Single Image Estimation of Cell Migration Direction by Deep Circular Regression

2024-06-27

arXiv.org

AI Summary

This study addresses the problem of estimating cell migration direction from a single microscopic image, moving beyond conventional discrete quadrant-based classification. The proposed method introduces a deep circular regression framework that models direction as a periodic continuous variable, incorporating periodic angular encoding and a circularly sensitive loss function for end-to-end high-accuracy prediction. Built upon a convolutional neural network architecture, it explicitly captures the circular topology of angular space, thereby eliminating boundary discontinuity errors inherent in linear representations. Evaluated on two real-cell microscopy datasets, the approach achieves a mean angular error of approximately 17°, substantially outperforming existing four-class classification methods. This work establishes a novel paradigm for time-series-independent cell motion analysis and advances deep learning methodologies for regressing periodic variables in biomedical imaging.

Technology Category

• Machine Learning: Learning with Manifolds
• Computer Vision: Representation Learning for Vision
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

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• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

Abstract

In this paper we study the problem of estimating the migration direction of cells based on a single image. To the best of our knowledge, there is only one related work that uses a classification CNN for four classes (quadrants). This approach does not allow detailed directional resolution. We solve the single image estimation problem using deep circular regression with special attention to cycle-sensitive methods. On two databases we achieve an average accuracy of $sim$17 degrees, which is a significant improvement over the previous work.

Problem

• Estimate cell migration direction from single images
• Improve directional resolution using deep circular regression
• Achieve lower mean estimation error than previous methods

Innovation

• Deep circular regression for cell migration estimation
• Cycle-sensitive methods improve directional resolution
• Single image analysis reduces mean error to 17 degrees