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.

翻译：近期医学图像分割方法将隐式神经表示（INR）应用于解码器，通过实现连续坐标解码来克服传统离散网格数据表示的缺陷。然而，基于INR的解码器难以妥善处理由INR中朴素潜在码获取策略引发的特征未对齐问题。尽管已有大量特征对齐工作，但它们均采用渐进式多步对齐范式处理离散特征金字塔，这与INR解码器连续单步的特性不兼容，因此无法成为解决方案。为此，我们提出Q2A——一种新颖的单步查询式对齐范式，用于解决INR解码器中的特征未对齐问题。具体而言，对于每个目标坐标，Q2A首先生成多个查询，描述与该坐标对齐的上下文特征的空间偏移量和细胞分辨率；随后通过将查询输入新型隐式全连续特征金字塔（FCFP）计算对应的对齐特征；最后融合这些对齐特征以预测类别分布。在FCFP中，我们进一步提出新颖的通用分区-聚合策略（P&A），替代INR中用于潜在码获取的朴素插值策略，该策略可缓解查询细胞分辨率较大时出现的信息丢失问题，并实现任意连续分辨率下的有效特征解码。我们在两个医学数据集（Glas和Synapse）及一个通用数据集（Cityscapes）上开展大量实验，结果证明了所提Q2A方法的优越性。