Title: scSplit: Bringing Severity Cognizance to Image Decomposition in Fluorescence Microscopy
Venue: NeurIPS 2025
Accepted: yes
Keywords: semantic unmixing

image decomposition

Fluorescence microscopy

Computer vision

inverse problem
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 of the range of relative intensities that can occur in fluorescence microscopy. In this work, we propose a novel method called scSplit 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 scSplit on 5 public datasets. The source code with pre-trained models is hosted at https://github.com/juglab/scSplit/.


Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis paper proposes a novel framework, indiSplit, to decompose fluorescence microscopy images containing mixed structures. The authors observe that in real-world scenarios, the mixing ratio of components can vary across images. To address this, they design a two-stage architecture: a Reg model estimates the mixing ratio, and a Gen model then predicts the individual components conditioned on this ratio. Additionally, an auxiliary normalization step is introduced to refine the mixed image, which originally has a variance as a function of the mixing ratio. The indiSplit framework outperforms prior approaches on relevant benchmarks and demonstrates potential generalizability to natural image tasks such as motion deblurring.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\n1. The idea of explicitly modeling the varying mixing ratios in composed images is novel and insightful for this task.\n\n2. The proposed normalization mechanism helps align the distribution of mixed images, which empirically improves performance.\n\n3. Decomposing the problem into two sub-tasks (mix ratio estimation and conditional generation) is a meaningful design choice and leads to improved separation quality.\n\nWeaknesses:\nMajor:\n1. It remains unclear why a single unified network could not implicitly learn the mixing ratio and perform separation directly. Typically, multi-stage pipelines are introduced when data scarcity limits end-to-end learning.\n\n2. The method appears to require retraining if the component types change. For example, if a model is trained to separate mixed types $C_0$ and $C_1$, can it generalize to mixed types $C_0$ and $C_2$? This raises concerns about generalizability and scalability.\n\n3. Related to the above, the framework lacks flexibility: different combinations of components require retraining the full model.\n\n4. The inference-time normalization using precomputed statistics indexed by the mean output of the Reg model is underexplained. If the input variance also depends on the mixing ratio, what is the theoretical basis for assuming this strategy leads to unit variance after normalization? A mathematical justification would be helpful.\n\n5. There is ambiguity around the meaning of t. While initially introduced as the mix ratio, Section 3.3 refers to t as time in the “Time Regressor Network Reg,” which is confusing and should be clarified.\n\nMinor:\n1. In the abstract, please clarify what is meant by \"technical limitations\" in the first sentence.\n\n2. Define the term superposition severity, as it is not a standard expression in the literature.\n\n3. Consider writing Bleedthrough in lowercase or using quotation marks to emphasize it.\n\n4. The introduction should explicitly mention that the proposed framework is inspired by InDI.\n\n5. In Section 3.2, specify the data type of the input images (e.g., uint16) to clarify the range and interpretation of pixel intensities.\n\n6. The definition of $C_i$​ appears redundant with the earlier definition of $C_0$​ and $C_1$.\n\n7. There is no definition provided for $\\delta$​; this should be clearly introduced.\n\n8. Please standardize the spelling of \"artifact\" vs. \"artefact\" throughout the manuscript.\n\nQUESTIONS:\n1. Why was 100 chosen as the number of intervals in Section 3.2? Is this value optimal or empirically determined?\n\n2. Please address whether the two-stage pipeline is strictly necessary, or whether an end-to-end model with sufficient training data could perform equally well (related to Weakness 1).\n\n3. Can the model generalize to unseen combinations of component types, or does it require retraining for every new pair (related to Weakness 2)?\n\n4. Can you theoretically or empirically justify the proposed normalization strategy in inference? Given that ground truth is available in your experiments, use it to verify that your normalization leads to the desired variance behavior (related to Weakness 4).\n\n5. What is the precise meaning of t? Is it a mix ratio, a time variable, or both? Please clarify (related to Weakness 5).\n\nLIMITATIONS:\nThe paper should clearly state that the current approach requires retraining when the types of mixed components change.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 2, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-10"}

comments: SUMMARY:
This article addresses the issue of limited observable cell structures in a single fluorescence microscopy image and proposes a method named indiSplit by introducing a regression network to predict mixing proportions and a degradation-specific normalization module. This method solves the tasks of image decomposition and crosstalk elimination, with its effectiveness validated on multiple public datasets.

