Unsupervised Domain Adaptation of Brain MRI Skull Stripping Trained on Adult Data to Newborns: Combining Synthetic Data with Domain Invariant Features

Authors: abbas  omidi
amirmohammad  shamaei
roberto  souza
Venue: MIDL_io_2024_Conference
Published: 2024-01-12 20:28:54

Abstract: Skull-stripping constitutes a crucial initial step in neuroimaging analysis, and supervised deep-learning models have demonstrated considerable success in automating this task. However, a notable challenge is the limited availability of publicly accessible newborn brain MRI datasets. Furthermore, these datasets frequently use diverse post-processing techniques to improve image quality, which may not be consistently feasible in all clinical settings. Additionally, manual segmentation of newborn brain MR images is labor-intensive and demands specialized expertise, rendering it inefficient. While various adult brain MRI datasets with skull-stripping masks are publicly available, applying supervised models trained on these datasets directly to newborns poses a challenge due to domain shift. We propose a methodology that combines domain adversarial models to learn domain-invariant features between newborn and adult data, along with the integration of synthetic data generated using a Gaussian Mixture Model (GMM) as well as data augmentation procedures. The GMM method facilitates the creation of synthetic brain MR images, ensuring a diverse and representative input from multiple domains within our source dataset during model training. The data augmentation procedures were tailored to make the adult MRI data distribution closer to the newborn data distribution. Our results yielded an overall Dice coefficient of 0.9308 ± 0.0297 (mean± std), outperforming all compared unsupervised domain adaptation models and surpassing some supervised techniques previously trained on newborn data. This project's code and trained models' weights are publicly available at https://github.com/abbasomidi77/GMM-Enhanced-DAUnet

