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Abstract
Automated sperm morphology analysis plays a crucial role in the assessment of male fertility, yet its efficacy is often compromised by the challenges in accurately segmenting sperm images. Existing segmentation techniques, including the Segment Anything Model (SAM), are notably inadequate in addressing the complex issue of sperm overlap—a frequent occurrence in clinical samples. Our exploratory studies reveal that modifying image characteristics by removing sperm heads and easily segmentable areas, alongside enhancing the visibility of overlapping regions, markedly enhances SAM’s efficiency in segmenting intricate sperm structures. Motivated by these findings, we present the Cascade SAM for Sperm Segmentation (CS3), an unsupervised approach specifically designed to tackle the issue of sperm overlap. This method employs a cascade application of SAM to segment sperm heads, simple tails, and complex tails in stages. Subsequently, these segmented masks are meticulously matched and joined to construct complete sperm masks. In collaboration with leading medical institutions, we have compiled a dataset comprising approximately 2,000 unlabeled sperm images to fine-tune our method, and secured expert annotations for an additional 240 images to facilitate comprehensive model assessment. Experimental results demonstrate superior performance of CS3 compared to existing methods.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2024/paper/0747_paper.pdf
SharedIt Link: pending
SpringerLink (DOI): pending
Supplementary Material: https://papers.miccai.org/miccai-2024/supp/0747_supp.pdf
Link to the Code Repository
Link to the Dataset(s)
N/A
BibTex
@InProceedings{Shi_CS3_MICCAI2024,
author = { Shi, Yi and Tian, Xu-Peng and Wang, Yun-Kai and Zhang, Tie-Yi and Yao, Bing and Wang, Hui and Shao, Yong and Wang, Cen-Cen and Zeng, Rong and Zhan, De-Chuan},
title = { { CS3: Cascade SAM for Sperm Segmentation } },
booktitle = {proceedings of Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
year = {2024},
publisher = {Springer Nature Switzerland},
volume = {LNCS 15003},
month = {October},
page = {pending}
}
Reviews
Review #1
- Please describe the contribution of the paper
The paper introduces a new unsupervised method for sperm image segmentation called Cascade SAM for Sperm Segmentation (CS3). This approach addresses the challenge of accurately segmenting overlapping sperm structures. The main contributions are: (1)Identification of three limitations of the existing Segment Anything Model (SAM) in sperm segmentation and proposing solutions. (2)Development of CS3, an unsupervised method that uses a cascade of SAM applications to progressively segment complex sperm images. (3)Demonstration of superior segmentation performance of CS3 over existing methods, particularly in handling overlapping sperm instances.
- Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
（1）Novel Formulation: The CS3 algorithm introduces a novel cascade application of the Segment Anything Model (SAM), specifically tailored for the segmentation of overlapping sperm structures. This cascade approach is innovative in that it applies multiple stages of segmentation to progressively isolate and refine the masks for sperm heads and tails. This method allows for a more precise and comprehensive segmentation than existing single-stage methods. （2）Novel Application in a Medical Context: While image segmentation technologies are widely used, their application to sperm image segmentation, particularly for handling overlapping structures, is relatively unexplored. CS3’s adaptation of SAM for this purpose is both novel and significant, offering potential improvements in automated sperm analysis.
- Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
（1）Computational Efficiency: The paper does not discuss the computational requirements or efficiency of the CS3 method. Given the multiple stages of the cascade process and the iterative application of SAM, it is possible that CS3 could be computationally intensive, which might limit its practical deployment in settings with limited computational resources. （2）Limited Handling of Highly Complex Overlaps: Although CS3 significantly improves the segmentation of overlapping sperm, it struggles with scenarios where sperm overlaps are excessively complex (e.g., more than ten sperm intertwined). This limitation might reduce the applicability of the method in certain clinical settings where high-density sperm samples are common. The paper acknowledges this limitation, suggesting that adequate sample preparation is still necessary to reduce complexity before analysis. （3）The article uses a large model as the main structure and is less innovative in the network model.
- Please rate the clarity and organization of this paper
Very Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The authors claimed to release the source code and/or dataset upon acceptance of the submission.
- Do you have any additional comments regarding the paper’s reproducibility?
No
- Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review. Pay specific attention to the different assessment criteria for the different paper categories (MIC, CAI, Clinical Translation of Methodology, Health Equity): https://conferences.miccai.org/2024/en/REVIEWER-GUIDELINES.html
(1)Conduct ablation experiments: Systematically study the impact of the number of cascade stages on model performance. This can be achieved by fixing other conditions and only changing the number of layers of the cascade. (2)Detailed documentation of the choice of cascade levels: Detailed documentation in the experimental section of the cascade levels chosen when processing different image sets and how these choices affect the segmentation results. (3)Resource analysis of the overall structure: Analyze the specific consumption of the overall architecture training, such as hardware resources (GPU size), training time or parameter size of the overall model and other information.
