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Training Nuclei Detection Algorithms with Simple Annotations.

Henning Kost¹, André Homeyer¹, Jesper Molin^2,3,4

¹Fraunhofer Institute for Medical Image Computing MEVIS, 28359 Bremen, Germany.

Journal of Pathology Informatics

|June 7, 2017

Summary

This summary is machine-generated.

Generating high-quality training data for nuclei detection is challenging. This study shows that using simple nucleus center markers with smart data selection methods can create effective training datasets, matching traditional methods.

Keywords:

Active learning machine learning nuclei detection training set generation

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Area of Science:

Computational pathology
Machine learning in biology
Image analysis

Background:

Effective training datasets are crucial for machine learning-based nuclei detection.
Manual annotation of nuclei contours is time-consuming and often impractical.

Purpose of the Study:

To compare methods for training nuclei detection algorithms using only nucleus center markers.
To assess automated sample extraction and selection techniques for improving training data efficiency and quality.

Main Methods:

Nuclei detection algorithms were trained using nucleus center markers instead of full contours.
Various automated sample extraction and selection strategies were evaluated.
Performance was assessed using a generic nuclei detection algorithm on Ki-67-stained breast cancer images.

Main Results:

A Voronoi tessellation approach for sample extraction yielded the best results.
Subsampling and class balancing significantly improved detection quality.
Active learning further enhanced the performance of the nuclei detection models.

Conclusions:

Nuclei detection algorithms trained with center markers can achieve quality comparable to those trained with contour annotations.
Appropriate sample extraction and selection methods are key to this success.