Main paper: 
 introduction: 
 Advancements in neuroimaging, particularly through Brain Magnetic Resonance Imaging (MRI), have revolutionized diagnostic approaches in neurology. MRI now has become a vital diagnostic and monitoring tool for neurological illnesses since it is a thorough, noninvasive imaging technology, that aids in the understanding of various neurologic conditions (Combes et al., 2022) . A fundamental step in MRI analysis is skull-stripping, the process of isolating brain tissue from the rest of the head image, which is critical for accurate diagnosis and effective research in neurology (Rehman et al., 2020) .Newborn brain MRI presents distinctive challenges due to the unique physiological attributes of the neonatal brain, such as differences in brain structure and the contrast between white matter (WM) and gray matter (GM) due to incomplete myelination (Dubois et al., 2014) . Also, motion artifacts and additional structures, such as the neck and portions of the shoulders, show up in newborn MR images. These differences result in a substantial domain shift between adult and newborn MRI data, making it challenging to apply segmentation models trained on adult data to newborn data. The limited availability of annotated newborn MRI datasets further hampers the training of supervised deep-learning models.This paper introduces a new unsupervised method for newborn brain MRI skull-stripping that combines domain adversarial approaches to learn domain invariant features (Ganin et al., 2016) with a Gaussian Mixture Model (GMM) to generate synthetic data (Billot et al., 2023) , and a data augmentation strategy to help reduce the distribution shift between adult and newborn MRI data. This combination enables our system to accurately segment newborn brain images despite this domain's inherent challenges and without the need for any labelled newborn MR images. A detailed ablation study and comparison against stateof-the-art supervised models highlight the advantages of the proposed method. 
related work: 
 Domain adaption (DA) techniques are essential for reliable and generalizable model performance on a variety of datasets in the field of medical image segmentation, especially MRI skull-stripping (Ghafoorian et al., 2017; Zhong et al., 2021) . These methods tackle the issue of domain shift, which can hinder the transferability of models between domains because of variations in image acquisition protocols (Kondrateva et al., 2021; Full et al., 2021) .A prominent DA approach is adversarial DA (Ganin and Lempitsky, 2015; Tzeng et al., 2017; Dinsdale et al., 2021) . These methods revolve around fostering the creation of domaininvariant features and closing the gap between different domains. It leverages a discriminator to explicitly distinguish between domains, encouraging the segmentation network to learn domain-invariant features and showcasing the potential to yield more flexible and effective models. However, its efficiency decreases when there are complex domain shifts.Classic methods such as BET (Brain Extraction Tool) (Smith, 2002) , BSE (Brain Surface Extractor) (Shattuck et al., 2001), and Robex (Iglesias et al., 2011) have laid the groundwork for skull-stripping, demonstrating effectiveness in various contexts yet facing limitations in adaptability and precision, particularly with heterogeneous data like neonatal MRI scans.In skull-stripping, automated methods have evolved from traditional image-processing techniques to deep learning-based methods. Techniques like BEaST (Eskildsen et al., 2012) , region growing (Lu et al., 2003) , and deformable models (Colliot et al., 2006) were pioneering but often fell short due to their reliance on predefined features and parameters, making them less adaptable to the variability inherent in MRI scans.Recent advancements in deep learning and the development of infant-specific methods, such as SSCNN (Jog et al., 2019) , iBEAT (Dai et al., 2013) , and the approaches proposed by Lucena et al. (Lucena et al., 2019) and Chen et al. (Chen et al., 2018) , have aimed to tailor computational techniques to the distinct anatomical and contrast characteristics of neonatal and infant brains. These methods represent innovative strides toward more accurate brain imaging analyses in neonates and infants. However, despite their advancements, these methods still face challenges related to domain shifts, data scarcity or limited availability, and the need for extensive labeled datasets for training. This is particularly problematic given the high variability present in real-world clinical settings. Moreover, the utilization of comprehensive datasets, such as the developing Human Connectome Project (dHCP) dataset, underscores the critical need for robust and diverse data to effectively train and validate these deep learning approaches (Edwards et al., 2022) . However, the dHCP dataset is limited by its single-center nature and the use of sophisticated post-processing to enhance image quality, practices not universally feasible in clinical environments. These factors contribute to potential generalization issues across different centers. Hence, while invaluable, relying solely on dHCP for developing skull-stripping models might not ensure broad applicability in diverse clinical settings.To mitigate these issues, recent research has explored the use of DA techniques (Wang and Deng, 2018; Munk et al., 2023; Farahani et al., 2021; Ben-David et al., 2006) . For instance, Billot et al. introduced a novel method utilizing a GMM to create synthetic brain MRI scans of various contrasts and resolutions (Billot et al., 2023) . These synthetic scans are then used to train segmentation models, showing promising results in handling domain shifts. However, this data-centric strategy addresses the domain shift