Recent medical image segmentation methods apply implicit neural
representation (INR) to the decoder for achieving a continuous coordinate
decoding to tackle the drawback of conventional discrete grid-based data
representations. However, the INR-based decoder cannot well handle the feature
misalignment problem brought about by the naive latent code acquisition
strategy in INR. Although there exist many feature alignment works, they all
adopt a progressive multi-step aligning paradigm on a discrete feature pyramid,
which is incompatible with the continuous one-step characteristics of INR-based
decoder, and thus fails to be the solution. Therefore, we propose Q2A, a novel
one-step query-based aligning paradigm, to solve the feature misalignment
problem in the INR-based decoder. Specifically, for each target coordinate, Q2A
first generates several queries depicting the spatial offsets and the cell
resolutions of the contextual features aligned to the coordinate, then
calculates the corresponding aligned features by feeding the queries into a
novel implicit fully continuous feature pyramid (FCFP), finally fuses the
aligned features to predict the class distribution. In FCFP, we further propose
a novel universal partition-and-aggregate strategy (P&A) to replace the naive
interpolation strategy for latent code acquisition in INR, which mitigates the
information loss problem that occurs when the query cell resolution is
relatively large and achieves an effective feature decoding at arbitrary
continuous resolution. We conduct extensive experiments on two medical
datasets, i.e. Glas and Synapse, and a universal dataset, i.e. Cityscapes, and
they show the superiority of the proposed Q2A.

提出了一种名为 Q2A 的新型查询式对齐模式，用于解决隐式神经表示（INR）解码器中引起的特征不对齐问题，并通过使用全新的全连续特征金字塔（FCFP）在任意连续分辨率上实现了有效的特征解码。在两个医学数据集和一个通用数据集上的实验表明，所提出的 Q2A 算法优于现有方法。