Annotation-Efficient Strategy for Segmentation of 3D Body Composition

Authors: lena  philipp
john  hermans
matthieu  rutten
horst  hahn
alessa  hering
m  philipp
j  de rooij
m  hermans
h  rutten
b  hahn
a hering van ginneken
Venue: MIDL_io_2024_Conference
Published: 2024-01-29 17:14:12

Abstract: Body composition as a diagnostic and prognostic biomarker is gaining importance in various medical fields such as oncology. Therefore, accurate quantification methods are necessary, like analyzing CT images. While several studies introduced deep learning approaches to automatically segment a single slice, quantifying body composition in 3D remains understudied due to the high required annotation effort. This study proposes an annotation-efficient strategy using an iterative self-learning approach with sparse annotations to develop a segmentation model for the abdomen and pelvis, significantly reducing manual annotation needs. The developed model demonstrates outstanding performance with Dice scores for skeletal muscle (SM): 0.97+/-0.01, inter-/intra-muscular adipose tissue (IMAT): 0.83 +/- 0.07, visceral adipose tissue (VAT): 0.94 +/-0.04, and subcutaneous adipose tissue (SAT): 0.98 +/-0.02. A reader study supported these findings, indicating that most cases required negligible to no correction for accurate segmentation for SM, VAT and SAT. The variability in reader evaluations for IMAT underscores the challenge of achieving consensus on its quantification and signals a gap in our understanding of the precision required for accurately assessing this tissue through CT imaging. Moreover, the findings from this study offer advancements in annotation efficiency and present a robust tool for body composition analysis, with potential applications in enhancing diagnostic and prognostic assessments in clinical settings.

