  Left atrial (LA) segmentation is a crucial technique for irregular heartbeat
(i.e., atrial fibrillation) diagnosis. Most current methods for LA segmentation
strictly assume that the input data is acquired using object-oriented center
cropping, while this assumption may not always hold in practice due to the high
cost of manual object annotation. Random cropping is a straightforward data
pre-processing approach. However, it 1) introduces significant irregularities
and incompleteness in the input data and 2) disrupts the coherence and
continuity of object boundary regions. To tackle these issues, we propose a
novel Dynamic Position transformation and Boundary refinement Network (DPBNet).
The core idea is to dynamically adjust the relative position of irregular
targets to construct their contextual relationships and prioritize difficult
boundary pixels to enhance foreground-background distinction. Specifically, we
design a shuffle-then-reorder attention module to adjust the position of
disrupted objects in the latent space using dynamic generation ratios, such
that the vital dependencies among these random cropping targets could be well
captured and preserved. Moreover, to improve the accuracy of boundary
localization, we introduce a dual fine-grained boundary loss with
scenario-adaptive weights to handle the ambiguity of the dual boundary at a
fine-grained level, promoting the clarity and continuity of the obtained
results. Extensive experimental results on benchmark dataset have demonstrated
that DPBNet consistently outperforms existing state-of-the-art methods.
