The advancement of developing efficient medical image segmentation has
evolved from initial dependence on Convolutional Neural Networks (CNNs) to the
present investigation of hybrid models that combine CNNs with Vision
Transformers. Furthermore, there is an increasing focus on creating
architectures that are both high-performing in medical image segmentation tasks
and computationally efficient to be deployed on systems with limited resources.
Although transformers have several advantages like capturing global
dependencies in the input data, they face challenges such as high computational
and memory complexity. This paper investigates the integration of CNNs and
Vision Extended Long Short-Term Memory (Vision-xLSTM) models by introducing a
novel approach called UVixLSTM. The Vision-xLSTM blocks captures temporal and
global relationships within the patches extracted from the CNN feature maps.
The convolutional feature reconstruction path upsamples the output volume from
the Vision-xLSTM blocks to produce the segmentation output. Our primary
objective is to propose that Vision-xLSTM forms a reliable backbone for medical
image segmentation tasks, offering excellent segmentation performance and
reduced computational complexity. UVixLSTM exhibits superior performance
compared to state-of-the-art networks on the publicly-available Synapse
dataset. Code is available at: this https URL

这篇论文研究了将 CNN 和 Vision-xLSTM 模型相结合的新方法 UVixLSTM，提出了它作为医学图像分割任务可靠的主干结构，提供出色的分割性能和较低的计算复杂性，并在公开可用的 Synapse 数据集上展现了卓越的性能。