challenge predominantly through the lens of data availability and diversity. The architecture of a model is pivotal in determining its learning efficiency and generalization power across varying domains. If the architectural aspects of the model remain unchanged or unoptimized, the benefits of a richer and more diverse dataset may not be fully realized.Our previous work addresses the challenge of skull-stripping in newborn MRI data by developing an architecture trained on adult MRI data for use on newborns (Omidi et al., 2024) . While this approach represents an improvement in handling the domain shift between adult and newborn data without supervision, it faces constraints due to limited source data. This discrepancy highlights a critical issue in medical imaging: the balance between innovative model architecture and the availability of diverse, representative training data. 
gaussian mixture model: 
 Building upon the domain randomization strategy introduced by Billot et al., we utilize a generative model to produce a sequence of diverse synthetic brain MRI scans featuring significant anatomical and intensity variations. By fully randomizing the generation parameters, the synthetic training data encompasses a wide range of contrasts and resolutions, enabling the segmentation network to generalize without retraining.In the Billot et al. approach, the generative model assumes the availability of N training label maps {S n } N n=1 defined over discrete spatial coordinates (x, y, z) at high resolution r HR . These label maps, taking values from a set of K labels, can be obtained manually, Our segmentation framework is built upon a 3D U-Net model (Figure 1 ). In our DA model, we interweave three principal alterations into the classical U-Net construct. To distinguish between MRI brain images of adults (source) and newborns (target), we incorporated a discriminator network. This network takes as input the features extracted from the last layer of the decoder and the bottleneck features. This process is applied to both source sets (GMM-Enhanced Dataset and Calgary-Campinas Dataset) and our target dataset (Newborn). This discriminator plays a pivotal role in classifying the domain of input images, leveraging both high and low-level features.To encourage the generation of features independent of the imaging domain, we implemented a gradient reversal layer, inspired by the work of Ganin et al. (Ganin and Lempitsky, 2015) . This layer adjusts backpropagation gradients inversely through a scaling factor, providing a strategic means to promote domain-invariant feature extraction. Also, we combined the output from the encoder's bottleneck with the final layer before the discriminator. The operational mechanism of our network is shown through the following equation:EQUATIONThe provided equation illustrates the update process of network parameters, denoted as ∆θ, during the learning phase, guided by the learning rate (µ) and scaling factor (λ). The equation indicates that the decoder parameters (θ dec ) undergo updates based solely on the segmentation loss (L seg ), the discriminator parameters (θ disc ) undergo updates based solely on the discriminator loss (L disc ), while the encoder parameters (θ enc ) undergo updates considering both the segmentation loss (L seg ) and the discriminator loss (L disc ).Addressing the unique challenges in newborn brain MRI, we tackled the issue of not fully myelinated GM and WM tissue contrast by inverting the WM-GM contrast in the Calgary-Campinas dataset. This modification, aimed at aligning with the inverted contrast observed in newborns, facilitated more effective skull-stripping by training the model on a mix of original and contrast-inverted images. The contrast inversion was implemented as follows:C(i) = max j∈M [C(j)] -C(i), ∀i ∈ M (2)where C(i) is the original voxel intensity and C(i) the transformed intensity for voxel i within the WM-GM mask, with M being the set of WM-GM voxels.To mitigate the effects of motion artifacts due to newborn movements during scans, we adapted our GMM-Enhanced dataset with motion artifacts and Gaussian Blur transformations. These adaptations, aimed at mimicking newborn MRI conditions, employed the MONAI framework's blur function (Cardoso et al., 2022) and motion artifacts protocol proposed by Zaitsev et al. (Zaitsev et al., 2015) , enhancing the dataset's suitability for newborn imaging analysis.In our model, we use the Dice loss function for segmentation, and for the discriminator's performance evaluation, we rely on Binary Cross-Entropy (BCE) loss. The composite loss function can be mathematically expressed as follows:L total = N 1 i=1 [1 -Dice(ŷ i , y i ) + BCE(u i , v i )] + N 2 j=1 BCE(u j , v j ) (3)Here, L total denotes the overall loss. The first part computes the Dice Loss for the predicted segmentation (ŷ i ) against the ground truth (y i ) for N 1 source images. The second part calculates the BCE Loss for these source images (u i ) against their domain labels (v i ). The equation also includes BCE Loss for N 2 target images (u j ) and their domain labels (v j ). This part is crucial for enabling the model to adjust to the target domain's characteristics. In our research, we employed the Calgary-Campinas public brain MRI dataset, comprising 359 T1-weighted, 3D, 1 mm isotropic adult brain MRIs, with a gender distribution of 176 males and 183 females (Souza et al., 2018) . This dataset has vital features like skull-stripped brain masks and segmentation masks for WM and GM generated using FSL software (Jenkinson et al., 2012) . The images were acquired using MRI scanners from various manufacturers and magnetic field strengths. 
gmm-enhanced dataset: 
