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Updated: Apr 7, 2026

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Automated Dissection Protocol for Tumor Enrichment in Low Tumor Content Tissues

Published on: March 29, 2021

MiroSCOPE: An AI-driven digital pathology platform for annotating functional tissue units.

Madeleine R Fenner¹, Selim Sevim², Guanming Wu³

¹Department of Molecular and Medical Genetics, Oregon Health and Science University, Portland, OR, USA.

Biorxiv : the Preprint Server for Biology

|August 12, 2025

Summary

This summary is machine-generated.

MiroSCOPE is an AI-assisted platform that accelerates the annotation of functional tissue units (FTUs) in digital pathology. This tool enables efficient analysis of cancer tissue structures, crucial for accurate pathological assessment.

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Published on: June 6, 2025

Area of Science:

Digital Pathology
Computational Pathology
Artificial Intelligence in Medicine

Background:

Current digital pathology analyses often overlook functional tissue units (FTUs), vital for tissue function and cancer assessment.
Manual annotation of FTUs is time-consuming and costly, hindering large-scale studies.
Existing artificial intelligence (AI) solutions lack comprehensive workflows for building annotated cohorts for FTU analysis.

Purpose of the Study:

To develop an end-to-end AI-assisted platform, MiroSCOPE, for scalable annotation of FTUs in digital pathology.
To overcome the limitations of manual annotation and accelerate the development of interpretable AI approaches for FTU analysis.
To provide a high-quality, publicly available dataset for FTU-level machine learning in cancer research.

Main Methods:

Developed MiroSCOPE, an AI-assisted platform built on QuPath for annotating FTUs.
Integrated a fine-tunable multiclass segmentation model with curation-specific usability features for a human-in-the-loop system.
Applied MiroSCOPE to annotate over 71,900 FTUs across 184 prostate cancer hematoxylin and eosin (H&E)-stained tissue samples.

Main Results:

MiroSCOPE significantly accelerated AI-driven annotation of FTUs by pathologists.
Successfully annotated 71,900+ FTUs on 184 prostate cancer samples, demonstrating scalability and potential for breast cancer applications.
Publicly released the Miro-120 dataset (120 prostate cancer H&E samples with 30,568 annotations) for community use.

Conclusions:

MiroSCOPE provides an adaptable AI-driven platform for efficient FTU annotation in digital pathology.
Facilitates the incorporation of crucial structural information into digital pathology analyses.
The Miro-120 dataset serves as a valuable resource for advancing FTU-level machine learning in cancer research.