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Joint segmentation and landmark detection for cardiac MRI with multi-scale inter-task feature fusion and
Haiyang Chen1, Zhihao Liu2, Chenxi Hu1
1National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.
Background:
Segmentation and landmark detection are essential yet complex tasks in Cardiac MRI (CMR) for quantitative assessment of the heart. Existing automatic CMR analysis methods typically handle the two tasks independently, overlooking their synergies that could improve the performance of both tasks.
Purpose:
To develop an accurate and efficient multi-task learning (MTL) framework that jointly performs segmentation and landmark detection for CMR.
Methods:
The proposed SLaDNet-GaB framework integrates a Segmentation and Landmark Detection Network (SLaDNet) with a Gradient-as-Bridge (GaB) loss. SLaDNet adopts a shared feature encoder and two separate decoders to generate segmentation and landmark predictions, respectively. Inter-Task Feature Fusion (ITFF) modules are inserted between the decoders at multiple resolution scales to facilitate bidirectional information exchange via channel-wise attention. Along with two intra-task losses, the dedicated GaB loss is further introduced during training to enforce inter-task anatomical consistency. Specifically, the loss guides the predicted landmarks toward the segmentation objective boundaries using gradient maps. We comprehensively evaluated the proposed method against both single- and MTL baselines on two public CMR datasets: a cine dataset (ACDC; 105/15/30 subjects for training/validation/testing) and a T1 mapping dataset (STONE; 150/20/40 subjects for training/validation/testing).
Results:
SLaDNet-Gab outperformed the baselines in both segmentation (dice scores: 92.37/94.27% on ACDC/STONE, p < 0.05) and landmark detection (point-to-point distances: 2.22/1.51 voxels on ACDC/STONE, p < 0.05). For downstream tasks, the proposed method consistently achieved lower strain and T1 quantification errors than the other methods in most myocardial segments, with per-segment measurements closely agreeing with the manually derived values. Moreover, SLaDNet-GaB demonstrated relatively low training times (11.4/3.2 hours on ACDC/STONE) and an inference time of 6.12 msec./sample suitable for clinical adoption.
Conclusions:
The proposed multi-tasking method achieves improved accuracy in CMR segmentation, landmark detection, and downstream quantification, while maintaining high computational efficiency.