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making
Weak Accept — could be accepted, dependent on rebuttal (4)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
The article provides a novel training architecture, which achieves a substantial improvement in segmentation performance. However, the article does not provide detailed data on cascade levels or conduct corresponding ablation studies to analyze the impact of different cascade levels on segmentation effects.
- Reviewer confidence
Somewhat confident (2)
- [Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed
N/A
- [Post rebuttal] Please justify your decision
N/A
Review #2
- Please describe the contribution of the paper
The paper proposes Cascade SAM for Sperm Segmentation (CS3) an unsupervised approach specifically designed to tackle the problem of sperm overlap in sperm images. Instance segmentation of overlapping sperm cell tails in microscopic imaging is a novel problem in the domain of sperm cell segmentation.
- Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
The paper proposes Cascade SAM for Sperm Segmentation (CS3) an unsupervised approach specifically designed to tackle the problem of sperm overlap in sperm images. Instance segmentation of overlapping sperm cell tails in microscopic imaging is a novel problem in the domain of sperm cell segmentation.
The paper identified three limitations of SAM in sperm segmentation and provides actionable solutions.
The paper uses an existing segmentation model SAM but introduce a recursive method of applying the model.
Generated segmented masks for instances of sperm heads and instances of sperm tails are meticulously matched to construct complete sperm masks.
The instance segmentation tasks of the SAMs S_1, S_2, …, S_n well defined (e.g. S_1 for instance segmentation of sperm cell heads, S_2, …, S_n for recursive instance segmentation of sperm tails from simple untangled tails to more complex overlapping tails.
- Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
A quantitative measure to determine when to stop the recursive SAM segmentation is missing from the paper. The statement on page 5 “This cascade process persists until SAM’s segmentation outputs remain consistent across two successive rounds” suggests authors used a qualitative (by only visual observation) metric for determining when two successive segmentation outputs are consistent, thereby terminating the recursive segmentations. The use of a quantitative measure for segmentation output consistency could have reduced the frequency of manual input and handcrafting in the proposed unsupervised method CS3.
The enlargement and bold method used to resolve cases of sperm tail overlap which are not resolved by the model sequence (S_1, S_2, …, S_n) seems to involve significant handcrafting and manual inputs. It would have been beneficial to describe an automated quantitative method for identifying the presence of intertwined tails and applying the enlargement and bold operation without human intervention.
It is also not clear whether the enlargement and bold method is applied somewhere within the sequence S_2, S_3, …, S_n or after S_n.
The authors states on page 5: “…a marginal subset of these overlaps presents a notable challenge, resisting separation through cascade processing.” It would have been preferable if the authors quantify the percentage of overlap instances which were not resolved by the cascading processes of CS3 and require the enlargement and bold method. A numerical (percentage) estimation should be used in place of the “marginal subset” description.
2,000 unlabelled images were used in this paper, it is not clear that the proposed unsupervised learning method direct learned anything from the images. It seems that the SAM models’ parameters (weights) when the 2,000th image was fed to them is the same as when it the first image. Does CS3 get better at segmenting with more sperm images fed to it? If not, then should CS3 be described as an unsupervised “learning” method rather than an unsupervised method? Is this the same for the 240 annotated images?
- Please rate the clarity and organization of this paper
Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission has provided an anonymized link to the source code, dataset, or any other dependencies.
- Do you have any additional comments regarding the paper’s reproducibility?
N/A
- Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review. Pay specific attention to the different assessment criteria for the different paper categories (MIC, CAI, Clinical Translation of Methodology, Health Equity): https://conferences.miccai.org/2024/en/REVIEWER-GUIDELINES.html
The use of a quantitative measure for segmentation output consistency could have reduced the frequency of manual input and handcrafting in the proposed unsupervised method CS3.
It would have been beneficial to describe an automated quantitative method for identifying the presence of intertwined tails and applying the enlargement and bold operation without human intervention.
It would have been preferable if the authors quantify the percentage of overlap instances which were not resolved by the cascading processes of CS3 and require the enlargement and bold method. A numerical (percentage) estimation should be used in place of the “marginal subset” description.
