CellSeg3D: self-supervised 3D cell segmentation for light-sheet microscopy
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eLife assessment
This work presents a valuable new approach for self-supervised segmentation for fluorescence microscopy data, which could eliminate time-consuming data labeling and speed up quantitative analysis. The experimental evidence supplied is currently incomplete as the comparison with other methods is only done on a single dataset, lacks common metrics, and could not be easily reproduced for other sample data listed in the manuscript.
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Abstract
Understanding the complex three-dimensional structure of cells is crucial across many disciplines in biology and especially in neuroscience. Here, we introduce a novel 3D self-supervised learning method designed to address the inherent complexity of quantifying cells in 3D volumes, often in cleared neural tissue. We offer a new 3D mesoSPIM dataset and show that CellSeg3D can match state-of-the-art supervised methods. Our contributions are made accessible through a Python package with full GUI integration in napari.
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Author Response:
Reviewer #1 (Public Review):
This work makes several contributions: (1) a method for the self-supervised segmentation of cells in 3D microscopy images, (2) an cell-segmented dataset comprising six volumes from a mesoSPIM sample of a mouse brain, and (3) a napari plugin to apply and train the proposed method.
First, thanks for acknowledging our contributions of a new tool, new dataset, and new software.
(1) Method
This work presents itself as a generalizable method contribution with a wide scope: self-supervised 3D cell segmentation in microscopy images. My main critique is that there is almost no evidence for the proposed method to have that wide of a scope. Instead, the paper is more akin to a case report that shows that a particular self-supervised method is good enough to segment cells in two datasets with …
Author Response:
Reviewer #1 (Public Review):
This work makes several contributions: (1) a method for the self-supervised segmentation of cells in 3D microscopy images, (2) an cell-segmented dataset comprising six volumes from a mesoSPIM sample of a mouse brain, and (3) a napari plugin to apply and train the proposed method.
First, thanks for acknowledging our contributions of a new tool, new dataset, and new software.
(1) Method
This work presents itself as a generalizable method contribution with a wide scope: self-supervised 3D cell segmentation in microscopy images. My main critique is that there is almost no evidence for the proposed method to have that wide of a scope. Instead, the paper is more akin to a case report that shows that a particular self-supervised method is good enough to segment cells in two datasets with specific properties.
First, thanks for acknowledging our contributions of a new tool, new dataset, and new software. We agree we focus on lightsheet microscopy data, therefore to narrow the scope we have changed the title to “CellSeg3D: self-supervised 3D cell segmentation for light-sheet microscopy”.
To support the claim that their method "address[es] the inherent complexity of quantifying cells in 3D volumes", the method should be evaluated in a comprehensive study including different kinds of light and electron microscopy images, different markers, and resolutions to cover the diversity of microscopy images that both title and abstract are alluding to. The main dataset used here (a mesoSPIM dataset of a whole mouse brain) features well-isolated cells that are easily distinguishable from the background. Otsu thresholding followed by a connected component analysis already segments most of those cells correctly.
You have selectively dropped the last part of that sentence that is key: “.... 3D volumes, often in cleared neural tissue” – which is what we tackle. The next sentence goes on to say: “We offer a new 3D mesoSPIM dataset and show that CellSeg3D can match state-of-the-art supervised methods.” Thus, we literally make it clear our claims are on MesoSPIM and cleared data.
The proposed method relies on an intensity-based segmentation method (a soft version of a normalized cut) and has at least five free parameters (radius, intensity, and spatial sigma for SoftNCut, as well as a morphological closing radius, and a merge threshold for touching cells in the post-processing). Given the benefit of tweaking parameters (like thresholds, morphological operation radii, and expected object sizes), it would be illuminating to know how other non-learning-based methods will compare on this dataset, especially if given the same treatment of segmentation post-processing that the proposed method receives. After inspecting the WNet3D predictions (using the napari plugin) on the used datasets I find them almost identical to the raw intensity values, casting doubt as to whether the high segmentation accuracy is really due to the self-supervised learning or instead a function of the post-processing pipeline after thresholding.
First, thanks for testing our tool, and glad it works for you. The deep learning methods we use cannot “solve” this dataset, and we also have a F1-Score (dice) of ~0.8 with our self-supervised method. We don’t see the value in applying non-learning methods; this is unnecessary and beyond the scope of this work.