Main paper: 
 introduction: 
 A body mass index (BMI) greater than 30 kg/m2 is commonly seen as a health risk factor for various cardiovascular diseases (Powell-Wiley et al., 2021) and cancer types (Lauby-Secretan et al., 2016) . However, the BMI is limited in its inability to differentiate between adipose tissue/muscles and to account for the heterogeneity in fat distribution, which can lead to imprecise or misleading results (Piché et al., 2018) . The importance of body composition is increasingly recognized in studying survival in cancer patients (Cespedes Feliciano et al., 2018) , (Shachar et al., 2016) , implications for care (Prado et al., 2018) and metabolic diseases (Pi-Sunyer, 2019) and highlights the need for accurate methods of measurement. The widespread use of CT imaging in clinical settings, coupled with its ability to distinguish between adipose tissue and muscle and provide detailed insights into fat distribution, positions it as a superior method for improving body composition analysis accuracy. In routine clinical practice, measuring body composition remains difficult due to the expert knowledge required for the time-consuming annotation process. This is why only an axial slice at the level of the lumbar spine level 3 (L3) is often used due to the high correlation to the entire volume (Mourtzakis et al., 2008) . Convolutional neural networks have enabled the automatic segmentation of skeletal muscle (SM), inter-/intra-muscular adipose tissue (IMAT), visceral adipose tissue (VAT), and subcutaneous adipose tissue (SAT) using a slice at the height of L3 ((Weston et al., 2019) , (Paris et al., 2020) , (Shen et al., 2023) ), L3/L4 ( (Nowak et al., 2020) , (Park et al., 2020) ), pelvis (Hemke et al., 2020) , and multiple heights (Ahmad et al., 2023) . Additionally, end-to-end solutions have been developed to automate slice selection as well ((Nowak et al., 2021) , (Dabiri et al., 2020) , (Zhang et al., 2022a ), (Bridge et al., 2022) , (Xu et al., 2022) ). The trend has recently evolved towards volumetric body composition analysis for more comprehensive measurements across larger body regions using multi-atlas segmentation ( (Hu et al., 2018) , (Decazes et al., 2019) ) or deep learning (DL) approaches ( (Koitka et al., 2021) , (Pu et al., 2021) , (Liu et al., 2020) , (Dai et al., 2024) , (Fu et al., 2020) , (Borrelli et al., 2021 ),(Lee et al., 2021) ). Despite the advancements, the annotation of data required for training these 3D DL models presents significant challenges, attributed to the labor-intensive and meticulous nature of the task. This often results in training and test sets that are either small in size or only partially annotated. (Lee et al., 2021) and (Pu et al., 2021) utilized larger training datasets to enhance their models. (Lee et al., 2021) employed manual annotations for their training and testing sets, leading to a model that achieved high Dice scores. However, it did not distinguish IMAT with a separate segmentation mask. Addressing the complexity of the volumetric annotation of body composition, (Pu et al., 2021) tackled this challenge by employing semi-supervised self-training in the training process, albeit with suboptimal performance outcomes with Dice scores of 0.82 for VAT and 0.59 for IMAT. Semi-supervised learning is particularly relevant in contexts where extensive data exists, but only a fraction is labeled. It incorporates not only fully annotated data but also utilizes noisy, weak annotations or pseudo labels to enrich the training process. Pseudo labels are annotations created by applying a model to unlabeled data and using the output as new additional annotations. This self-training step is repeated to extend the training data iteratively or improve the label's quality. Self-training can be seen as a form of weak supervision and builds on the potential to outperform the teacher ( (Guan et al., 2018) , (Khoreva et al., 2017) , (Zhang et al., 2018) ). While many 3D semi-supervised segmentation methods require an initial dataset of 3D annotations, utilizing sparse annotations can significantly enhance efficiency by annotating just a few slices within a 3D volume, taking advantage of the strong correlation among slices to preserve precise object boundaries with minimal annotations. Approaches using sparse annotations have been shown to outperform traditional weakly supervised techniques that utilize scribbles ( (Lin et al., 2016) , (Liu et al., 2022) ) and bounding boxes ( (Oh et al., 2021) , (Dai et al., 2015) ) in both efficiency and accuracy. Most of these methods involve the use of registration modules to generate pseudo labels ( (Li et al., 2022) , (Cai et al., 2023a) , (Bitarafan et al., 2020) ). (Cai et al., 2023b) propose a cross-teaching method that enforces consistency between the predictions of 3D and 2D networks, thereby increasing the view difference of networks. In this work, we aimed to develop a robust 3D segmentation model to quantify SM, IMAT, VAT and SAT. Facing challenges like limited annotated datasets, time-consuming manual labeling, and the need for diverse scans for robustness, we introduce a novel self-training strategy with sparse annotations. This strategy transitions from a 2D to a 3D model, substantially reducing the manual annotation workload by requiring only individual slices to be labeled manually. The goal of this method is to decrease annotation efforts without compromising on segmentation quality. To validate the efficacy of our approach, we conducted an evaluation on an internal test set, focusing on the segmentation performance through the Dice score. Additionally, an expert reader study using a larger external dataset was conducted to evaluate the effort needed to correct generated segmentation masks, offering a detailed assessment of our model's clinical utility and accuracy. 
materials and methods: 
