across a wide field of view without image resampling. This sparse sampling strategy makes it possible to classify voxels into multiple organs in real time without using accelerators. Although our method is an independent classifier, it can generate full segmentation by querying grid locations at any resolution. We have compared our method with existing segmentation techniques, demonstrating its potential for superior runtime in practical applications in medical imaging.

翻译：器官分割是医学影像中的一项基础任务，对许多临床自动化流程具有重要价值。传统流程通常涉及对整个体素区域进行分割，而当关注点有限时，这种做法可能并非必要。在此类场景下，可采用分类器替代分割方法。然而，分类器在上下文规模与处理速度之间存在固有权衡。针对这一问题，我们提出一种新方法，该方法采用数据选择策略，在不进行图像重采样的情况下，对宽视野区域执行稀疏采样。这种稀疏采样策略使得无需使用加速器即可实时将体素分类为多个器官。尽管我们的方法是一个独立的分类器，但通过以任意分辨率查询网格位置，它能够生成完整的分割结果。我们将本方法与现有分割技术进行了对比，验证了其在医学影像实际应用中具有卓越的运行速度潜力。