Spelling error on page 7 (under “Comparison methods): “The fist type centers…” should be “The first type centers…”
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making
Weak Accept — could be accepted, dependent on rebuttal (4)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
Though CS3 is described as an unsupervised learning method for segmentation in that it does not require annotations or labels, however it seems to require significant manual human input to perform the task of instance segmentation of overlapping sperm tails in microscopic images.
- Reviewer confidence
Confident but not absolutely certain (3)
- [Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed
N/A
- [Post rebuttal] Please justify your decision
N/A
Review #3
- Please describe the contribution of the paper
The paper introduces the “Cascade SAM for Sperm Segmentation” (CS3), a novel, unsupervised method designed to improve the segmentation of overlapping sperm structures in microscopic images—a significant challenge in automated sperm morphology analysis. CS3 employs a cascading application of the Segment Anything Model (SAM) to sequentially segment different components of the sperm image, specifically addressing the segmentation of complex overlapping tails and heads. This method allows for the precise isolation and reconstruction of complete sperm structures without the need for labeled training data, presenting a major advancement over existing segmentation techniques that struggle with the complexities of overlapping sperm instances.
- Please list the main strengths of the paper; you should write about a novel formulation, an original way to use data, demonstration of clinical feasibility, a novel application, a particularly strong evaluation, or anything else that is a strong aspect of this work. Please provide details, for instance, if a method is novel, explain what aspect is novel and why this is interesting.
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The paper introduces the Cascade SAM for Sperm Segmentation (CS3), which innovatively applies the Segment Anything Model (SAM) in a cascaded manner to tackle the challenge of overlapping sperm segmentation. This novel approach is significant as it adapts SAM, typically used for general image segmentation, to the specific complexities of sperm morphology analysis, enhancing its capability to differentiate and segment overlapping structures without requiring labeled data.
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CS3 operates entirely in an unsupervised fashion, which is particularly beneficial in the medical imaging field where labeled data are scarce and expensive to produce. This approach not only mitigates the limitations imposed by the lack of labeled datasets but also demonstrates a scalable solution that can be adapted to other medical imaging tasks with similar challenges.
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The research was conducted in collaboration with leading medical institutions, leading to the creation of a new dataset comprising approximately 2,000 unlabeled sperm images. This collaboration not only underlines the clinical relevance of the study but also enhances the practical utility of the CS3 method by ensuring it is trained and validated under real-world conditions.
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The CS3 method was empirically tested against traditional supervised methods and other unsupervised techniques, demonstrating superior performance in terms of segmentation accuracy. The paper uses robust metrics such as mean Intersection over Union (mIOU) and Dice coefficient (mDice) to validate the effectiveness of CS3, providing a compelling argument for its adoption over existing methodologies.
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While CS3 is tailored for sperm segmentation, the authors suggest that the methodology could be applicable to other domains where similar challenges exist, such as vascular and neural imaging. This potential for broader application highlights the versatility and impact of the research beyond its immediate focus, suggesting a path for future explorations in various fields of medical imaging.
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- Please list the main weaknesses of the paper. Please provide details, for instance, if you think a method is not novel, explain why and provide a reference to prior work.
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The CS3 algorithm faces difficulties in processing images with extremely complex overlaps, such as those containing more than ten intertwined sperms. While CS3 improves on existing methodologies, this limitation suggests that the algorithm may not fully replace human expertise in cases of high complexity, which could limit its practical application in clinical settings without prior sample preparation.
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While the paper compares CS3 against a variety of existing methods, it predominantly focuses on well-established or traditional segmentation techniques. The inclusion of more recent state-of-the-art methods in unsupervised learning or specialized biomedical image segmentation could have provided a more rigorous benchmarking, ensuring that CS3’s advancements are contextualized against the cutting edge of current research.
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CS3’s performance is highly dependent on specific preprocessing steps such as brightness, contrast adjustment, and background whitening. This dependency might limit the method’s applicability to different types of image data where such preprocessing might not be optimal or feasible, potentially reducing the method’s robustness across varying clinical environments.
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The study primarily focuses on sperm morphology images collected from specific medical collaborations, and while it suggests potential applications in other domains, it does not provide empirical evidence or trials to demonstrate how CS3 performs outside the context of sperm images. This lack of cross-domain validation might question the method’s effectiveness in other biomedical segmentation tasks.
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The paper does not discuss the computational requirements or efficiency of the CS3 algorithm. Given that the method involves multiple cascading stages of segmentation, it could be resource-intensive, which might not be suitable for real-time or on-device applications in clinical settings, potentially limiting its utility in scenarios where rapid processing is crucial.
