PatternExtract: A Facile, Scalable Pipeline for Point Pattern Generation from Spatial Imaging Data

Computational and Structural Biotechnology Journal 2026 Open access

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Authors: Shruti Sridhar, Gayatri Kumar, Victoire Ringler, Kanav Gupta, Siddham Jasoria, Ziwei Meng, Patrick William Jaynes, Chartsiam Tipgomut, Vaibhav Rajan, David W. Scott, Claudio Tripodo, Kasthuri Kannan
+1 more Anand Devaprasath D. Jeyasekharan
DOI: 10.34133/csbj.0141
Cited by: 0
References: 15

Abstract

Abstract We present PatternExtract, an open-source pipeline that generates accurate spatial point patterns from RGB pathology images and cell coordinate data without relying on composite channels or proprietary software. Using a novel two-kernel tissue segmentation method combined with automated pixel classification in QuPath, PatternExtract precisely excludes tissue artifacts such as necrosis and blood vessels to define spatial windows. Optimized on 568 diffuse large B-cell lymphoma images and validated on an independent cohort, the pipeline enables robust spatial analyses.

Fields of Study & Subjects

3D Modeling in Geospatial Applications Remote Sensing and LiDAR Applications 3D Shape Modeling and Analysis

Publication details

Journal / source
Computational and Structural Biotechnology Journal
Volume
35
Issue
1
Publication date
1 March 2026
Language
EN