从任意CMR切片实现左心室（LV）自动定位与短轴平面估计
Automatic LV Localization and Short-Axis Plane Estimation from Arbitrary CMR Slice
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中文总结 AI 辅助
本研究提出从单张CMR切片同时进行左心室定位与3D方向估计的新范式，引入PCC嵌入解决方向建模问题，构建基准并在四数据集上取得优异性能，为CMR分析提供新框架。
中文摘要 AI 辅助
准确估计左心室（LV）方向对于心脏磁共振（CMR）成像及后续分析至关重要。现有方法通常将方向识别表述为离散视图分类，或依赖多切片几何相交，这限制了其对连续3D方向的建模能力及在任意切片间的泛化性。本研究提出一种新范式：从单张CMR切片同时进行LV定位与3D方向估计。为探究该场景，我们将代表性的方向感知检测框架适配到CMR领域，并分析其局限性。在此基础上，我们提出Polar-Coupled Circular（PCC）嵌入，该嵌入提供连续且无歧义的方向表示以解决上述局限性；同时，通过从体积型CMR分割数据集自动采样切片构建了可扩展基准。在四个数据集上的大量实验表明，该方法性能强劲，平均交并比（mIoU）达86.18%，平均角度偏差为3.39°。本研究为单切片LV方向建模确立了新的任务场景，并为空间感知的CMR分析提供了几何一致性框架，代码可在指定网址获取。
英文摘要
Accurate estimation of left ventricular (LV) orientation is essential for cardiac magnetic resonance (CMR) imaging and downstream analysis. Existing methods typically formulate orientation recognition as discrete view classification or rely on multi-slice geometric intersection, limiting their ability to model continuous 3D orientation and generalize across arbitrary slices. This work introduces a novel paradigm: Joint LV localization and 3D orientation estimation from a single CMR slice. To investigate this setting, representative orientation-aware detection frameworks are adapted to the CMR domain, and their limitations are analyzed. Upon that, we propose the Polar-Coupled Circular (PCC) embedding that provides a continuous and unambiguous orientation representation to address the limitations. Meanwhile, a scalable benchmark is constructed through automatic slice sampling from volumetric CMR segmentation datasets. Extensive experiments on four datasets demonstrate strong performance, achieving an average mIoU of 86.18% and an average angle deviation of 3.39°. This study establishes a new task setting for single-slice LV orientation modeling and provides a geometry-consistent framework for spatially informed CMR analysis. Code is available at https://github.com/yuyi1005/cmr-3d-ood.
发表机构
- The Ohio State University（俄亥俄州立大学）
- Nanjing University（南京大学）
机构由 AI 辅助整理，请以论文原文为准。
