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

TL;DR

This paper introduces an automated, cloud-native pipeline for histology image segmentation and clustering, significantly reducing manual annotation effort by grouping similar tissue structures for labeling.

Contribution

The authors present a novel end-to-end pipeline combining segmentation, feature extraction, dimensionality reduction, and clustering, enabling efficient tissue component annotation in WSIs.

Findings

Achieved 96.8% accuracy in cluster-label alignment across diverse tissue types.

Reduced annotation effort by enabling labeling of clusters instead of individual objects.

Successfully applied to 3,696 tissue components from 13 tissue types across three species.

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.