 The objective is to create a DL model that accurately measures body composition throughout the abdominal and pelvic regions. This involves training the model to produce segmentation masks for four categories: SM, IMAT, VAT, and SAT.Our training dataset comprises 116 scans, amounting to 38,002 slices, from 100 patients (47% female), gathered between 2008 and 2021. These scans were selected for their comprehensive field of view spanning the entire abdomen and pelvis. A subset of 417 slices (extracted from 24 scans) from this dataset were annotated by a trained researcher and student using an open-source tool 3D Slicer (Slicer, 2020) , (Fedorov et al., 2012) . For quality control, an experienced radiologist was consulted. The annotation process followed a standardized annotation protocol (Alberta Protocol; (TomoVision, 2021) ). The proposed segmentation strategy was applied to the remaining 92 scans and slices. The internal test set, aimed at assessing segmentation performance measured as Dice score, includes 20 scans (10 male, 10 female) chosen for their field of view. Given the labor-intensive nature of annotating each of these scans (each with an average of 322 slices) this set was initially automatically annotated using an intermediate baseline 3D U-Net trained on 12 CT scans. These automatically derived segmentation masks were subsequently manually verified and corrected. For external testset for visual assessment, we utilized 100 cases from the KiTS21 dataset (Heller et al., 2023) , ensuring a balance in BMI categories (normal, overweight, obese) and sex, using the KiC data (IBM Research, 2022). These cases were adjusted to focus on the L1 to L5 region and resampled to a 3mm slice thickness to streamline the review process. Further data details are provided in Appendix A.The proposed annotation strategy employs a self-training methodology, utilizing both 2D and 3D neural networks.2D training: The process began with the selection of a subset of scans, totaling 10. For each scan, 15-20 slices were chosen, spanning from the T9 vertebra to the end of the pelvis, and were semi-automatically annotated using a threshold brush to create training data. A 2D U-Net model was then specifically trained to segment these slices accurately.To keep the model's training focused, we avoided using regularization techniques like data augmentation or dropout. After training, this 2D model was used to produce segmentation masks for all slices in the scans, which were then assembled into complete 3D masks.3D training: The initial, imperfect masks served as the training foundation for a 3D nnU-Net (Isensee et al., 2021) . Leveraging the added contextual patterns, this 3D model was adept at generating refined, smoother masks for the scans. . With this strategy, expert annotations were only performed for a small subset of 2D masks (417 out of 38002, 1%) instead of correcting 3D masks, which significantly lower the overall annotation workload.Our evaluation comprises two phases to ensure a thorough assessment of the model: Quantitative Assessment: We utilize a dataset of 20 cases to measure the model's Dice score performance, focusing on the area from the thoracic vertebra T9 to the end of the pelvis which we divided into three specific segments: T9-T12, L1-L5, and S1 to the end of the pelvis. Visual Assessment: Acknowledging the limitations presented by the small size of our internal test set, we incorporated an additional visual evaluation step to provide a broader perspective on the model's performance. Three experienced radiologists visually evaluate 100 cases within the L1-L5 region, rating the effort to correct the segmentation masks. With this evaluation, conducted on the grand-challenge platform (grand-challenge.org, 2024), we gain insights into the model's current performance and usability. The rating scale, adapted from (Berta et al., 2021) , ranges from 1 (extensive effort) to 5 (no effort). Further details on this scale are available in the Appendix B. 
results: 
 Dice Scores per Class and Segment. The final 3D model was evaluated using the internal test set. To see the performance for different parts of the body, the performance with split into different segments. The model achieved high precision across all sections, reflected in the high Dice scores for SM, VAT, and SAT, with slightly more variability noted in the IMAT segmentations. The results are presented in Table 1 . For additional details, refer to the boxplot in Appendix C and the visual overview of a sample case in Appendix D. Reader Study. The aggregated scores from the reader study (Figure 2 ) indicate that most cases required minimal to no corrections for SM, VAT, and SAT, highlighting the model's high accuracy and reliability for these classes. Specifically, for SM, all readers assigned a score of at least 4 in 81 cases, denoting negligible to no correction needed, and in 97 cases, the score was above 3, suggesting limited correction effort. Similar results are obtained for SAT, for which 79 cases received a score of at least 4, while 94 cases scored above 3. VAT segmentation also demonstrated promising results, with 90 cases rated with a score of 4 or 5, and 97 cases scored above 3. However, IMAT proved more challenging. Two readers found the vast majority (90 cases) to require minimal correction, scoring 4.18 and 4.87 on average, while one reader's average score was 1.32, mainly due to unannotated fatty streaks in muscles, suggesting significant correction time. Figure 2  
discussion and conclusion: 
