IAMSAM: image-based analysis of molecular signatures using the Segment Anything Model

被引：0

作者：

Dongjoo Lee ^{[1
]}

Jeongbin Park ^{[1
]}

Seungho Cook ^{[1
]}

Seongjin Yoo ^{[1
]}

Daeseung Lee ^{[1
]}

Hongyoon Choi ^{[1
]}

机构：

[1] Portrai,Department of Nuclear Medicine

[2] Inc,Department of Nuclear Medicine

[3] Seoul National University Hospital,undefined

[4] Seoul National University College of Medicine,undefined

来源：

Genome Biology | / 25卷 / 1期

关键词：

Spatial transcriptomics; Image segmentation; H&E image; Deep learning; Histology;

D O I：

10.1186/s13059-024-03380-x

中图分类号：

学科分类号：

摘要：

Spatial transcriptomics is a cutting-edge technique that combines gene expression with spatial information, allowing researchers to study molecular patterns within tissue architecture. Here, we present IAMSAM, a user-friendly web-based tool for analyzing spatial transcriptomics data focusing on morphological features. IAMSAM accurately segments tissue images using the Segment Anything Model, allowing for the semi-automatic selection of regions of interest based on morphological signatures. Furthermore, IAMSAM provides downstream analysis, such as identifying differentially expressed genes, enrichment analysis, and cell type prediction within the selected regions. With its simple interface, IAMSAM empowers researchers to explore and interpret heterogeneous tissues in a streamlined manner.

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