Label-Efficient Deep Color Deconvolution of Brightfield Multiplex IHC Images

Area of Science:

• Computational pathology
• Biomedical imaging analysis
• Machine learning in histology

Background:

• Multiplex immunohistochemistry (mIHC) enables simultaneous protein biomarker detection on single tissue sections.
• Analyzing mIHC images requires accurate color deconvolution to distinguish multiple stains.
• Increasing multiplexing levels complicates traditional deconvolution methods.

Purpose of the Study:

• To develop advanced computational methods for robust mIHC color deconvolution.
• To leverage deep learning for improved stain unmixing and segmentation in high-plex IHC images.
• To address the challenges posed by increasing numbers of multiplexed stains.

Main Methods:

• Implementation of self-supervised and semi-supervised deep learning models.
• Utilizing deep convolutional autoencoders for stain unmixing.
• Employing physics-inspired reconstruction losses and weak annotations with unlabeled data.

Main Results:

• Demonstrated reliable unmixing of multiplexed stains in 7-plex IHC images.
• Generated accurate stain segmentation maps.
• Showcased the effectiveness of integrating unlabeled data and weak annotations.

Conclusions:

• Proposed deep learning approaches significantly enhance mIHC color deconvolution.
• The methods offer a scalable solution for analyzing complex, high-plex IHC data.
• This work advances computational pathology for better understanding of tumor microenvironments.