 The GMM-enhanced dataset comprises 420 synthetic 3D samples generated from 20 adult brain segmentation masks (Billot et al., 2023) . The number of synthetic samples was obtained empirically. The GMM approach allowed us to create a diverse and comprehensive set of data without relying on actual newborn MRI scans.Our study incorporated a private dataset from the Alberta Children's Hospital, gathered using a GE 3T MRI scanner. This dataset consists of 12 high-resolution T1-weighted, 3D newborn brain MRIs (7 females and 5 males), each with dimensions of 1 mm × 1 mm × 0.5 mm. For evaluation purposes, brain masks were manually acquired on ten sagittal slices, each spaced ten slices apart, from five out of the twelve samples in the test dataset.Our model was trained for 500 epochs with a batch size of four on an A-100 GPU. Each epoch took around 20 minutes to complete. For training, we employed a strategy of extracting 96 × 96 × 96 patches, which was executed using the MONAI framework (Cardoso et al., 2022) . The patch-based method not only managed the data volume effectively but also optimized our resource usage. Furthermore, the datasets are provided to the network in a balanced fashion, indicating that we duplicated the limited target dataset multiple times to align its quantity with that of our source datasets.To evaluate and compare the performance of the models, we used the Dice coefficient and the 95th percentile Hausdorff distance metrics. These metrics were calculated on the newborn test set. Details of the data distribution across training, validation, and testing phases are provided in Table 1 . To evaluate the effectiveness of our proposed approach, we conducted three ablation studies focusing on different aspects of the proposed model. We also compared the proposed model against the U-Net model trained on the Calgary-Campinas dataset, HippoDeep (Thyreau et al., 2018) , SynthStrip (Billot et al., 2023) , and our previous work (Omidi et al., 2024) . Both HippoDeep and SynthStrip had access to labelled newborn data during training.In the first ablation study, we utilized a basic U-Net model and trained it with the Calgary-Campinas and GMM-Enhanced datasets (Named: U-Net GMM in Table 2 ). The aim was to see how much better the U-Net segmentation would be when exposed to the GMM-Enahnced data during training.The second ablation study added the domain adversarial component to our model so it would learn domain invariant features (Named: DA GMM in Table 2 ). This model also used GMM-Enhanced data during training, but the GMM-Enhanced data used did not leverage our data augmentation strategy (see Section 3.3). This study aimed to quantify the impact of the DA component in our proposed network architecture.In the third ablation study, the proposed data augmentation strategy was added to our training data (Named: Proposed Method in Table 2 ). This study evaluated the impact of the proposed data augmentation on the segmentation results. 
results: 
 The Dice coefficient and the 95 th percentile Hausdorff distance metrics are summarized in Table 2 . Our proposed method achieved the best performance among unsupervised models in the newborn test set. Strip in the Dice coefficient and 8.6% for the Haussdorff distance metric. For a visual representation of the segmentation masks that reflect the overall results of the newborn test set, please refer to Figure 2 . For a discussion on the limitations of our study, we have included a failure case analysis in the Supplementary Material.  
discussion: 
 The ablation studies showed that combining synthetic data for training with architectural model improvements specifically designed for DA purposes and a data augmentation strategy tailored to close the gap between the adult and newborn data distribution shift resulted in better segmentation results. The GMM-enhanced data improved the Dice coefficient from 0.08 (U-Net) to 0.67 (U-Net GMM). While the DA component of our model further improved the Dice results to 0.92 (DA GMM). Finally, our data augmentation component that added to our training set elements, such as motion corruption and WM-GM contrast inversion, further improved our Dice coefficient to 0.93. The results of these ablation studies highlight the importance of combined approaches that look both at data aspects and model architecture for medical image segmentation problems. Also, the generalizability of the proposed method to other real-world scenarios may be impacted by the limited availability of publicly accessible newborn brain MRI datasets, as the model's performance has been validated on a small sample of the newborn MRI data landscape. In medical imaging, the diversity of data, encompassing variations in imaging protocols and patient demographics, plays a crucial role in the robustness of models. Without access to a broader range of datasets, there's an inherent risk that the model may not capture the full spectrum of variability in newborn brain anatomy and pathology. 
conclusion: 
 Our proposed unsupervised skull-stripping method for newborn MRI achieved results comparable to supervised methods. The proposed method leverages DA techniques, GMMgenerated synthetic data, and a data augmentation approach tailored to newborns to overcome the issue of domain shift across adult and newborn MRI data.Our results showed a 1.67% improvement in the Dice coefficient compared to the best existing unsupervised methods in the literature. Also, when compared to state-of-the-art supervised models, our approach surpasses the Hippodeep model by 0.93% in the Dice coefficient and closely trails SynthStrip by only 1.11%. It is worth noting that SynthStrip is trained on a range of newborn MRIs, and Hippodeep utilizes a dataset of over 5000 MRI scans. Our results pave the way for developing new deep learning DA segmentation models that operate both from the data and model architecture perspectives. 
 