I suggest the following baselines be included to better understand how much of the segmentation accuracy is due to parameter tweaking on the considered datasets versus a novel method contribution:
* comparison to thresholding (with the same post-processing as the proposed method)
* comparison to a normalized cut segmentation (with the same post-processing as the proposed method)
* comparison to references 8 and 9.Ref 8 and 9 don’t have readily usable (https://github.com/LiangHann/USAR) or even shared code (https://github.com/Kaiseem/AD-GAN), so re-implementing this work is well beyond the bounds of this paper. We benchmarked Cellpose, StartDist, SegResNets, and a transformer – SwinURNet. Moreover, models in the MONAI package can be used. Note, to our knowledge the transformer results also are a new contribution that the Reviewer does not acknowledge.
I further strongly encourage the authors to discuss the limitations of their method. From what I understand, the proposed method works only on well-separated objects (due to the semantic segmentation bottleneck), is based on contrastive FG/BG intensity values (due to the SoftNCut loss), and requires tuning of a few parameters (which might be challenging if no ground-truth is available).
We added text on limitations. Thanks for this suggestion.
(2) Dataset
I commend the authors for providing ground-truth labels for more than 2500 cells. I would appreciate it if the Methods section could mention how exactly the cells were labelled. I found a good overlap between the ground truth and Otsu thresholding of the intensity images. Was the ground truth generated by proofreading an initial automatic segmentation, or entirely done by hand? If the former, which method was used to generate the initial segmentation, and are there any concerns that the ground truth might be biased towards a given segmentation method?
In the already submitted version, we have a 5-page DataSet card that fully answers your questions. They are ALL labeled by hand, without any semi-automatic process.
In our main text we even stated “Using whole-brain data from mice we cropped small regions and human annotated in 3D 2,632 neurons that were endogenously labeled by TPH2-tdTomato” - clearly mentioning it is human-annotated.
(3) Napari plugin
The plugin is well-documented and works by following the installation instructions.
Great, thanks for the positive feedback.
However, I was not able to recreate the segmentations reported in the paper with the default settings for the pre-trained WNet3D: segments are generally too large and there are a lot of false positives. Both the prediction and the final instance segmentation also show substantial border artifacts, possibly due to a block-wise processing scheme.
Your review here does not match your comments above; above you said it was working well, such that you doubt the GT is real and the data is too easy as it was perfectly easy to threshold with non-learning methods.
You would need to share more details on what you tried. We suggest following our code; namely, we provide the full experimental code and processing for every figure, as was noted in our original submission: https://github.com/C-Achard/cellseg3d-figures.
Reviewer #2 (Public Review):
Summary:
The authors propose a new method for self-supervised learning of 3d semantic segmentation for fluorescence microscopy. It is based on a WNet architecture (Encoder / Decoder using a UNet for each of these components) that reconstructs the image data after binarization in the bottleneck with a soft n-cuts clustering. They annotate a new dataset for nucleus segmentation in mesoSPIM imaging and train their model on this dataset. They create a napari plugin that provides access to this model and provides additional functionality for training of own models (both supervised and self-supervised), data labeling, and instance segmentation via post-processing of the semantic model predictions. This plugin also provides access to models trained on the contributed dataset in a supervised fashion.
Strengths:
(1) The idea behind the self-supervised learning loss is interesting.
(2) The paper addresses an important challenge. Data annotation is very time-consuming for 3d microscopy data, so a self-supervised method that yields similar results to supervised segmentation would provide massive benefits.
Thank you for highlighting the strengths of our work and new contributions.
Weaknesses:
The experiments presented by the authors do not adequately support the claims made in the paper. There are several shortcomings in the design of the experiment and presentation of the results. Further, it is unclear if results of similar quality as reported can be achieved within the GUI by non-expert users.
Major weaknesses:
(1) The main experiments are conducted on the new mesoSPIM dataset, which contains quite small and well separated nuclei. It is unclear if the good performance of the novel self-supervised learning method compared to CellPose and StarDist would hold for dataset with other characteristics, such as larger nuclei with a more complex morphology or crowded nuclei.
StarDist is not pretrained, we trained it from scratch as we did for WNet3D. We retrained Cellpose and reported the results both with their pretrained model and our best-retrained model. This is documented in Figure 1 and Suppl. Figure 1. We also want to push back and say that they both work very well on this data. In fact, our main claim is not that we beat them, it is that we can match them with a self-supervised method.