 Annotation-Efficient Segmentation Approach. This work introduces a novel annotationefficient training strategy for 3D body composition segmentation in the abdominal and pelvic cavity, which drastically reduces the need for expert annotations. By self-learning and using 2D and 3D models, only approximately 1% of the slices used in training required manual labeling. The evaluation shows that subsequent training steps effectively reduced the label noise in the 2D model's output. Additionally, incorporating a human-in-the-loop approach for expanding the training dataset further enhanced the final model's performance, demonstrating the strategic value of combining automated refinement with expert human oversight in the training process. The outcome is a robust model capable of producing high-quality segmentation masks, as evidenced by the evaluations. However, this approach comes with its challenges. While self-training is particularly effective for small labeled datasets (Bai et al., 2017) , its advantages are limited for larger datasets. Moreover, adding weak labels introduces the risk of overfitting to noise and adopting incorrect patterns. In this approach, the reliability is roughly assessed by human oversight. Inadequate masks lead to selected scans being redirected into manual single slice annotation, incorporating an element of active learning. Future improvements could involve automating this process through techniques like uncertainty estimation to flag poor examples. It is also valuable to investigate strategies to counteract negative impacts on the learning process, such as modifying loss functions ( (Ghosh et al., 2017) , (Zhang and Sabuncu, 2018) ) or employing sample weighting strategies ( (Ren et al., 2018) , (Mirikharaji et al., 2019) ). Regarding the sparse annotation approach, further promising ideas to explore involve 2.5D approaches for multi-view fusion (Zhang et al., 2022b) . Leveraging additional views for sparse annotations introduces more variability and has demonstrated encouraging outcomes (Cai et al., 2023b) . Future research should compare semi-and weakly supervised methods evaluated on datasets with wide field of views, such as body composition segmentation, since they present unique challenges. Robustness. Our model is trained on a diverse dataset that includes different slice thicknesses, convolution kernel types, low-dose CT scans, and images from multiple manufacturers, enhancing its adaptability to various image qualities and noise levels, particularly in low slice thickness scenarios.In evaluating the model, we noted its strong performance in segmenting SM, VAT, and SAT from early development stages, even for challenging tasks like distinguishing VAT as fat within the abdominal cavity from organ-encased and pericardial fat. However, difficulties arose with both small VAT volumes, complicating SM segmentation near organs, and high SAT or VAT distributions, leading to bigger segmentation gaps. This observation prompted a reevaluation of the training samples to underscore the significance of body type as a critical factor in sample selection. One challenge in creating a balanced dataset stems from the inability to determine fat and muscle distribution from standard scan data or BMI alone. To mitigate this, we propose adopting a sampling strategy that considers demographic characteristics, following the insights from (Magudia et al., 2021) highlighting the variance that comes from age, ethnicity, and sex in automated body composition analysis. Inclusion of IMAT. An interesting addition to our study is the inclusion of IMAT as a separate class, a feature often omitted in other studies due to the complexity and timeconsuming nature of its annotation. The Dice score indicates a reasonably high level of precision for IMAT segmentation; however, this metric prompts several questions. The absence of exclusively expert-annotated references could be limiting the Dice score's reliability, a factor that is particularly critical for accurately quantifying the relatively small volume of this class. However, even with references like that available, the reader study underscores concerns about whether voxel-based annotations of IMAT alone on CT images are adequate, highlighted by one reader's assessment that most cases required extensive annotation efforts. Given IMAT's small volume, partial volume effects, image noise, and even minimal inaccuracies could have a disproportionate impact on its measurement. A potential approach to these challenges might involve the use of a unified mask for IMAT and SM, coupled with histogram analysis to evaluate the overall attenuation within the muscle. This could provide insights into muscle quality or the patient's overall health status, although the specific application of such an approach remains to be fully explored.Limitations. Additionally, scans with severe beam hardening or strong truncation artifacts were excluded from the study. Future work, inspired by strategies such as those proposed by (Xu et al., 2023) for managing truncation artifacts on individual slices, is essential. An overview of our model's performance in scenarios complicated by artifacts and other challenging variations is detailed in the Appendix F, offering insights into its current robustness and areas for enhancement. The dimension of our internal test set is acknowledged as limited, underscoring the necessity for broader validation. A reader study incorporating external datasets has begun to affirm our model's efficacy and robustness, yet it is clear that further validation is needed. Conclusion. This work presents an efficient annotation strategy for 3D body composition segmentation that drastically reduces manual annotations while improving model accuracy via iterative improvements. By training with a diverse dataset and specifically identifying IMAT as a distinct class, our model aims to adapt to various imaging conditions and offers in-depth body composition analysis. The divergent assessments of IMAT by readers not only highlight the difficulties in standardizing its measurement but also point to an essential area for further investigation regarding the accuracy needed for CT imaging-based assessments. Overall, the ability to assess body composition in 3D enhances patient care by enabling precise monitoring of changes, providing deeper insights into patients' health status. Assessing these measurements holds potential for refining outcome predictions and diagnostics, especially when integrated with other health parameters.  
 
