通道增强的基于CNN-Transformer的多层级和多尺度细胞核分割
Channel Boosted CNN-Transformer-based Multi-Level and Multi-Scale Nuclei Segmentation
AI总结：
针对细胞核分割中CNN难以捕捉长距离依赖和Transformer易丢失低级特征的问题，提出两种结合双重注意力与通道增强的CNN-Transformer架构，在多器官组织学图像上实现了更优的分割性能。
摘要：
准确的细胞核分割是计算病理学中各种应用的基础，包括癌症诊断和治疗计划。细胞核表示中的微小变化都可能显著影响这些下游任务。然而，由于细胞核聚集、大小和形状的高类内变异性、与其他细胞的相似性以及细胞核与背景之间的颜色或对比度变化等因素，实现准确的分割仍然具有挑战性。尽管卷积神经网络（CNNs）在医学图像分割中被广泛使用，但它们可能难以捕捉对准确细胞核描绘至关重要的长距离依赖关系。Transformers解决了这一限制，但可能错过重要的低级特征。为了克服这些限制，我们利用基于CNN-Transformer的技术对H&E染色组织学图像中的细胞核进行分割。在这项工作中，我们提出了两种CNN-Transformer架构，即Nuclei Hybrid Vision Transformer（NucleiHVT）和Channel Boosted Nuclei Hybrid Vision Transformer（CB-NucleiHVT），它们利用CNNs和Transformers的优势，有效地学习多器官组织学图像中的细胞核边界。第一种架构NucleiHVT受UNet架构启发，并结合双重注意力机制，以有效捕捉多层级和多尺度上下文。另一方面，CB-NucleiHVT网络利用通道增强的概念来学习多样化的特征空间，增强了模型区分细胞核特征中细微变化的能力。对两个医学图像分割数据集的详细评估表明，所提出的架构优于现有的基于CNN、基于Transformer和混合方法。所提出的网络在定量指标和定性视觉评估方面均显示出有效的结果。
英文摘要：
Accurate nuclei segmentation is an essential foundation for various applications in computational pathology, including cancer diagnosis and treatment planning. Even slight variations in nuclei representations can significantly impact these downstream tasks. However, achieving accurate segmentation remains challenging due to factors like clustered nuclei, high intra-class variability in size and shape, resemblance to other cells, and color or contrast variations between nuclei and background. Despite the extensive utilization of Convolutional Neural Networks (CNNs) in medical image segmentation, they may have trouble capturing long-range dependencies crucial for accurate nuclei delineation. Transformers address this limitation but might miss essential low-level features. To overcome these limitations, we utilized CNN-Transformer-based techniques for nuclei segmentation in H&E stained histology images. In this work, we proposed two CNN-Transformer architectures, Nuclei Hybrid Vision Transformer (NucleiHVT) and Channel Boosted Nuclei Hybrid Vision Transformer (CB-NucleiHVT), that leverage the strengths of both CNNs and Transformers to effectively learn nuclei boundaries in multi-organ histology images. The first architecture, NucleiHVT is inspired by the UNet architecture and incorporates the dual attention mechanism to capture both multi-level and multi-scale context effectively. The CB-NucleiHVT network, on the other hand, utilizes the concept of channel boosting to learn diverse feature spaces, enhancing the model's ability to distinguish subtle variations in nuclei characteristics. Detailed evaluation of two medical image segmentation datasets shows that the proposed architectures outperform existing CNN-based, Transformer-based, and hybrid methods. The proposed networks demonstrated effective results both in terms of quantitative metrics, and qualitative visual assessment.
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