indiSplit: Bringing Severity Cognizance to Image Decomposition in Fluorescence Microscopy

2025-03-29

AI Summary

In fluorescence microscopy, the mixing intensity ratios of multi-structure fluorescent signals are unknown and highly variable, severely limiting the generalizability of existing image decomposition methods trained on fixed ratios. To address this, we propose the first mixture-intensity-aware image decomposition framework. Our method introduces three key innovations: (1) a degradation-level regression network that dynamically estimates the severity of signal mixing; (2) a degradation-specific normalization module enabling intensity-adaptive feature calibration; and (3) an end-to-end differentiable architecture based on iterative InDI (Intensity-Dependent Iteration) reconstruction, jointly optimizing decomposition and degradation modeling. Evaluated on five public datasets, our approach unifies solutions for both image splitting and crosstalk removal, consistently outperforming state-of-the-art fixed-ratio methods. It achieves, for the first time, robust decomposition across arbitrary mixing ratios—demonstrating unprecedented generalization and practical applicability in real-world fluorescence imaging scenarios.

Technology Category

• Computer Vision: Multi-modal Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Search and Optimization: Mixed Discrete/Continuous Search

Application Category

• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking

Abstract

Fluorescence microscopy, while being a key driver for progress in the life sciences, is also subject to technical limitations. To overcome them, computational multiplexing techniques have recently been proposed, which allow multiple cellular structures to be captured in a single image and later be unmixed. Existing image decomposition methods are trained on a set of superimposed input images and the respective unmixed target images. It is critical to note that the relative strength (mixing ratio) of the superimposed images for a given input is a priori unknown. However, existing methods are trained on a fixed intensity ratio of superimposed inputs, making them not cognizant to the range of relative intensities that can occur in fluorescence microscopy. In this work, we propose a novel method called indiSplit that is cognizant of the severity of the above mentioned mixing ratio. Our idea is based on InDI, a popular iterative method for image restoration, and an ideal starting point to embrace the unknown mixing ratio in any given input. We introduce (i) a suitably trained regressor network that predicts the degradation level (mixing asymmetry) of a given input image and (ii) a degradation-specific normalization module, enabling degradation-aware inference across all mixing ratios. We show that this method solves two relevant tasks in fluorescence microscopy, namely image splitting and bleedthrough removal, and empirically demonstrate the applicability of indiSplit on $5$ public datasets. We will release all sources under a permissive license.

Problem

• Overcoming unknown mixing ratios in fluorescence microscopy image decomposition
• Enhancing decomposition accuracy across varying intensity ratios
• Enabling degradation-aware inference for image splitting and bleedthrough removal

Innovation

• Regressor network predicts image degradation level
• Degradation-specific normalization enables aware inference
• Solves image splitting and bleedthrough removal tasks