 Appendix:  


Decision: Three reviewers intend to accept this work, although, after the rebuttal, the authors provide no comparison to existing methods, which I think is a drawback of the paper. The iterative learning is a commonly used strategy for reducing annotation efforts in medical imaging. In my view, the novelty of this specific iterative learning strategy is to use 2D and 3D UNet methods together in the annotation process.  

Because muscle and adipose tissue segmentation is a relatively new field, and the experiment includes a multi-reader study, this paper can provide more insights for other researchers to develop similar tools or datasets. Thus, I align with the reviewers’ decision to accept this paper. I recommend that in the future the author could enhance their contribution by comparing their work with other existing work such as [1].

[1] Blankemeier, Louis, Arjun Desai, Juan Manuel Zambrano Chaves, Andrew Wentland, Sally Yao, Eduardo Reis, Malte Jensen et al. "Comp2comp: Open-source body composition assessment on computed tomography." arXiv preprint arXiv:2302.06568 (2023).

Reviews: review: paper_summary: paper_summary: This research introduces an annotation-efficient strategy for 3D body composition segmentation using CT images, addressing challenges in manual annotations. The study employs an iterative self-learning approach, transitioning from a 2D to a 3D model, significantly reducing manual annotation efforts. The final model demonstrates excellent segmentation accuracy for skeletal muscle, adipose tissues, and achieves high Dice scores. The significance lies in offering a robust tool for precise body composition analysis, enhancing diagnostic and prognostic assessments in clinical settings, with potential applications in fields such as oncology.
main_review: main_review: While the paper addresses the concerns raised in the review, the paper still does not have any comparisons to existing methods. Since both the dataset and method is new in the paper, it makes it impossible to comment of the effectiveness of the method when compared to previous methods and hence I maintain my rating but bump to borderline accept.
strength_weakness: strength_weakness: One potential weakness of the paper lies in the limited size of the internal test set (20 cases), raising concerns about the generalizability of the proposed model. Additionally, the lack of a comprehensive comparison with existing state-of-the-art methods in the field may impact the paper's ability to establish its superiority. While the iterative self-learning strategy is innovative, the paper could benefit from a more thorough exploration of potential limitations and challenges associated with this approach. Further, the absence of a detailed discussion on the potential impact of imaging artifacts and variability in scan qualities on the model's performance is a notable gap. Addressing these aspects would enhance the paper's completeness and credibility.
questions: questions: In their rebuttal, it would be valuable for the authors to offer further details on the criteria guiding the selection of the 20 cases in the internal test set, ensuring transparency and addressing concerns of potential biases. A more in-depth discussion on the limitations and challenges associated with the proposed iterative self-learning strategy, along with concrete strategies for improvement, would strengthen the paper. Additionally, emphasizing the real-world implications of the model by discussing its performance in the presence of imaging artifacts, noise, and variations in scan qualities would contribute to a more comprehensive understanding of its robustness. To enhance the novelty of their approach, the authors are encouraged to provide a more thorough review of related work, particularly focusing on existing methods for 3D body composition segmentation. Addressing these aspects in the rebuttal would positively impact the paper's clarity, credibility, and potential impact on the scientific community.
paper_summary: paper_summary: The authors propose an annotation-efficient strategy for 3D images with a human in the loop. Their strategy requires only a number of slices (2D images) to be segmented initially instead of whole scan volumes. The initial 2D annotated slices are then used to train a 2D model and infer segmentations of all other slices in the dataset. The predicted masks are then treated as a 3D segmentation and a 3D model. Finally, the annotator adjusts 3D model's predicted masks if necessary, and the resulting predictions are used for training a refined 3D model.

