Cross-Sequence Robustness of Deep Learning for Placental MRI Segmentation: A Patient-Matched Study in Placenta Accreta Spectrum

Background: Deep learning-based placental MRI segmentation could enable quantitative assessment of placenta accreta spectrum (PAS), but models developed using one MRI sequence may not generalize reliably to another, potentially compromising downstream imaging measurements. Objectives: Our objectives were to quantify the magnitude and directionality of performance degradation during direct cross-sequence transfer between balanced turbo field echo (BTFE) and single-shot turbo spin echo (ssh-TSE), determine whether pooled multisequence training using both sequences mitigates this degradation, and assess whether cross-sequence degradation is consistently observed across three segmentation pipelines. Methods: We evaluated 7106 placental MRI image-mask pairs using three segmentation pipelines: U-Net, nnU-Net v2, and U-Mamba Bot. Models were trained using BTFE-only, ssh-TSE-only, or pooled multisequence data and evaluated on a patient-matched held-out cohort under sequence-specific, direct cross-sequence transfer, and pooled multisequence-training conditions. The primary outcome was patient-level aggregated Dice, supplemented by boundary and segmentation-derived size-error analyses. Results: In-domain mean Dice ranged from 0.888 to 0.917. Cross-sequence transfer significantly reduced performance across all pipelines, with markedly greater degradation when BTFE-trained models were applied to ssh-TSE (Dice loss 0.066–0.119) than when ssh-TSE-trained models were applied to BTFE (0.039–0.051; all p < 0.001). Pooled multisequence training recovered 0.044–0.118 Dice relative to direct cross-sequence transfer and achieved performance within −0.0020 to +0.0058 Dice of the corresponding sequence-specific models. Boundary-error analyses showed deterioration under sequence shift, while increases in mean absolute segmentation-derived size error on the ssh-TSE target were more pronounced for nnU-Net and U-Mamba Bot, with less conclusive findings for U-Net and inconsistent changes on BTFE. Conclusions: Placental MRI segmentation in PAS was strongly sequence-dependent, with substantial and directionally asymmetric degradation when models were directly transferred to an MRI sequence not represented during training across all three evaluated segmentation pipelines, although the magnitude of degradation varied among pipelines. Pooled multisequence training on BTFE and ssh-TSE largely mitigated this degradation and restored performance toward sequence-specific levels. These findings support sequence-aware development and validation of placental segmentation models across heterogeneous MRI protocols.