Unsupervised Landmark detection
||Unsupervised Landmark detection|
||Markus Ekvall <firstname.lastname@example.org>|
||Kungliga Tekniska högskolan|
||2023-03-30 – 2023-10-01|
Spatial landmarks are useful for comparing histological heterogeneity between samples or sites, tracking regions of interest in microscopy, and registering tissue samples in a common coordinate framework. While unsupervised landmark detection has been studied previously, current methods are unsuitable for histological image data as they typically require a large number of images and do not account for non-linear deformations between tissue sections. We address these limitations by proposing a novel method for landmark detection and registration using neural-network-guided thin plate splines. We evaluate the proposed method on a wide range of datasets and show that it outperforms existing approaches in terms of both accuracy and stability.