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相关实验视频

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Quantitation of Protein Expression and Co-localization Using Multiplexed Immuno-histochemical Staining and Multispectral Imaging
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MSNSegNet:基于注意力的多形状核实例细分在组织病理学图像中的细分.

Ziniu Qian1, Zihua Wang1, Xin Zhang1

  • 1School of Biological Science and Medical Engineering, Beihang University, Haidian District, Beijing, 100191, Beijing, China.

Medical & biological engineering & computing
|February 24, 2024
PubMed
概括

准确细分不规则的细胞核对于评估免疫疗法的有效性至关重要. 本研究介绍了MSNSegNet,这是一种新的方法,可以改善多形状核的细分,特别是对于挑战非凸核,提高临床研究的准确性.

关键词:
核心实例细分 核心实例细分基于提案的方法 基于提案的方法专注于自己的注意力有意识的语义意识.

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科学领域:

  • 医疗图像分析 医学图像分析
  • 计算病理学计算病理学
  • 生物医学成像学 生物医学成像学

背景情况:

  • 准确细分不规则形状的细胞核,特别是纤维细胞,对于评估免疫疗法中的组织修复至关重要.
  • 由于非凸核的明显曲率变化存在挑战,阻碍了准确的细分.
  • 现有的核细分方法往往忽略不规则的形态,影响临床研究评估.

研究的目的:

  • 引入和解决多形状核细分的任务,包括正规和不规则的核形态.
  • 开发一种高效准确的计算方法,用于在临床研究中对各种核形状进行细分.
  • 通过加强病理特征分析,改善免疫疗法疗效的评估.

主要方法:

  • 一种基于提案的方法 (MSNSegNet),采用两阶段结构,以实现高效,高精度的细分.
  • 在第二阶段集成了一种新的自我注意模块,以改进特征并捕捉远程依赖.
  • 在第一阶段包括剩余注意力和语义意识模块,以准确预测提案和通过语义意识损失进行额外的监督.
  • 多形状核 (MsN) 数据集的构建,其中具有很大比例的非凸核.

主要成果:

  • 与现有方法相比,MSNSegNet在细分指标方面取得了显著的改进.
  • 对于所有核,在 (1.66), (2.15) 和 (0.65) 中观察到改善.
  • 对于具有挑战性的非凸核,提高了3.86%,提高了2.54,突出显示了临床相关性.

结论:

  • 拟议的MSNSegNet方法有效地解决了多形状核细分的挑战,特别是不规则和非凸核.
  • 选择性部署计算密集型模块和新的自我注意机制提高了准确性和效率.
  • 这一进步为改善病理学评估和评估临床研究中的免疫治疗疗效具有重大潜力.