Article ; Online: Cellular data extraction from multiplexed brain imaging data using self-supervised Dual-loss Adaptive Masked Autoencoder.
Artificial intelligence in medicine
2024 Volume 151, Page(s) 102828
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 ... ...
| 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. In addition, to examine the generalizability of DAMA, we also experimented on TissueNet, a multiplexed imaging dataset comprised of two-channel fluorescence images from six distinct tissue types, captured using six different imaging platforms. Our code is publicly available at https://github.com/hula-ai/DAMA. |
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| MeSH term(s) | Brain/diagnostic imaging ; Image Processing, Computer-Assisted/methods ; Supervised Machine Learning ; Humans ; Deep Learning ; Animals ; Algorithms ; Neuroimaging/methods |
| Language | English |
| Publishing date | 2024-03-15 |
| Publishing country | Netherlands |
| Document type | Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't |
| ZDB-ID | 645179-2 |
| ISSN | 1873-2860 ; 0933-3657 |
| ISSN (online) | 1873-2860 |
| ISSN | 0933-3657 |
| DOI | 10.1016/j.artmed.2024.102828 |
| Database | MEDical Literature Analysis and Retrieval System OnLINE |
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