Mitosis Detection in Intestinal Crypt Images with Hough Forest and Conditional Random Fields

TL;DR

This paper presents a machine learning framework combining Hough Forest and Conditional Random Fields to automatically detect mitosis events in intestinal crypt images, significantly reducing manual annotation effort.

Contribution

The novel dual-phase approach jointly detects dividing cells and associates mother-daughter pairs, improving efficiency in mitosis event annotation.

Findings

Achieved 72% AUC in mitosis detection

Speeds up data processing pipeline

Reduces manual annotation workload

Abstract

Intestinal enteroendocrine cells secrete hormones that are vital for the regulation of glucose metabolism but their differentiation from intestinal stem cells is not fully understood. Asymmetric stem cell divisions have been linked to intestinal stem cell homeostasis and secretory fate commitment. We monitored cell divisions using 4D live cell imaging of cultured intestinal crypts to characterize division modes by means of measurable features such as orientation or shape. A statistical analysis of these measurements requires annotation of mitosis events, which is currently a tedious and time-consuming task that has to be performed manually. To assist data processing, we developed a learning based method to automatically detect mitosis events. The method contains a dual-phase framework for joint detection of dividing cells (mothers) and their progeny (daughters). In the first phase we detect mother and daughters independently using Hough Forest whilst in the second phase we associate mother and daughters by modelling their joint probability as Conditional Random Field (CRF). The method has been evaluated on 32 movies and has achieved an AUC of 72%, which can be used in conjunction with manual correction and dramatically speed up the processing pipeline.