Cluster-First Labelling: An Automated Pipeline for Segmentation and Morphological Clustering in Histology Whole Slide Images

2026-04-10

AI Summary

Manual annotation of cells and structures in whole-slide tissue images is prohibitively expensive. This work proposes an end-to-end “cluster-then-annotate” pipeline that dramatically reduces labeling effort by shifting the annotation target from individual structures to representative clusters. The method leverages Cellpose-SAM for structural segmentation, ResNet-50 to extract morphological embeddings, and UMAP followed by DBSCAN for dimensionality reduction and clustering. Annotators label only one exemplar per cluster, enabling comprehensive coverage of the entire image. Evaluated across 13 tissue types and 3,696 annotated structures, the approach achieves a weighted cluster-to-label alignment accuracy of 96.8%, with perfect consistency in seven tissue categories. This framework demonstrates the first successful generalization of morphology-based clustering across species and diverse tissue types, substantially enhancing annotation efficiency.

Technology Category

Machine Learning: ClusteringComputer Vision: SegmentationHumans and AI: Crowd Sourcing and Human Computation

Application Category

Web Mining and Content Analysis: Normalization, clustering, classification, and summarization of Web textSemantics and Knowledge: Scalable techniques for the creation, curation, publication, maintenance, and consumption of large, Web-based, structured, reusable, knowledge graphs and ontologiesEconomics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling

Abstract

Labelling tissue components in histology whole slide images (WSIs) is prohibitively labour-intensive: a single slide may contain tens of thousands of structures--cells, nuclei, and other morphologically distinct objects--each requiring manual boundary delineation and classification. We present a cloudnative, end-to-end pipeline that automates this process through a cluster-first paradigm. Our system tiles WSIs, filters out tiles deemed unlikely to contain valuable information, segments tissue components with Cellpose-SAM (including cells, nuclei, and other morphologically similar structures), extracts neural embeddings via a pretrained ResNet-50, reduces dimensionality with UMAP, and groups morphologically similar objects using DBSCAN clustering. Under this paradigm, a human annotator labels representative clusters rather than individual objects, reducing annotation effort by orders of magnitude. We evaluate the pipeline on 3,696 tissue components across 13 diverse tissue types from three species (human, rat, rabbit), measuring how well unsupervised clusters align with independent human labels via per-tile Hungarian-algorithm matching. Our system achieves a weighted cluster-label alignment accuracy of 96.8%, with 7 of 13 tissue types reaching perfect agreement. The pipeline, a companion labelling web application, and all evaluation code are released as open-source software.

Problem

• whole slide images
• tissue segmentation
• morphological clustering
• manual annotation
• histology

Innovation

• cluster-first labelling
• whole slide image
• Cellpose-SAM
• unsupervised morphological clustering
• cloud-native pipeline