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- Please rate the clarity and organization of this paper
Very Good
- Please comment on the reproducibility of the paper. Please be aware that providing code and data is a plus, but not a requirement for acceptance.
The submission has provided an anonymized link to the source code, dataset, or any other dependencies.
- Do you have any additional comments regarding the paper’s reproducibility?
Strengths in Reproducibility
- The creation of a dataset through clinical collaboration and its use in testing CS3 enhance the reproducibility of the research, as external researchers could potentially request access to the same dataset for verification and comparison purposes.
- The paper provides a comprehensive account of the CS3 algorithm, including the preprocessing steps, the cascade application of SAM, and the criteria for matching and assembling the complete sperm masks. This level of detail aids in understanding and potentially replicating the study’s methodology.
- The authors mention that the codes for CS3 are available online. Providing access to the implementation code is excellent for reproducibility, allowing others to directly use or modify the algorithm for further research or application.
Areas for Improvement
- While the paper mentions some parameters (like the HSV range for detecting purple regions and the thresholds for matching), a more detailed account of all parameter settings and computational resources required would help in precisely replicating the experiments. Details such as the hardware used, processing times, and any software dependencies should also be included.
- The paper would benefit from a more detailed robustness analysis, showing how CS3 performs under different settings or with varied image qualities. This would help in assessing the algorithm’s stability and reliability across different scenarios.
- To enhance the generalizability and reproducibility of the findings, the paper could include validation of the CS3 algorithm on different datasets or for other similar segmentation tasks in medical imaging. This would demonstrate the adaptability of the approach and provide a broader base for reproduction.
- Please provide detailed and constructive comments for the authors. Please also refer to our Reviewer’s guide on what makes a good review. Pay specific attention to the different assessment criteria for the different paper categories (MIC, CAI, Clinical Translation of Methodology, Health Equity): https://conferences.miccai.org/2024/en/REVIEWER-GUIDELINES.html
The introduction of CS3 as a novel method for sperm segmentation using a cascaded application of SAM is innovative and well-articulated. However, the paper could benefit from a clearer distinction between the capabilities of CS3 and existing methods in handling complex overlaps. Specific comparative insights against more recent unsupervised methods could enhance the novelty aspect and technical evaluation.
The methodology section is detailed, but it would be helpful to include more information about the computational efficiency of the CS3. Information such as algorithmic complexity, runtime, and hardware requirements would provide a complete picture of the practical deployment of the method.
More rigorous statistical analysis or a broader set of metrics could enhance the methodological rigor. For instance, including sensitivity, specificity, or a receiver operating characteristic (ROC) curve analysis might provide a deeper understanding of the model’s performance.
The clinical relevance of CS3 is well established, with its potential impact on automated sperm morphology analysis clearly highlighted. However, a more detailed discussion on the clinical testing, feedback from medical practitioners, or pilot studies in clinical settings could provide stronger evidence for its translation readiness.
Expanding on the dataset’s accessibility, whether it will be made publicly available for the broader research community, and under what conditions, would align well with open science principles.
Discuss any implications your work might have on health equity. For instance, can this technology be easily deployed in low-resource settings? What are the cost implications? Addressing these questions could significantly broaden the impact of your research.
- Rate the paper on a scale of 1-6, 6 being the strongest (6-4: accept; 3-1: reject). Please use the entire range of the distribution. Spreading the score helps create a distribution for decision-making
Accept — should be accepted, independent of rebuttal (5)
- Please justify your recommendation. What were the major factors that led you to your overall score for this paper?
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Innovative Methodology: The paper introduces a novel cascaded approach using the Segment Anything Model (SAM) for sperm segmentation, addressing the challenge of overlapping structures effectively.
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Unsupervised Learning: The use of unsupervised learning is highly valuable, given the scarcity of labeled datasets in medical imaging, making CS3 adaptable and scalable.
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Strong Empirical Validation: CS3 demonstrates superior performance over existing methods in terms of segmentation accuracy, which is well-supported by empirical results.
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Clinical Collaboration: The development of the CS3 involved collaboration with medical institutions and the creation of a relevant dataset, enhancing the method’s clinical relevance and applicability.
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Open Source Code Contribution: The provision of source code and detailed method descriptions promotes reproducibility and transparency, encouraging further research and development.
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- Reviewer confidence
Very confident (4)
- [Post rebuttal] After reading the author’s rebuttal, state your overall opinion of the paper if it has been changed
N/A
- [Post rebuttal] Please justify your decision
N/A
Author Feedback
N/A
Meta-Review
Meta-review not available, early accepted paper.