Year: 2025
Type: preprint
Abstract: Histopathological analysis has been transformed by serial section-based methods, advancing beyond traditional 2D histology to enable volumetric and microstructural insights in oncology and inflammatory disease diagnostics. This review outlines key developments in specimen preparation and high-throughput imaging that support these innovations. Computational workflows are categorized into multimodal image co-registration, 3D histoarchitecture reconstruction, multiplexed immunohistochemical correlation, and cross-scale data fusion. These approaches exploit serial section-derived spatial concordance to enhance resolution in microenvironmental and molecular profiling. Despite progress, challenges remain in harmonizing heterogeneous datasets, optimizing large-scale registration, and ensuring interpretability. Future directions include spatial transcriptomics, and applications in developmental biology and neuroscience in AI integration, establishing serial section analytics as central to precision histopathology.
Source: arXiv (Cornell University)
Authors: Zhuang, Zhenfeng, Cen, Min, Jiang, Lei, Peng, Qiong, Hu, Yihuang, Zhou, Hong-Yu, Wang, Liansheng
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4416553315
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