Further, additional preprocessing of the mesoSPIM images may improve results for StarDist and CellPose (see the first point in minor weaknesses). Note: having a method that works better for small nuclei would be an important contribution. But I am uncertain the claims hold for larger and/or more crowded nuclei as the current version of the paper implies.
Figure 2 benchmarks our method on larger and denser nuclei, but we do not intend to claim this is a universal tool. It was specifically designed for light-sheet (brain) data, and we have adjusted the title to be more clear. But we also show in Figure 2 it works well on more dense and noisy samples, hinting that it could be a promising approach. But we agree, as-is, it’s unlikely to be good for extremely dense samples like in electron microscopy, which we never claim it would be.
With regards to preprocessing, we respectfully disagree. We trained StarDist (and asked the main developer of StarDist, Martin Weigert, to check our work and he is acknowledged in the paper) and it does very well. Cellpose we also retrained and optimized and we show it works as-well-as leading transformer and CNN-based approaches. Again, we only claimed we can be as good as these methods with an unsupervised approach.
The contribution of the paper would be stronger if a comparison with StarDist / CellPose was also done on the additional datasets from Figure 2.
We appreciate that more datasets would be ideal, but we always feel it’s best for the authors of tools to benchmark their own tools on data. We only compared others in Figure 1 to the new dataset we provide so people get a sense of the quality of the data too; there we did extensive searches for best parameters for those tools. So while we think it would be nice, we will leave it to those authors to be most fair. We also narrowed the scope of our claims to mesoSPIM data (added light-sheet to the title), which none of the other examples in Figure 2 are.
(2) The experimental setup for the additional datasets seems to be unrealistic. In general, the description of these experiments is quite short and so the exact strategy is unclear from the text. However, you write the following: "The channel containing the foreground was then thresholded and the Voronoi-Otsu algorithm used to generate instance labels (for Platynereis data), with hyperparameters based on the Dice metric with the ground truth." I.e., the hyperparameters for the post-processing are found based on the ground truth. From the description it is unclear whether this is done a) on the part of the data that is then also used to compute metrics or b) on a separate validation split that is not used to compute metrics. If a): this is not a valid experimental setup and amounts to training on your test set. If b): this is ok from an experimental point of view, but likely still significantly overestimates the quality of predictions that can be achieved by manual tuning of these hyperparameters by a user that is not themselves a developer of this plugin or an absolute expert in classical image analysis, see also 3. Note that the paper provides notebooks to reproduce the experimental results. This is very laudable, but I believe that a more extended description of the experiments in the text would still be very helpful to understand the set-up for the reader. Further, from inspection of these notebooks it becomes clear that hyper-parameters where indeed found on the testset (a), so the results are not valid in the current form.
We apologize for this confusion; we have now expanded the methods to clarify the setup is now b; you can see what we exactly did as well in the figure notebook: https://c-achard.github.io/cellseg3d-figures/fig2-b-c-extra-datasets/self-supervised-extra.html#threshold-predictions. For clarity, we additionally link each individual notebook now in the Methods.
(3) I cannot obtain similar results to the ones reported in the manuscript using the plugin. I tried to obtain some of the results from the paper qualitatively: First I downloaded one of the volumes from the mesoSPIM dataset (c5image) and applied the WNet3D to it. The prediction looks ok, however the value range is quite narrow (Average BG intensity ~0.4, FG intensity 0.6-0.7). I try to apply the instance segmentation using "Convert to instance labels" from "Utilities". Using "Voronoi-Otsu" does not work due to an error in pyClesperanto ("clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR"). Segmentation via "Connected Components" and "Watershed" requires extensive manual tuning to get a somewhat decent result, which is still far from perfect.
We are sorry to hear of the installation issue; pyClesperanto is a dependency that would be required to reproduce the images (sounds like you had this issue; https://forum.image.sc/t/pyclesperanto-prototype-doesnt-work/45724 ) We added to our docs now explicitly the fix: https://github.com/AdaptiveMotorControlLab/CellSeg3D/pull/90. We recommend checking the reproduction notebooks (which were linked in initial submission): https://c-achard.github.io/cellseg3d-figures/intro.html.
Then I tried to obtain the results for the Mouse Skull Nuclei Dataset from EmbedSeg. The results look like a denoised version of the input image, not a semantic segmentation. I was skeptical from the beginning that the method would transfer without retraining, due to the very different morphology of nuclei (much larger and elongated). None of the available segmentation methods yield a good result, the best I can achieve is a strong over-segmentation with watersheds.
