通过对象重新绘制半监督的腹部多器官细分
Min Jeong Cho1,2,3, Jae Sung Lee1,2,3,4,5
1Interdisciplinary Program in Bioengineering, Seoul National University Graduate School, Seoul, South Korea.
Medical physics
|August 21, 2024
概括
本研究引入了一种用于腹部多器官细分的新型半监督学习 (SSL) 方法,通过整合重新绘制网络来纠正未标记数据的细分错误,显著提高了准确性.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 多器官细分对于医学成像应用,如放射治疗规划和定量分析至关重要.
- 手动细分是耗时的,缺乏可重复性;深度学习方法通常需要大量的标记数据.
- 现有的半监督学习 (SSL) 方法对腹部多器官细分有局限性.
研究的目的:
- 为腹部多器官细分引入一种新的SSL方法,利用未标记的数据.
- 为了提高深度神经网络在细分腹部器官中的性能.
- 整合一个重新绘制网络来纠正细分错误并提高准确性.
主要方法:
- 一种使用三个相互连接的神经网络的新型SSL方法:细分,教师和重新绘制网络.
- 分段网络使用标记和未标记的数据与一致性学习 (平均教师模型) 进行训练.
- 重绘网络从CT扫描中生成纠正的图像,保存解剖信息,以减少重新调整阶段的细分错误.
主要成果:
- 拟议的SSL方法在BTCV和AMOS数据集上进行了评估.
- 它始终超过了最先进的SSL方法 (MT,DTC) 和监督学习方法.
- 对腹部器官实现了优异的细分性能,在有限的标记数据下证明了有效性.
结论:
- 新的SSL方法有效地解决了腹部多器官细分方面的挑战.
- 整合一个重新绘制网络并利用未标记的数据显著提高了细分的准确性.
- 这种方法有望提高医学成像细分的精度和效率.
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