 Appendix:  


Decision: While there is a point of contention in the actual usefulness of the *application* of the method given the availability of other datasets, the reviewers agree that there is value in the method itself, combining domain adaptation with synthetic data for enhanced DA. Moreover, the interactions between authors and reviewers have significantly improved the submission. I am recommending borderline acceptance for this paper.

Reviews: review: paper_summary: paper_summary: This paper tries to tackle the challenge of brain MRI skull stripping for newborns. The authors proposed a combination of adversarial learning and GMM-based data augmentation to encourage the model to learn domain-invariant features while leveraging the domain gap between rich labeled adult and insufficient unlabeled newborn samples. The overall quality of the manuscript is acceptable, and even though the major components of the proposed method are adopted from existing works, the experiments show some promising potential of their method compared to others.  I would recommend accepting if my concerns below could be addressed.
main_review: main_review: The authors have successfully addressed all my concerns. The newly added explanation of GMM in Section 3.1 eases the understanding of data augmentation. The failure case analysis further enhances the overall organization. Several typos have been corrected.
strength_weakness: strength_weakness: 1. Lacking sufficient details in Section 3.1 because this is one of the major components of the method; even though it is not original, details must be provided to offer readers a clearer concept
2. The ablation study needs improving; the proposed discriminator lacks further inspection.
3. No limitation analysis, which might be crucial for medical applications.
questions: questions: Updated Section 3.1, the ablation studies, and failure case analysis.
paper_summary: paper_summary: The authors propose a new framework to carry out skullstripping in newborn brain MRI. Their solution combines domain adversarial models learning domain-invariant features between newborn and adult data, along with the integration of GMM-generated synthetic data and data augmentation procedures.
This formulation is a modification of an existing tool and the motivation for it is the lack of available newborn neuroimaging data set. 

The experiments are run on a fairly small data set and result in satisfactory skullstripping results.

The authors committed to sharing their code on GitHub.
main_review: main_review: The motivation / background section has been updated regarding previous work and the definition of the desired skull-stripped result has been provided. The idea about learning from rich adult datasets is interesting, but I would argue that the availability of various publicly available newborn brain MRI datasets makes the experimental section weak with its limited test data.

I change my score to weak reject due to the updates made in the submission.
strength_weakness: strength_weakness: The technical novelty is low and also the motivation cited is no longer valid (given the availability newborn neuroimaging data with including skullstripping mask).

The motivation / background section lacks some key citations regarding prior work.

No definition of the desired skull-stripped result is provided. 

The generated motion artifacts (used for data augmentation) seem to be suboptimal (if the Appendix figure is a representative example)
questions: questions: What is the reason for not looking for publicly available newborn data sets?
Does the scaling range in the data augmentation phase cover the variability in the newborn population?
paper_summary: paper_summary: The paper proposes a methodology that combines domain adversarial models, synthetic data generation using a Gaussian Mixture Model (GMM), and data augmentation procedures for unsupervised domain adaptation of brain MRI skull stripping from adult data to newborns. The proposed methodology addresses the challenge of domain shift when applying supervised models trained on adult brain MRI datasets to newborns.
main_review: main_review: This study introduces a method that combines domain adversarial models to learn domain-invariant features between newborn and adult brain MRI datasets. It also integrates synthetic data generated using a Gaussian Mixture Model and data augmentation techniques. The results of this study outperform unsupervised domain adaptation models and even some supervised techniques previously trained on newborn data. The authors have satisfactorily addressed all concerns.
strength_weakness: strength_weakness: - The paper does not provide a detailed discussion or analysis of the limitations or potential drawbacks of the proposed methodology.

- The scarcity of publicly accessible newborn brain MRI datasets with manually annotated masks is acknowledged as a challenge. Still, the paper does not discuss this limitation's potential impact on the proposed method's generalizability and applicability.

