Spatial-Temporal Mitosis Detection in Phase-Contrast Microscopy via Likelihood Map Estimation by 3DCNN

TL;DR

This paper introduces a 3D convolutional neural network-based method for detecting multiple mitosis events in phase-contrast microscopy videos, effectively addressing closely spaced events and annotation gaps.

Contribution

The proposed approach estimates a spatiotemporal likelihood map to improve mitosis detection and mitigate annotation gaps, outperforming existing methods on a challenging dataset.

Findings

Outperforms existing methods in F1-score

Effectively detects multiple closely spaced mitosis events

Reduces impact of annotation gaps during training

Abstract

Automated mitotic detection in time-lapse phasecontrast microscopy provides us much information for cell behavior analysis, and thus several mitosis detection methods have been proposed. However, these methods still have two problems; 1) they cannot detect multiple mitosis events when there are closely placed. 2) they do not consider the annotation gaps, which may occur since the appearances of mitosis cells are very similar before and after the annotated frame. In this paper, we propose a novel mitosis detection method that can detect multiple mitosis events in a candidate sequence and mitigate the human annotation gap via estimating a spatiotemporal likelihood map by 3DCNN. In this training, the loss gradually decreases with the gap size between ground truth and estimation. This mitigates the annotation gaps. Our method outperformed the compared methods in terms of F1- score using a challenging dataset that contains the data under four different conditions.