- We are surprised to hear this; did you follow the following notebook which directly produces the steps to create this figure? (This was linked in preprint): https://c-achard.github.io/cellseg3d-figures/fig2-c-extra-datasets/self-supervised-extra .html
- We have made a video demo for you such that any step that might be unclear is also more clear to a user: (https://youtu.be/U2a9IbiO7nE).
- We also expanded the methods to include the exact values from the notebook into the text.
Minor weaknesses:
(1) CellPose can work better if images are resized so that the median object size in new images matches the training data. For CellPose the cyto2 model should do this automatically. It would be important to report if this was done, and if not would be advisable to check if this can improve results.
We reported this value in Figure 1 and found it to work poorly, that is why we retrained Cellpose and found good performance results (also reported in Figure 1). Resizing GB to TB volumes for mesoSPIM data is otherwise not practical, so simply retraining seems the preferable option, which is what we did.
(2) It is a bit confusing that F1-Score and Dice Score are used interchangeably to evaluate results. The dice score only evaluates semantic predictions, whereas F1-Score evaluates the actual instance segmentation results. I would advise to only use F1-Score, which is the more appropriate metric. For Figure 1f either the mean F1 score over thresholds or F1 @ 0.5 could be reported. Furthermore, I would advise adopting the recommendations on metric reporting from https://www.nature.com/articles/s41592-023-01942-8.
We are using the common metrics in the field for instance and semantic segmentation, and report them in the methods. In Figure 2f we actually report the “Dice” as defined in StarDist (as we stated in the Methods). Note, their implementation is functionally equivalent to F1-Score of an IoU >= 0, so we simply changed this label in the figure now for clarity. We agree this clarifies for the expert readers what was done, and we expanded the methods to be more clear about metrics. We added a link to the paper you mention as well.
(3) A more conceptual limitation is that the (self-supervised) method is limited to intensity-based segmentation, and so will not be able to work for cases where structures cannot be distinguished based on intensity only. It is further unclear how well it can separate crowded nuclei. While some object separation can be achieved by morphological operations this is generally limited for crowded segmentation tasks and the main motivation behind the segmentation objective used in StarDist, CellPose, and other instance segmentation methods. This limitation is only superficially acknowledged in "Note that WNet3D uses brightness to detect objects [...]" but should be discussed in more depth.
Note: this limitation does not mean at all that the underlying contribution is not significant, but I think it is important to address this in more detail so that potential users know where the method is applicable and where it isn't.
We agree, and we added a new section specifically on limitations. Thanks for raising this good point. Thus, while self-supervision comes at the saving of hundreds of manual labor, it comes at the cost of more limited regimes it can work on. Hence why we don’t claim this should replace excellent methods like Cellpose or Stardist, but rather complement them and can be used on mesoSPIM samples, as we show here.
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eLife assessment
This work presents a valuable new approach for self-supervised segmentation for fluorescence microscopy data, which could eliminate time-consuming data labeling and speed up quantitative analysis. The experimental evidence supplied is currently incomplete as the comparison with other methods is only done on a single dataset, lacks common metrics, and could not be easily reproduced for other sample data listed in the manuscript.
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Reviewer #1 (Public Review):
This work makes several contributions: (1) a method for the self-supervised segmentation of cells in 3D microscopy images, (2) an cell-segmented dataset comprising six volumes from a mesoSPIM sample of a mouse brain, and (3) a napari plugin to apply and train the proposed method.
(1) Method
This work presents itself as a generalizable method contribution with a wide scope: self-supervised 3D cell segmentation in microscopy images. My main critique is that there is almost no evidence for the proposed method to have that wide of a scope. Instead, the paper is more akin to a case report that shows that a particular self-supervised method is good enough to segment cells in two datasets with specific properties.
To support the claim that their method "address[es] the inherent complexity of quantifying cells in 3D …
Reviewer #1 (Public Review):
This work makes several contributions: (1) a method for the self-supervised segmentation of cells in 3D microscopy images, (2) an cell-segmented dataset comprising six volumes from a mesoSPIM sample of a mouse brain, and (3) a napari plugin to apply and train the proposed method.
(1) Method
This work presents itself as a generalizable method contribution with a wide scope: self-supervised 3D cell segmentation in microscopy images. My main critique is that there is almost no evidence for the proposed method to have that wide of a scope. Instead, the paper is more akin to a case report that shows that a particular self-supervised method is good enough to segment cells in two datasets with specific properties.
