3D船体细分与有限的指导的2D结构-无神论船体注释的3D船体细分
IEEE journal of biomedical and health informatics
|June 4, 2024
概括
这项研究引入了一种新的3D深度学习模型,用于使用有限的2D注释进行血管细分. 基于3D形状的局部区分 (3D-SLD) 模型有效地对3D船只进行细分,减少了对广泛的3D手动注释的需求.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 生物医学工程 生物医学工程
背景情况:
- 准确的3D血管细分对于临床应用至关重要,但受到复杂的解剖变异和成像挑战的阻碍.
- 目前的监督深度学习方法需要昂贵的3D手动注释,并且缺乏跨不同血管结构的注释可重复使用性.
- 依赖昂贵且耗时的注释流程限制了自动化3D船舶细分的临床适用性.
研究的目的:
- 开发一种新的3D深度学习模型,以实现高效准确的3D血管细分.
- 通过使用有限的二维船舶注释来克服昂贵的3D注释的局限性.
- 提出一种方法,利用不同血管结构的现有注释,减少重复注释的努力.
主要方法:
- 引入了用于3D血管细分的3D形状引导局部歧视 (3D-SLD) 模型.
- 采用3D区域歧视损失来学习语音相似性,并为候选细分集群语义一致的语音.
- 通过对抗性形状约束损失集成的2D结构-不可知注释的形状分布,以指导树状形态.
- 采用突出评审总结 (HRS) 机制来提高培训稳定性,时间一致性,并识别可信的伪标签.
主要成果:
- 在3D脑血管细分中,只使用2D冠状动脉注释,取得了与最先进的 (SOTA) 几乎没有监督的方法相似的结果.
- 在3D肝血管细分方面表现出卓越的性能,达到最佳的子相似系数 (DSC).
- 验证了模型在细分复杂的3D血管结构中的有效性,指导有限.
结论:
- 拟议的3D-SLD模型有效地使用有限的2D注释进行3D血管细分,大大降低了注释成本.
- 该方法展示了重复使用现有的2D注释以指导跨不同解剖结构的3D细分任务的潜力.
- 这种方法为通过更容易获得的自动化3D血管细分来推进临床诊断和治疗规划提供了一个有希望的解决方案.
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