- The paper does not provide a comprehensive comparison with other state-of-the-art unsupervised domain adaptation models or discuss the specific limitations of those models that the proposed method overcomes.
questions: questions: 1. What are the limitations of the proposed methodology?

2. How does the scarcity of annotated datasets impact the generalizability of the method?

3. What are the specific limitations of other unsupervised domain adaptation models?

4. What role does the Gaussian Mixture Model (GMM) play in generating synthetic brain MR images?

Comments: title: Our response
Our response
Our response (Part 2)
Our response (Part 1)
Our response
comment: Thanks for the authors' responses to my questions. 

I appreciate the clarification on the tools task and the addition to the background section about already existing tools.

I think we will have some disagreement remaining about the availability of newborn datasets. I mentioned the dHCP cohort as an example, but more varied (obtained at various sites, on various scanners) have also been available for years. See, for example, NIH MRI Study of Normal Brain Development, UNC/UMN Baby Connectome Project. A more complete validation / experimental section could be established by the use of any of these.

re motion mitigation. "We updated the figure to show a less extreme case of motion artifacts" The modified example in the figure looks more reasonable. I did not find a tool associated with [3] though, that would generate these artifacts. [3] describes a set of tools that can be used to mitigate motion artifacts.
The authors have addressed all of my concerns. I have no more comments.
Thank you for pointing us to the new download mechanism. As stated in our responses, we plan to use the dHCP data in future works to evaluate our model more in-depth. Also, according to the dHCP release notes, this download mechanism started in February 2024 (https://biomedia.github.io/dHCP-release-notes/), which was after the 2024 MIDL paper registration deadline.
The dHCP download site moved to https://nda.nih.gov/. The download mechanism does not include torrents and seeds any more.
We thank the reviewer for their relevant and detailed feedback and appreciate the opportunity to discuss the limitations and challenges of our proposed methodology further. Please see our answers to your questions below. 

**Comment:** What are the limitations of the proposed methodology? 

**Response:** In response to the request for a discussion on the situations in which our method works poorly, we have included a section devoted to examining the shortcomings and failure scenarios in the Supplementary Material. For instance, our method struggles with infant subjects whose skull is unusually elongated, and in general, it encounters limitations in cases with atypical skull structures. Additionally, our methodology faces challenges with input images where brain regions are exceptionally dark and with very low contrast.

**Comment:** How does the scarcity of annotated datasets impact the generalizability of the method? 

**Response:** To address this concern comprehensively, we've included more details in our paper's discussion section, to discuss this limitation's potential impact on the proposed method's generalizability and applicability. In medical imaging, the diversity of data, encompassing variations in imaging protocols and patient demographics, plays a crucial role in the robustness of machine learning models. Without access to a broader range of datasets, there's an inherent risk that the model may not capture the full spectrum of variability in newborn brain anatomy and pathology. As a result, we agree that the generalizability of our proposed method to other datasets and real-world scenarios may be impacted by the scarcity of publicly accessible newborn brain MRI datasets, as the model's performance has been validated on a relatively small sample of the newborn MRI data landscape. 

**Comment:** What are the specific limitations of other unsupervised domain adaptation models? 

**Response:** Our exploration into unsupervised domain adaptation for newborn brain MRI data highlighted a significant gap in the literature, as there are no existing unsupervised models directly addressing this challenging application. This absence underscored the innovative aspect of our research, which applied data augmentation strategies (white-matter and gray-matter contrast inversion and motion artifacts) to make the adult data distribution more similar to newborn data. Given the availability of supervised models trained on newborn data (SynthStrip is trained on newborns labeled MRI from ages 0 to 18 months, and Hippodeep uses over 5000 MRI samples), we opted to use them as baselines for comparisons. Our method outperformed one of these models, demonstrating its efficacy and contributing a novel solution to the field. As future work, we agree that other domain adaptation techniques could be adapted/implemented for the specific problem of skull-stripping newborn brain MRI.

