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Patient-Specific Polyvinyl Alcohol Phantom Fabrication with Ultrasound and X-Ray Contrast for Brain Tumor Surgery Planning

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Published on: July 14, 2020

基于深度学习的头瘤细分的概率图:图形用户界面设计和测试.

Alessia De Biase¹, Liv Ziegfeld², Nanna Maria Sijtsema³

¹Department of Radiation Oncology, University Medical Center Groningen (UMCG), 9700 RB, Groningen, the Netherlands; Data Science Center in Health (DASH), University Medical Center Groningen (UMCG), 9700 RB, Groningen, the Netherlands.

Computers in biology and medicine

|May 31, 2024

概括

深度学习生成的瘤概率图为头癌细分提供了直观和可解释的替代方案,改善了放射瘤学家的工作流程. 这种方法增强了瘤自我细分模型的概括性.

关键词:

适应性细分是适应性的细分.临床解释性临床解释性深度学习是一种深度学习.头癌是头部和部的癌症.以人为中心的人工智能瘤细分瘤的细分

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科学领域:

医疗成像医学成像
在瘤学中使用人工智能
辐射治疗计划辐射治疗计划

背景情况:

在头癌中手动瘤细分是主观的,因为成像的变化.
手动轮的变化限制了深度学习 (DL) 自动细分模型的概括性.
一种基于DL的新方法被开发出来,可以输出瘤概率图,而不是固定的轮.

研究的目的:

为了证明DL生成的瘤细分概率图的临床相关性和直观性.
为放射瘤学家提供一个更适合的解决方案,用于总瘤体积细分.
为了提高基于DL的瘤自细分模型的性能.

主要方法:

开发一种基于DL的方法,以生成voxel-wise瘤概率图.
用于与概率图进行交互的图形用户界面 (GUI) 的设计.
通过对9名放射性瘤学专家进行用户研究,评估可用性和功能.

主要成果:

辐射瘤学家更喜欢彩虹彩色图,以便直观地可视化瘤概率图.
一个滑块功能,为值选择被赞赏的轮生成.
原型展示了出色的可用性和积极融入临床工作流程.

结论:

由DL生成的瘤概率图是可以解释的,透明的和直观的.
这些地图代表了单个输出瘤细分模型的优越替代方案.
这种方法增强了瘤的自我细分,并协助放射瘤学家.