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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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模式感知变压器:在连续的医学图像中等级模式的传播.

Lingyun Wu, Xiang Gao, Zhiqiang Hu

IEEE transactions on medical imaging

|August 18, 2023

概括

本研究介绍了一种新的医疗成像等级模式感知令牌化策略,改进了连续图像中的上下文信息挖掘. 模式感知变压器 (PATrans) 在视频对象检测和3D语义细分方面取得了最先进的结果.

科学领域:

计算机视觉计算机视觉
医学成像分析医学成像分析
人工智能的人工智能

背景情况:

医学成像中的序列图像分析往往难以有效地捕获上下文信息.
使用点智能令牌编码的现有方法在保留本地和全球细节方面存在局限性.

研究的目的:

在医学成像任务中,开发一种用于在连续图像中挖掘上下文信息的新方法.
引入一个层次化的模式意识的代币化策略和一个模式意识的变压器 (PATrans) 来改进序列建模.

主要方法:

提出了一个层次模式意识的代币化策略,以独立地和层次地处理不同的视觉模式.
一个具有全球-本地双路径模式感知交叉注意力机制的模式感知变压器 (PATrans) 已被开发用于层次模式匹配.
PATrans被设计成一个插入式模块,用于与各种骨干网络集成.

主要成果:

在视频对象检测和3D体积语义细分方面,PATrans在多个基准中实现了最先进的性能.
具体的基准结果包括CVC-Video (92.3%检测F1),ASU-Mayo (99.1%定位F1),肺瘤 (78.59% DSC),鼻瘤 (75.50% DSC) 和瘤 (87.53% DSC).

结论:

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拟议的层次模式感知代币化和PATrans有效地通过保留本地和全球信息来模拟序列图像.
在各种医学成像序列建模任务中,PATrans展示了广泛的适用性和卓越的性能.