**Comment:** What role does the Gaussian Mixture Model (GMM) play in generating synthetic brain MR images? 

**Response:** To give a fuller description of how the GMM approach is applied in sample synthesis, we have expanded on Section 3.1. This provides a more thorough explanation of the GMM's function in our methodology and how it's integrated into the creation of synthetic data. 

We hope these clarifications and reflections address your concerns.
**Comment:** In order to provide a wide variety of variability in the training data set, SynthSeg is used for data generation. It seems to me that the proposed scaling range [.9-1.2] cannot be sufficient to bridge the newborn-adult domains, given that the newborn brain often doubles in size within the first two years of life. 

**Response:** The scaling range of [0.9-1.2] was specifically chosen not to replicate neonate brain sizes but to introduce a broader spectrum of adult data variability. We do not need to generate synthetic images that look precisely like newborns. Our discriminator, as we detail in our following response, encourages our model to learn invariant features across adult and newborn domains. Our ablation study also supports our implementation since adding the synthetic data and data augmentation improved our model, and the addition of the adversarial component further improved our results. 

**Comment:** If I understand correctly, a discriminator network is defined by the authors to determine whether the input is from newborn or adult subjects; given the above-mentioned scaling difference, this should be a straightforward task. 

**Response:** The discriminator seeks to distinguish between newborn and adult images. We agree with the reviewer that this is a trivial task. However, as we train our model, we “encourage” it to learn features that are not discriminative of these two groups (i.e., domain-invariant features), which is at the root of domain adversarial domain adaptation techniques [2].  

**Comment:** The motion artifacts implementation seems suboptimal. If Fig 5 is representative, the motion artifact seems like a pediatric scan overlaid on the adult and not an adult motion artifact. 

**Response:** Thank you for the comment. We updated the figure to show a less extreme case of motion artifacts. The motion artifacts were generated using existing methods [3]. 

**Comment:** It is the Calgary-Campinas Dataset that is used for the experiments, along with a small data set from Alberta. As mentioned above, I do not understand why the dHCP cohort was not relied on. It is actually even bigger than the one used and has skull-tripped and segmented solutions shared. 
The Alberta newborn data set is also used. For testing, 5 data sets are selected (where 10 slices from each are manually annotated). This seems to be too small when a newborn datasets is readily available. 

**Response:** Please see our answer to your second comment. We agree that a more detailed evaluation of the method should be performed in future works. 

**Comment:** Additionally immature --> not fully myelinated Table 2 comment: "trained on" newborn data --> "trained on including" newborn data -- This is a very important distinction! "The main difficulty in performing a more detailed evaluation is the lack of public newborn brain MRI datasets." That used to be true but for the past several years it is not the case. 

**Response:** Thank you for your comment. We fixed this in the paper and also removed the sentence about the difficulty in performing a more detailed evaluation due the lack of public newborn brain MRI datasets and rephrased it to indicate the pilot nature of our work. 

We hope to have addressed/mitigated the reviewer’s concerns and hope that they will reconsider our score.  

**[1]** Shirokikh, B., Zakazov, I., Chernyavskiy, A., Fedulova, I. and Belyaev, M., 2020. First U-Net layers contain more domain specific information than the last ones. In Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning: Second MICCAI Workshop, DART 2020, and First MICCAI Workshop, DCL 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4–8, 2020, Proceedings 2 (pp. 117-126). Springer International Publishing. 

**[2]** Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., March, M. and Lempitsky, V., 2016. Domain-adversarial training of neural networks. Journal of machine learning research, 17(59), pp.1-35. 

**[3]** Zaitsev, M., Maclaren, J. and Herbst, M., 2015. Motion artifacts in MRI: A complex problem with many partial solutions. Journal of Magnetic Resonance Imaging, 42(4), pp.887-901.
Thank you for your valuable and detailed feedback, which has led us to clarify and enhance our manuscript in several key areas. Please see our answers to your comments below: 

**Comment:** One key description I miss in this submission is the lack of a definition of the skull-stripping mask here. Knowing whether the training data did or did not include external CSF is very crucial when evaluating and comparing to other methods. SynthStrip, for example, includes CSF in the foreground. Does the new tool? 