To support the claim that their method "address[es] the inherent complexity of quantifying cells in 3D volumes", the method should be evaluated in a comprehensive study including different kinds of light and electron microscopy images, different markers, and resolutions to cover the diversity of microscopy images that both title and abstract are alluding to.
The main dataset used here (a mesoSPIM dataset of a whole mouse brain) features well-isolated cells that are easily distinguishable from the background. Otsu thresholding followed by a connected component analysis already segments most of those cells correctly. The proposed method relies on an intensity-based segmentation method (a soft version of a normalized cut) and has at least five free parameters (radius, intensity, and spatial sigma for SoftNCut, as well as a morphological closing radius, and a merge threshold for touching cells in the post-processing). Given the benefit of tweaking parameters (like thresholds, morphological operation radii, and expected object sizes), it would be illuminating to know how other non-learning-based methods will compare on this dataset, especially if given the same treatment of segmentation post-processing that the proposed method receives. After inspecting the WNet3D predictions (using the napari plugin) on the used datasets I find them almost identical to the raw intensity values, casting doubt as to whether the high segmentation accuracy is really due to the self-supervised learning or instead a function of the post-processing pipeline after thresholding.
I suggest the following baselines be included to better understand how much of the segmentation accuracy is due to parameter tweaking on the considered datasets versus a novel method contribution:
* comparison to thresholding (with the same post-processing as the proposed method)
* comparison to a normalized cut segmentation (with the same post-processing as the proposed method)
* comparison to references 8 and 9.I further strongly encourage the authors to discuss the limitations of their method. From what I understand, the proposed method works only on well-separated objects (due to the semantic segmentation bottleneck), is based on contrastive FG/BG intensity values (due to the SoftNCut loss), and requires tuning of a few parameters (which might be challenging if no ground-truth is available).
(2) Dataset
I commend the authors for providing ground-truth labels for more than 2500 cells. I would appreciate it if the Methods section could mention how exactly the cells were labelled. I found a good overlap between the ground truth and Otsu thresholding of the intensity images. Was the ground truth generated by proofreading an initial automatic segmentation, or entirely done by hand? If the former, which method was used to generate the initial segmentation, and are there any concerns that the ground truth might be biased towards a given segmentation method?
(3) Napari plugin
The plugin is well-documented and works by following the installation instructions. However, I was not able to recreate the segmentations reported in the paper with the default settings for the pre-trained WNet3D: segments are generally too large and there are a lot of false positives. Both the prediction and the final instance segmentation also show substantial border artifacts, possibly due to a block-wise processing scheme.
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Reviewer #2 (Public Review):
Summary:
The authors propose a new method for self-supervised learning of 3d semantic segmentation for fluorescence microscopy. It is based on a WNet architecture (Encoder / Decoder using a UNet for each of these components) that reconstructs the image data after binarization in the bottleneck with a soft n-cuts clustering. They annotate a new dataset for nucleus segmentation in mesoSPIM imaging and train their model on this dataset. They create a napari plugin that provides access to this model and provides additional functionality for training of own models (both supervised and self-supervised), data labeling, and instance segmentation via post-processing of the semantic model predictions. This plugin also provides access to models trained on the contributed dataset in a supervised fashion.
Strengths:
(1) …
Reviewer #2 (Public Review):
Summary:
The authors propose a new method for self-supervised learning of 3d semantic segmentation for fluorescence microscopy. It is based on a WNet architecture (Encoder / Decoder using a UNet for each of these components) that reconstructs the image data after binarization in the bottleneck with a soft n-cuts clustering. They annotate a new dataset for nucleus segmentation in mesoSPIM imaging and train their model on this dataset. They create a napari plugin that provides access to this model and provides additional functionality for training of own models (both supervised and self-supervised), data labeling, and instance segmentation via post-processing of the semantic model predictions. This plugin also provides access to models trained on the contributed dataset in a supervised fashion.
Strengths:
(1) The idea behind the self-supervised learning loss is interesting.
(2) The paper addresses an important challenge. Data annotation is very time-consuming for 3d microscopy data, so a self-supervised method that yields similar results to supervised segmentation would provide massive benefits.
Weaknesses:
The experiments presented by the authors do not adequately support the claims made in the paper. There are several shortcomings in the design of the experiment and presentation of the results. Further, it is unclear if results of similar quality as reported can be achieved within the GUI by non-expert users.
