RepSNet：一种基于边界回归与结构重参数化的细胞核实例分割模型
RepSNet: A Nucleus Instance Segmentation model based on Boundary Regression and Structural Re-parameterization
AI总结：
本文提出RepSNet模型，通过边界回归、边界投票机制和结构重参数化，在提升细胞核实例分割精度的同时降低推理计算负担，并在H&E病理图像上验证了其优越性。
摘要：
病理诊断是肿瘤诊断的金标准，而细胞核实例分割是数字病理分析与病理诊断中的关键步骤。然而，模型的计算效率以及重叠目标的处理是该问题研究中的主要挑战。为此，设计了一种神经网络模型RepSNet，该模型基于细胞核边界回归和结构重参数化方案，用于对H&E染色的组织病理图像中的细胞核进行分割和分类。首先，RepSNet为每个像素估计其父细胞核的边界位置信息（BPI）。BPI估计融合了像素的局部信息与父细胞核的上下文信息。然后，通过所提出的边界投票机制（BVM）聚合一系列像素的BPI来估计细胞核边界，并利用连通分量分析过程从估计的细胞核边界计算实例分割结果。BVM本质上在不同像素的BPI之间实现了一种协同置信增强。因此，与文献中基于直接像素识别方案获得细胞核边界的现有方法不同，RepSNet基于来自宏观信息的某种引导，通过一种集成机制来计算其边界决策。此外，RepSNet采用了可重参数化的编码器-解码器结构。该模型不仅能够聚合来自多个不同尺度感受野的特征，有助于提高分割精度，而且通过结构重参数化技术减少了模型推理阶段的参数量和计算负担。大量实验证明了RepSNet相较于若干典型基准模型的优越性。
英文摘要：
Pathological diagnosis is the gold standard for tumor diagnosis, and nucleus instance segmentation is a key step in digital pathology analysis and pathological diagnosis. However, the computational efficiency of the model and the treatment of overlapping targets are the major challenges in the studies of this problem. To this end, a neural network model RepSNet was designed based on a nucleus boundary regression and a structural re-parameterization scheme for segmenting and classifying the nuclei in H\&E-stained histopathological images. First, RepSNet estimates the boundary position information (BPI) of the parent nucleus for each pixel. The BPI estimation incorporates the local information of the pixel and the contextual information of the parent nucleus. Then, the nucleus boundary is estimated by aggregating the BPIs from a series of pixels using a proposed boundary voting mechanism (BVM), and the instance segmentation results are computed from the estimated nucleus boundary using a connected component analysis procedure. The BVM intrinsically achieves a kind of synergistic belief enhancement among the BPIs from various pixels. Therefore, different from the methods available in literature that obtain nucleus boundaries based on a direct pixel recognition scheme, RepSNet computes its boundary decisions based on some guidances from macroscopic information using an integration mechanism. In addition, RepSNet employs a re-parametrizable encoder-decoder structure. This model can not only aggregate features from some receptive fields with various scales which helps segmentation accuracy improvement, but also reduce the parameter amount and computational burdens in the model inference phase through the structural re-parameterization technique. Extensive experiments demonstrated the superiorities of RepSNet compared to several typical benchmark models.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