**Response:** We do include external CSF similar to SynthStrip. We clarified this in the methods section. 

**Comment:** The motivation of the tool development is weak due to abundant (compared to the data set used by the authors) labeled newborn data availability. The best example would be the publicly available dHCP data cohort publicly distributed with postprocessing outcomes (including skull stripping and volumetric segmentation). The current test data set is also very small with 10 slices manually annotated from 5 subjects. 

**Response:** We only partially agree with the reviewer’s comment. The dHCP study is a landmark study, and a reference was added to the manuscript. We agree that in future works, our model should also be evaluated using their dataset and others potentially available. However, the dHCP has limitations: 
- Data distribution is made through torrents, and seeds are often unavailable. Also, the high-speed networks of many universities block these types of downloads for cybersecurity reasons. To this date, we have not been able to download all anatomical scans and corresponding masks. 
- The dHCP data is single-centre. It was collected using a 3T Philips Achieva scanner located at the Evelina London Children’s Hospital. Training the skull-stripping model using only their data is unlikely to generalize well across centres. This has been explicitly shown in the case of skull-stripping in research by other groups [1].  
- The dHCP scans (T1w) are obtained twice and combined using sophisticated post-processing algorithms to improve image quality by mitigating artifacts like motion. Such high-quality data is not feasible in most clinical settings. Though we could have used dHCP when developing our method, it would not necessarily generalize well to new data due to potentially poorer data quality of images collected in other centres.  
- We agree that our current test set is small and should be increased in future works. Nevertheless, our opinion is that the proposed technique is novel. It combines synthetic data with learning domain-invariant features, and our results using a limited dataset show promise for future use. We clarified in the paper the pilot nature of this study.

**Comment:** The background section lacks some classic methods, such as BET, BSE, Robex. In particular, some more recent DL methods (EVAC+) and infant-specific ones (SSCNN, iBEAT, and dCHP). 

**Response:** We agree with the reviewer, and we have expanded our background section to include a discussion on classic methods (BET, BSE, Robex) and recent deep learning and infant-specific methods, providing a more comprehensive overview of the field.
We appreciate the detailed feedback provided and have taken steps to address the concerns raised. Please see our answers to your comments below. 

**Comment:** Section 3.1 regarding the Gaussian Mixture Model needs to be elaborated a bit more, although it is introduced in another paper. It remains unclear how this GMM method is applied in synthesizing samples. Besides, the set of deformations applied during training is just a very common data augmentation strategy. There is no need to allocate many lines in its details. 

**Response:** We have elaborated on Section 3.1 to provide a clearer explanation of the GMM method's application in synthesizing samples. This includes a more detailed description of the GMM's role in our methodology and its integration into the synthetic data generation process. Also, since our code is publicly available, we minimized the focus on common data augmentation strategies and have thus condensed this section accordingly, as the reviewer suggested. 

**Comment:** Despite GMM and the proposed DA, the discriminator in the model also acts as a constraint to limit the learning of domain-related features. However, no ablation study was conducted to verify that. 

**Response:** We appreciate the opportunity to clarify the existence of an ablation study, which indeed examines the effect of the discriminator's absence on our model's performance. This study is detailed in Section 4.3 under the designation Unet GMM. This variant of our model utilizes the same data and architectural framework, excluding the discriminator component. This comparison underscores the discriminator's significant role in enhancing the model's ability to generalize across domains by mitigating the learning of domain-specific features. We clarified our text to remove any potential confusion by readers. 
 
**Comment:** I would expect some words discussing failure (or worse) results in comparison to the results from other methods and possible explanations. 

**Response:** Thank you for your comment. We added a section in the supplementary material that analyzes these failure cases. 

**Comment:** In Section 6, the first two numbers should be 0.08 (0.0790) and 0.67 (0.6682). 

**Response:** We have revised this issue. Thank you for pointing it out. 

We hope that these clarifications and reflections help address your concerns.