具有吸引力的深度形态意识的活跃轮网络用于脊椎体轮提取,并扩展到异质和半监督的场景
Shen Zhao1, Jinhong Wang1, Xinxin Wang1
1Department of Artificial Intelligence, Sun Yat-sen University, Guangzhou 510006, China.
Medical image analysis
|July 27, 2023
概括
这项研究介绍了ADMIRE,这是一种新的深度活跃轮网络,用于从脊柱MRI中准确地自动提取脊椎身体轮 (AVBCE). ADMIRE提高了轮捕获范围和质量,在各种数据集上取得了最先进的结果.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 从脊柱MRI中自动抽取脊椎体轮 (AVBCE) 对于诊断脊柱疾病至关重要.
- 在AVBCE中的挑战包括数据异质性,复杂的图像特征,以及脊椎体形态的变化,导致细分错误.
- 深度活跃的基于轮 (基于深度ACM) 的方法提供了一个有希望的方法,通过直接参数化轮坐标来解决形态错误.
研究的目的:
- 提出一种新的基于深度ACM的方法,即ADMIRE (吸引深度形态意识的动态接触网络),以改进AVBCE.
- 为了增强捕获范围,并为活动轮生成形态意识参数.
- 为了在异质数据集和复杂的成像条件下实现强大而准确的AVBCE.
主要方法:
- 开发了ADMIRE,结合了详细的轮吸引术语 (CAT) 和全面的轮质量 (CCQ) 损失.
- CAT使用全对全力场来扩大轮捕获范围.
- CCQ损失监督轮形状,张力和光滑性,用于形态意识的参数生成.
主要成果:
- 在四个具有挑战性的数据集中,ADMIRE展示了最先进的性能,包括广泛的脊柱MRI和心脏成像数据.
- 该方法显示了高精度,对数据异质性和图像复杂性的稳定性,以及强大的概括能力.
- ADMIRE有效地解决了语义细分中的形态错误,并与半监督学习策略合作.
结论:
- 通过提高轮提取精度和稳定性,ADMIRE显著提升了AVBCE.
- 拟议的CAT和CCQ损失有效地增强了基于ACM的深度方法.
- ADMIRE的性能验证了其在脊髓疾病诊断和治疗规划中的临床应用潜力.
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