利用深度学习进行空间地标检测和组织配准，这一成果由瑞典索尔纳皇家理工学院Joakim Lundeberg、Markus Ekvall研究组经过不懈努力而取得。2024年3月4日出版的《自然-方法学》发表了这项成果。

为了解决空间特征处理的难题，研究人员引入了方便的特征检测方法，这是一种使用神经网络引导的薄板样条新型无监督特征检测和配准方法。该研究提出的方法在组织学和空间解析转录组学等各种数据集上进行了评估，结果表明，与现有方法相比，该方法在准确性和稳定性方面都表现出色。

据悉，空间特征对于揭示样本或部位之间的组织学特征，在显微镜下跟踪感兴趣区域以及在通用坐标框架内，对组织样本进行注释至关重要。虽然其他研究对无监督地标检测进行了探索，但现有的方法并不适合组织学图像数据，因为它们通常需要大量图像才能汇聚，无法处理组织切片之间的非线性变形，而且对Z堆栈对齐、图像数据以外的其他模式或多模态数据无效。

附：原文原文

Title: Spatial landmark detection and tissue registration with deep learning

Author: Ekvall, Markus, Bergenstrhle, Ludvig, Andersson, Alma, Czarnewski, Paulo, Olegrd, Johannes, Kll, Lukas, Lundeberg, Joakim

Issue&Volume: 2024-03-04

Abstract: Spatial landmarks are crucial in describing histological features between samples or sites, tracking regions of interest in microscopy, and registering tissue samples within a common coordinate framework. Although other studies have explored unsupervised landmark detection, existing methods are not well-suited for histological image data as they often require a large number of images to converge, are unable to handle nonlinear deformations between tissue sections and are ineffective for z-stack alignment, other modalities beyond image data or multimodal data. We address these challenges by introducing effortless landmark detection, a new unsupervised landmark detection and registration method using neural-network-guided thin-plate splines. Our proposed method is evaluated on a diverse range of datasets including histology and spatially resolved transcriptomics, demonstrating superior performance in both accuracy and stability compared to existing approaches.

DOI: 10.1038/s41592-024-02199-5

Source: https://www.nature.com/articles/s41592-024-02199-5