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

• URL: http://arxiv.org/abs/2604.09370v1
• Date: Fri, 10 Apr 2026 14:40:36 GMT
• Title: Cluster-First Labelling: An Automated Pipeline for Segmentation and Morphological Clustering in Histology Whole Slide Images
• Abstract summary: We present a cloud-native, end-to-end pipeline for labelling tissue components in whole slide images.<n>Our system filters out tiles deemed unlikely to contain valuable information.<n>It segments tissue components with Cellpose-SAM, extracts neural embeddings via a pretrained ResNet-50, reduces dimensionality with UMAP, and groups morphologically similar objects using DBSCAN clustering.<n>Our system achieves a weighted cluster-label alignment accuracy of 96.8%, with 7 of 13 tissue types reaching perfect agreement.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• Towards a Pseudo-Labeling Workflow for Celltype-Classification from Explanted Brain Slice Recordings [0.0]
  This paper proposes an unsupervised workflow to pseudo-label extracellular spikes from human brain slice MEA recordings into two putative cell types.<n>The raw data from the data acquisition system is used and processed.<n>The pipeline for pre-processing includes bandpass filtering, threshold-based spike detection, frame alignment and normalization.
  arXiv  Detail & Related papers  (2026-06-15T09:58:57Z)
• ε-Seg: Sparsely Supervised Semantic Segmentation of Microscopy Data [6.617593699054488]
  epsilon-Seg is a method based on hierarchical variational autoencoders (HVAEs)<n>We introduce epsilon-Seg, employing center-region masking, label contrastive learning (CL), a Gaussian mixture model (GMM) prior, and clustering-free label prediction.<n>Our results show that epsilon-Seg is capable of achieving competitive sparsely-supervised segmentation results on complex biological image data.
  arXiv  Detail & Related papers  (2025-10-21T13:41:07Z)
• Scalable Whole Slide Image Representation Using K-Mean Clustering and Fisher Vector Aggregation [2.822194296769473]
  Whole slide images (WSIs) are high-resolution, giga sized images that pose significant computational challenges.<n>We present a scalable and efficient methodology for WSI classification by leveraging patch-based feature extraction, clustering, and Fisher encoding.<n>Our method captures local and global tissue structures and yields robust performance for large-scale WSI classification.
  arXiv  Detail & Related papers  (2025-01-21T12:22:15Z)
• UniCell: Universal Cell Nucleus Classification via Prompt Learning [76.11864242047074]
  We propose a universal cell nucleus classification framework (UniCell) It employs a novel prompt learning mechanism to uniformly predict the corresponding categories of pathological images from different dataset domains. In particular, our framework adopts an end-to-end architecture for nuclei detection and classification, and utilizes flexible prediction heads for adapting various datasets.
  arXiv  Detail & Related papers  (2024-02-20T11:50:27Z)
• Generalized Category Discovery with Clustering Assignment Consistency [56.92546133591019]
  Generalized category discovery (GCD) is a recently proposed open-world task. We propose a co-training-based framework that encourages clustering consistency. Our method achieves state-of-the-art performance on three generic benchmarks and three fine-grained visual recognition datasets.
  arXiv  Detail & Related papers  (2023-10-30T00:32:47Z)
• Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [85.51611950757643]
  We propose IAC (Instance-Adaptive Clustering), the first algorithm whose performance matches the instance-specific lower bounds both in expectation and with high probability.<n>IAC maintains an overall computational complexity of $ mathcalO(n, textpolylog(n) $, making it scalable and practical for large-scale problems.
  arXiv  Detail & Related papers  (2023-06-18T08:46:06Z)
• Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling [65.40672505658213]
  We propose a novel deep neural network, namely Category-Aware feature alignment and Pseudo-Labelling Network (CAPL-Net) for UDA nuclei instance segmentation and classification. Our approach outperforms state-of-the-art UDA methods with a remarkable margin.
  arXiv  Detail & Related papers  (2022-07-04T07:05:06Z)
• Glo-In-One: Holistic Glomerular Detection, Segmentation, and Lesion Characterization with Large-scale Web Image Mining [5.786726927985774]
  We develop the Glo-In-One toolkit to achieve holistic glomerular detection, segmentation, and characterization via a single line of command. We release a large-scale collection of 30,000 unlabeled glomerular images to further facilitate the algorithmic development of self-supervised deep learning.
  arXiv  Detail & Related papers  (2022-05-31T21:22:10Z)
• Two-Stream Graph Convolutional Network for Intra-oral Scanner Image Segmentation [133.02190910009384]
  We propose a two-stream graph convolutional network (i.e., TSGCN) to handle inter-view confusion between different raw attributes. Our TSGCN significantly outperforms state-of-the-art methods in 3D tooth (surface) segmentation.
  arXiv  Detail & Related papers  (2022-04-19T10:41:09Z)
• Compositional Clustering: Applications to Multi-Label Object Recognition and Speaker Identification [19.470445399577265]
  We consider a novel clustering task in which clusters can have compositional relationships. We propose three new algorithms that can partition examples into coherent groups and infer the compositional structure among them. Our work has applications to open-world multi-label object recognition and speaker identification & diarization with simultaneous speech from multiple speakers.
  arXiv  Detail & Related papers  (2021-09-09T10:42:14Z)
• Generative Multi-Label Zero-Shot Learning [136.17594611722285]
  Multi-label zero-shot learning strives to classify images into multiple unseen categories for which no data is available during training. Our work is the first to tackle the problem of multi-label feature in the (generalized) zero-shot setting. Our cross-level fusion-based generative approach outperforms the state-of-the-art on all three datasets.
  arXiv  Detail & Related papers  (2021-01-27T18:56:46Z)
• Weakly Supervised 3D Classification of Chest CT using Aggregated Multi-Resolution Deep Segmentation Features [5.938730586521215]
  Weakly supervised disease classification of CT imaging suffers from poor localization owing to case-level annotations. We propose a medical classifier that leverages semantic structural concepts learned via multi-resolution segmentation feature maps.
  arXiv  Detail & Related papers  (2020-10-31T00:16:53Z)
• LSD-C: Linearly Separable Deep Clusters [145.89790963544314]
  We present LSD-C, a novel method to identify clusters in an unlabeled dataset. Our method draws inspiration from recent semi-supervised learning practice and proposes to combine our clustering algorithm with self-supervised pretraining and strong data augmentation. We show that our approach significantly outperforms competitors on popular public image benchmarks including CIFAR 10/100, STL 10 and MNIST, as well as the document classification dataset Reuters 10K.
  arXiv  Detail & Related papers  (2020-06-17T17:58:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.