Major weaknesses:
(1) The main experiments are conducted on the new mesoSPIM dataset, which contains quite small and well separated nuclei. It is unclear if the good performance of the novel self-supervised learning method compared to CellPose and StarDist would hold for dataset with other characteristics, such as larger nuclei with a more complex morphology or crowded nuclei. Further, additional preprocessing of the mesoSPIM images may improve results for StarDist and CellPose (see the first point in minor weaknesses). Note: having a method that works better for small nuclei would be an important contribution. But I am uncertain the claims hold for larger and/or more crowded nuclei as the current version of the paper implies. The contribution of the paper would be stronger if a comparison with StarDist / CellPose was also done on the additional datasets from Figure 2.
(2) The experimental setup for the additional datasets seems to be unrealistic. In general, the description of these experiments is quite short and so the exact strategy is unclear from the text. However, you write the following: "The channel containing the foreground was then thresholded and the Voronoi-Otsu algorithm used to generate instance labels (for Platynereis data), with hyperparameters based on the Dice metric with the ground truth." I.e., the hyperparameters for the post-processing are found based on the ground truth. From the description it is unclear whether this is done a) on the part of the data that is then also used to compute metrics or b) on a separate validation split that is not used to compute metrics. If a): this is not a valid experimental setup and amounts to training on your test set. If b): this is ok from an experimental point of view, but likely still significantly overestimates the quality of predictions that can be achieved by manual tuning of these hyperparameters by a user that is not themselves a developer of this plugin or an absolute expert in classical image analysis, see also 3. Note that the paper provides notebooks to reproduce the experimental results. This is very laudable, but I believe that a more extended description of the experiments in the text would still be very helpful to understand the set-up for the reader. Further, from inspection of these notebooks it becomes clear that hyper-parameters where indeed found on the testset (a), so the results are not valid in the current form.
(3) I cannot obtain similar results to the ones reported in the manuscript using the plugin. I tried to obtain some of the results from the paper qualitatively: First I downloaded one of the volumes from the mesoSPIM dataset (c5image) and applied the WNet3D to it. The prediction looks ok, however the value range is quite narrow (Average BG intensity ~0.4, FG intensity 0.6-0.7). I try to apply the instance segmentation using "Convert to instance labels" from "Utilities". Using "Voronoi-Otsu" does not work due to an error in pyClesperanto ("clGetPlatformIDs failed: PLATFORM_NOT_FOUND_KHR"). Segmentation via "Connected Components" and "Watershed" requires extensive manual tuning to get a somewhat decent result, which is still far from perfect.
Then I tried to obtain the results for the Mouse Skull Nuclei Dataset from EmbedSeg. The results look like a denoised version of the input image, not a semantic segmentation. I was skeptical from the beginning that the method would transfer without retraining, due to the very different morphology of nuclei (much larger and elongated). None of the available segmentation methods yield a good result, the best I can achieve is a strong over-segmentation with watersheds.
Minor weaknesses:
(1) CellPose can work better if images are resized so that the median object size in new images matches the training data. For CellPose the cyto2 model should do this automatically. It would be important to report if this was done, and if not would be advisable to check if this can improve results.
(2) It is a bit confusing that F1-Score and Dice Score are used interchangeably to evaluate results. The dice score only evaluates semantic predictions, whereas F1-Score evaluates the actual instance segmentation results. I would advise to only use F1-Score, which is the more appropriate metric. For Figure 1f either the mean F1 score over thresholds or F1 @ 0.5 could be reported. Furthermore, I would advise adopting the recommendations on metric reporting from https://www.nature.com/articles/s41592-023-01942-8.
(3) A more conceptual limitation is that the (self-supervised) method is limited to intensity-based segmentation, and so will not be able to work for cases where structures cannot be distinguished based on intensity only. It is further unclear how well it can separate crowded nuclei. While some object separation can be achieved by morphological operations this is generally limited for crowded segmentation tasks and the main motivation behind the segmentation objective used in StarDist, CellPose, and other instance segmentation methods. This limitation is only superficially acknowledged in "Note that WNet3D uses brightness to detect objects [...]" but should be discussed in more depth.
Note: this limitation does not mean at all that the underlying contribution is not significant, but I think it is important to address this in more detail so that potential users know where the method is applicable and where it isn't.
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