STRENGTHS AND WEAKNESSES:
### Strengths  
The article primarily introduces two modules: mixing ratio perception and degradation-specific normalization. Through a regression network and an aggregation module, the method explicitly models the changes in superimposed intensity, addressing the limitation of traditional methods that assume a fixed mixing ratio. Normalization is employed to ensure consistent input data distribution under different mixing proportions.  


### Weakness  
Please refer to my detailed comments.

QUESTIONS:
1. The input image c_t is generated by linearly superimposing two structural images c_0 and  c_1  with a mixing proportion t in [0,1] , i.e.,  c_t = (1-t)c_0 + tc_1. Why is the superposition implemented as an addition here? Typically, superposition often involves multiplying two textures.  
2. Has the author conducted experiments to prove that different mixing proportions significantly affect the results?  
3. Is the method applicable to multi-layer superposition?  
4. If interference occurs between superimposed textures, does the method in the article remain applicable?  
5. It is recommended to split the performance comparison in Table 1 and the model's research on mixing parameters into two separate tables.

LIMITATIONS:
Please refer to my questions part.
TITLE: Official Review
CLARITY: 2
IMPACT: 3
ORIGINALITY: 2
DATE: 2025-06-23

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThe authors introduce **indiSplit**, a novel computational method for fluorescence microscopy image decomposition, specifically designed to handle unknown mixing ratios between channels in multiplexed images. The paper proposes a potentially useful method, but I have significant doubts about its evaluation methodology due to reliance on metrics suited for different tasks and unrealistic test data derived from blending itself.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\nThe paper builds upon an existing technique by adding a regressor network that predicts the severity of the mixing ratio and a normalisation module based on this prediction, allowing degradation-aware splitting and bleed-through removal across various intensity conditions.\n\nMajor Weaknesses:\n\n1. The paper primarily uses standard image quality metrics like PSNR (Peak Signal-to-Noise Ratio), SSIM (Structural Similarity Index), and LPIPS (Learned Perceptual Image Patch Similarity). However, these metrics fundamentally assess how closely an output resembles a specific *target* image. As a result, the authors do not measure **channel separation** effectively. The task requires direct bleed-through reduction in the biological signal, which is not represented in any of the datasets used. The task should be measured by how accurately the models are unmixing overlapping signals to reveal distinct cellular structures and quantify markers independently. PSNR/SSIM/LPIPS fail to capture this because these metrics measure quality and therefore bear secondary importance.\n\n2.  The evaluation was performed on synthetic blended image datasets, which do not reflect the reality of unmixing unknown bleed-through from actual experimental acquisitions with variable intensities. This is likely an unrealistic assessment of performance in a real-world microscopy context where mixing ratios are unpredictable.\n\n3.  It's unclear why absolute pixel values (as opposed to relative signal contributions or normalised intensity) are used as the basis for\nevaluation, given their potential variability due to acquisition settings. In microscopy, raw intensity levels depend heavily on acquisition settings and can fluctuate naturally.\n\nQUESTIONS:\nAre the metrics you used adequate to achieve unmixing? Can you propose another metric that would measure that, for example, a downstream segmentation metric?\n\nLIMITATIONS:\nPlease clearly describe the practical problem that the task you are interested in is supposed to solve. Mention limitations of the metrics in assessing how well the task is addressed. Mention clearly how practical the datasets are that you are using to train and test the model. What are the limitations?", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 3, "DATE": "2025-06-25"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis work introduces a new method, indiSplit, to recover probe-specific signals from fluorescence microscopy images in which two signals have been mixed. Such digital demultiplexing methods could overcome technical limitations in fluorescence microscopy, by allowing for effective “overloading” of individual channels with multiple (instead of just one) structure-specific probes, thereby increasing the number of cellular structures that can be captured and cleanly related through multiplexed image acquisition pipelines. In a well-structured paper, the authors present the roots of their method, introduce technical modifications, and empirical evidence across several relevant datasets, along with numerous technical controls, that generally support their merit. However, certain methodological aspects remain unclear, one key ablation is missing, and the practical significance of indiSplit may be modest.\n\nSTRENGTHS AND WEAKNESSES:\n**Strengths:**  \n\n1. **A well-presented paper:** The authors present indiSplit in a well-written manuscript, with high-quality figures that clearly communicate motivation, reasoning, experimental design and results. Conclusions are well-calibrated to the level of empirical evidence provided.  \n2. **Clear application target:** The method has a clear real-world potential to address a concrete limitation in fluorescence microscopy, one of the most foundational and widely used techniques in biomedical research.  \n3. **High degree of experimental rigor:** Experimental include relevant technical controls which benefits attribution of empirical improvements to their method.  \n\n**Weaknesses:**  \n\n1. **Normalization module vs instance norm:** While the authors reasoning for the normalization module is clear, and the implementation effective, it appears that, to ensure $σ^2=1$ for input images during training, instead of precomputing a statistical memory bank, the same effect could be trivially achieved by applying instance normalization (i.e. using intrinsic statistics for normalization) to any input $c_t$ on the fly. If there are compelling technical reason against this, they should be discussed and empirically validated. While the use of instance normalization is well-established in computer vision, and in particular for fluorescence microscopy applications in which absolute intensity variations between technical replicates is assumed to be a major source of noise (e.g. cellular profiling [1]), documenting its empirical benefit in this context would still be useful. \n\n2. **Ablation of normalization module is missing:** Experiments to delineate the specific impact of ensuring $σ^2=1$ for any $c_t$ (as opposed to the impact of the time regressor module) are missing. \n\n3. **Unclear to what extent indiSplit is iterative:** The authors explain their study as building upon “InDI: Image Restoration by Direct *Iteration*” [2], but it remains unclear to what extent indiSplit, as well as the authors implementation of InDI for that matter, actually implements an iterative process of image of image restoration. This seems rather significant, because InDI’s primary innovation is exactly the iterative, stepwise recovery of source images in ill-posed settings. Method, experimental, and Appendix provide no information on this regarding indiSplit; Fig. 1 seems to show a one-shot restoration process. Moreover, the use of the Reg network seems more compatible with a single step of inference. In InDI, models learn to restore images over a consistent number of steps N, i.e. t=1 is implicitly set for any input image during inference and the image is restored over N steps of size δ. If indiSplit is indeed iterative in this sense, then it would seem that for any estimated $t_{agg}$<1, either N or δ would need to be adjusted dynamically. Whether or how this is done is unclear. \n\n4. **Generalization seems uncertain:** While indiSplit outperforms a strong set of baselines by substantial margins over datasets in which channels are artificially mixed, no clear performance advantage is apparent for real images. Combined with the fact that indiSplit seems to require two task-specific models to be trained for any particular unmixing setup (i.e. models trained to unmix some channels 1 and 2 are not expected to work for channels 1 and 3), and that these may well fail to generalize to images (with matched probes) collected on difference microscopes, the practical significance of indiSplit will likely be modest.   \n\n5. **Minor weaknesses:** There are some minor spelling mistakes. The authors may further want to sharpen their description of the distinction of natural vs. fluorescence microscopy images with respect to their value-distributions / histograms. In particular, the sentence in line 128 “For instance, it is common to have the maximum pixel intensity for a noisy acquisition to be less than where as [spelling mistake] the pixel intensities can easily be larger than 4000 for less noisy acquisitions.” should be refined to take account of varying image bit-depths (often 8-bit for natural images, hence dividing by 255 makes sense, but commonly 12- or 16-bit for microscope cameras).    \n\n[1] Pernice, W.M., Doron, M., Quach, A., Pratapa, A., Kenjeyev, S., De Veaux, N., Hirano, M. and Caicedo, J.C., 2023. Out of distribution generalization via interventional style transfer in single-cell microscopy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 4326-4335).  \n[2] Delbracio, M. and Milanfar, P., 2023. Inversion by direct iteration: An alternative to denoising diffusion for image restoration. arXiv preprint arXiv:2303.11435.\n\nQUESTIONS:\n1. To address weakness (1), I would ask that the authors address the relationship of the normalization module to instance norm / intrinsic normalizations of $c_t$ should be added. \n2. Weakness (3) could be addressed by adding sufficient detail to the text or appendix to clarify the extent to which indiSplit is iterative. \n3. The authors should affirm that InDI was faithfully implemented, i.e. as an iterative system. If not, the authors should clearly highlight this (including changing the acronym), and further explicitly delineate how indiSplit is different from their InDI implementation.   \n4. Depending on the difference between InDI and indiSplit, the authors should consider extending ablation experiments (weakness (2)) around the normalization question to other baselines. For example, for baselines trained by the authors, and in particular for InDI, instance norm of $c_t$ (a very cheap operation) could be explored.   \n5. If the authors could provide additional empirical data on the extent to which indiSplit generalizes to real images, ideally collected under relevant potential out-of-distribution settings (i.e. different technical circumstances such as a different microscope), that could substantially strengthen the significance of the work.   \n\nI expect addressing most of these key weaknesses to be feasible, and I would gladly increase my scores in response. I further extend my sincere apologies to the authors for the late submission of this review.\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 1, "ORIGINALITY": 3, "DATE": "2025-07-04"}