Precise segmentation of medical images is fundamental for extracting critical
clinical information, which plays a pivotal role in enhancing the accuracy of
diagnoses, formulating effective treatment plans, and improving patient
outcomes. Although Convolutional Neural Networks (CNNs) and non-local attention
methods have achieved notable success in medical image segmentation, they
either struggle to capture long-range spatial dependencies due to their
reliance on local features, or face significant computational and feature
integration challenges when attempting to address this issue with global
attention mechanisms. To overcome existing limitations in medical image
segmentation, we propose a novel architecture, Perspective+ Unet. This
framework is characterized by three major innovations: (i) It introduces a
dual-pathway strategy at the encoder stage that combines the outcomes of
traditional and dilated convolutions. This not only maintains the local
receptive field but also significantly expands it, enabling better
comprehension of the global structure of images while retaining detail
sensitivity. (ii) The framework incorporates an efficient non-local transformer
block, named ENLTB, which utilizes kernel function approximation for effective
long-range dependency capture with linear computational and spatial complexity.
(iii) A Spatial Cross-Scale Integrator strategy is employed to merge global
dependencies and local contextual cues across model stages, meticulously
refining features from various levels to harmonize global and local
information. Experimental results on the ACDC and Synapse datasets demonstrate
the effectiveness of our proposed Perspective+ Unet. The code is available in
the supplementary material.

针对医学图像分割的局限性，我们提出了一种新的架构，Perspective+ Unet。该架构具有三个主要创新点：在编码器阶段引入了双通道策略，通过结合传统和扩张卷积的结果，既保持了局部感知域又显著扩展了它，从而更好地理解图像的全局结构；采用高效的非局部变换块 ENLTB 来有效地捕捉长程依赖关系，具有线性计算和空间复杂性；采用空间交叉尺度集成策略，在模型阶段跨级别细化特征，以协调全局和局部信息。我们在 ACDC 和 Synapse 数据集上进行了实验，结果验证了我们提出的 Perspective+ Unet 的有效性。