Automatic LV Localization and Short-Axis Plane Estimation from Arbitrary CMR Slice
For cardiac MRI analysis, this provides a new single-slice approach to estimate LV orientation, potentially enabling more flexible and accurate downstream analysis without multi-slice data.
This paper introduces a new task of joint LV localization and 3D orientation estimation from a single CMR slice, proposing a Polar-Coupled Circular (PCC) embedding for continuous orientation representation. On four datasets, it achieves an average mIoU of 86.18% and an average angle deviation of 3.39°.
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.