Multiplexed Immunofluorescence Brain Image Analysis Using Self-Supervised Dual-Loss Adaptive Masked Autoencoder

TL;DR

This paper introduces DAMA, a self-supervised autoencoder that uses adaptive masking to learn detailed features from multiplexed immunofluorescence brain images, improving cell detection and segmentation without extensive annotations.

Contribution

DAMA is the first self-supervised learning method tailored for multiplexed immunofluorescence brain images, employing an adaptive masking strategy to enhance feature learning.

Findings

DAMA achieves superior cell detection and segmentation performance.

The adaptive masking strategy outperforms random masking methods.

DAMA reduces the need for extensive annotations in brain image analysis.

Abstract

Reliable large-scale cell detection and segmentation is the fundamental first step to understanding biological processes in the brain. The ability to phenotype cells at scale can accelerate preclinical drug evaluation and system-level brain histology studies. The impressive advances in deep learning offer a practical solution to cell image detection and segmentation. Unfortunately, categorizing cells and delineating their boundaries for training deep networks is an expensive process that requires skilled biologists. This paper presents a novel self-supervised Dual-Loss Adaptive Masked Autoencoder (DAMA) for learning rich features from multiplexed immunofluorescence brain images. DAMA's objective function minimizes the conditional entropy in pixel-level reconstruction and feature-level regression. Unlike existing self-supervised learning methods based on a random image masking strategy, DAMA employs a novel adaptive mask sampling strategy to maximize mutual information and effectively learn brain cell data. To the best of our knowledge, this is the first effort to develop a self-supervised learning method for multiplexed immunofluorescence brain images. Our extensive experiments demonstrate that DAMA features enable superior cell detection, segmentation, and classification performance without requiring many annotations.