CTARR：一种通过图谱配准识别CT图像解剖区域的快速鲁棒方法
CTARR: A fast and robust method for identifying anatomical regions on CT images via atlas registration
AI总结：
针对医学图像分析中无关区域增加深度学习计算负担与出错率的问题，提出基于图谱配准的CT解剖区域识别方法CTARR，可快速裁剪目标区域，在6个公开数据集上验证了其高前景保留率与显著提速效果。
摘要：
医学图像分析任务通常聚焦于患者体内特定位置的区域或结构，图像的大部分区域可能与分析任务无关。采用基于深度学习的方法时，这会导致推理阶段计算负担不必要地增加，同时提升出错概率。本文提出CTARR，一种全新的CT解剖区域识别通用方法。该方法可作为任何基于深度学习的CT图像分析流程的预处理步骤，自动识别与后续任务相关的预定义解剖区域并移除其余部分。它可应用于三类场景：（1）图像分割，用于避免解剖学上不合理区域的假阳性并加快推理速度；（2）图像分类，用于生成解剖背景一致的图像裁剪块；（3）图像配准，作为快速预配准步骤。我们提出的方法基于图谱配准，提供了一种快速鲁棒的方式，可从脑部、胸部、腹部和/或骨盆的任意无标注CT扫描中，裁剪出以一个或多个边界框编码的任意解剖区域。我们在6个公开分割挑战数据集上开展评估，在医学图像分割场景下验证了该方法的实用性与鲁棒性。在绝大多数案例和任务中，感兴趣区域内的前景体素均得到保留（保留率97.45%-100%），在深度学习工作站上仅需零点几秒即可完成计算（0.1-0.21秒），且大幅缩短了分割运行时间（提速2.0-12.7倍）。我们的代码可在https://github.com/ThomasBudd/ctarr 获取。
英文摘要：
Medical image analysis tasks often focus on regions or structures located in a particular location within the patient's body. Often large parts of the image may not be of interest for the image analysis task. When using deep-learning based approaches, this causes an unnecessary increases the computational burden during inference and raises the chance of errors. In this paper, we introduce CTARR, a novel generic method for CT Anatomical Region Recognition. The method serves as a pre-processing step for any deep learning-based CT image analysis pipeline by automatically identifying the pre-defined anatomical region that is relevant for the follow-up task and removing the rest. It can be used in (i) image segmentation to prevent false positives in anatomically implausible regions and speeding up the inference, (ii) image classification to produce image crops that are consistent in their anatomical context, and (iii) image registration by serving as a fast pre-registration step. Our proposed method is based on atlas registration and provides a fast and robust way to crop any anatomical region encoded as one or multiple bounding box(es) from any unlabeled CT scan of the brain, chest, abdomen and/or pelvis. We demonstrate the utility and robustness of the proposed method in the context of medical image segmentation by evaluating it on six datasets of public segmentation challenges. The foreground voxels in the regions of interest are preserved in the vast majority of cases and tasks (97.45-100%) while taking only fractions of a seconds to compute (0.1-0.21s) on a deep learning workstation and greatly reducing the segmentation runtime (2.0-12.7x). Our code is available at https://github.com/ThomasBudd/ctarr.
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