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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

像素扩散器:实用互动医疗图像细分,没有基础真相.

Mingeon Ju¹, Jaewoo Yang¹, Jaeyoung Lee¹

¹Major in Bio Artificial Intelligence, Department of Applied Artificial Intelligence, Hanyang University at Ansan, Ansan 15588, Republic of Korea.

Bioengineering (Basel, Switzerland)

|November 25, 2023

概括

像素扩散器 (PixelDiffuser) 是一种新的交互式医疗图像细分工具,不需要地面真相数据. 这种方法使用几次点击来实现高质量的细分,减少手工工作和培训成本.

关键词:

CT细分 CT细分 CT细分自动编码器自动编码器互动医疗细分互动医疗细分代的细分化代的细分化重建的噪音重建的噪音

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科学领域:

医疗成像医学成像
计算机视觉计算机视觉
人工智能的人工智能

背景情况:

深度学习模型需要广泛的标记数据用于医疗图像细分,这是昂贵和耗时的.
现有的交互式方法仍然需要大量的地面真相数据和用户交互来进行精确的细分.

研究的目的:

介绍PixelDiffuser,一种交互式医疗图像细分方法,消除了对细分地面真相数据的需求.
以最小的用户输入 (几次点击) 实现高质量的细分.

主要方法:

像素扩散器使用基于VGG19的自动编码器来执行细分.
该方法从用户选择的种子点开始细分,并逐渐扩大细分区域.
通过通过自动编码器的编码解码过程引入和传播图像扭曲来实现细分.

主要成果:

像素扩散器在医疗图像细分方面以不到五次点击实现了竞争性性能.
该方法在BTCV和CHAOS数据集上表现出有效性,其中包括CT和MRI扫描.
该模型需要最小的内存和没有额外的培训,提供效率.

结论:

像素扩散器为交互式医疗图像细分提供了一种高效和有效的解决方案.
该方法显著减少了对标记数据和用户交互时间的依赖.
这种方法为需要快速准确的细分的临床应用提供了有希望的替代方案.