Their strategy is evaluated on the task of 3D body composition segmentation - skeletal muscle (SM), visceral adipose tissue (VAT), subcutaneous adipose tissue (SAT), and inter/intra-muscular adipose tissue (IMAT). They used an internal train (116 scans) and test (20 scans) set, and an additional external test set for visual evaluation (KiTS21 dataset). The visual evaluation process presented the predicted segmentation to three radiologists who used a 5-point scale to describe the manual correction effort that the predictions would need.

The paper demonstrates that high results were achieved with only 417 initially annotated slices. Their 2D to 3D strategy is clever and simple, and could be very useful in cases where segmentations are very dense and require a lot of effort to be done in 3D manually.
main_review: main_review: The proposed method is sound. The task of body composition is appropriate for the problem that the proposed method addresses. I only found minor problems in writing and had a few questions out of interest rather than out of concern.
strength_weakness: strength_weakness: - Would identifying and refining the 2D model's prediction and training one 3D model instead of two be sufficient? On the other hand, this might not be as important of a question - I assume that the time to train the 3D model was relatively short.
- Writing can be improved
questions: questions: - Minor question: how was this implemented? Was something like MONAI Label used or were the steps done in more of a manual way?
paper_summary: paper_summary: In this paper, the authors propose an iterative self-training strategy for annotation-efficient segmentation of body composition for the abdomen and pelvis.  The task focus on 3D medical image segmentation task learning with 2D sparse annotations, including the segmentation of  four categories: SM, IMAT, VAT, and SAT. The proposed method is evaluated on several private and public datasets.
main_review: main_review: The author's revised version provides a much smoother overview of the overall objectives and related work. The readability of the article has been significantly improved. However, my main concern lies in the fact that this paper does not compare proposed method with state-of-the-art methods. Instead, it only presents a baseline implementation on a new segmentation task. Therefore, I will maintain my initial rating.
strength_weakness: strength_weakness: The introduction states several annotation-efficient efforts, like learning with noisy, weak annotations or pseudo labels.
Among annotation-efficient approaches, the task in this paper is 3D segmentation with 2D sparse annotations, several related work should be introduced and discussed.

[1] Bitarafan, Adeleh, et al. "3D image segmentation with sparse annotation by self-training and internal registration." IEEE Journal of Biomedical and Health Informatics 25.7 (2020): 2665-2672.
[2] Cai, Heng, et al. "3D Medical Image Segmentation with Sparse Annotation via Cross-Teaching Between 3D and 2D Networks." International Conference on Medical Image Computing and Computer-Assisted Intervention, 2023.
[3] Zhang, Yichi, et al. "Bridging 2D and 3D segmentation networks for computation-efficient volumetric medical image segmentation: An empirical study of 2.5D solutions." Computerized Medical Imaging and Graphics 99 (2022): 102088.

For Kits 21 challenge which focus on kidney tumor segmentation, is there ground truth annotations of targets in this work? Or did the authors annotate corresponding targets. I didn’t see this information in the manuscript.

It would be interesting to compare proposed method with related work stated above, and 2D semi-supervised methods, to further validate the effectiveness of proposed method.

Minor issues: missing reference in p2 paragraph2
questions: questions: Please see detailed comments.

Comments: title: The discussion period begins
comment: Dear reviewers and authors,

Thank you for your contribution to MIDL24. The discussion period begins! I encourage all reviewers and authors to participate in the discussion to address questions and clarify uncertainties. 

Thank you!
We thank the reviewers for their constructive feedback and the recognition of the strengths in our work.
We agree that there is room for improvement in writing and communication. We have taken your suggestions into consideration and have revised the abstract for better flow and clarity. Additionally, we have meticulously reviewed the document to address and correct missing citations and improve sentence structures.

Regarding the question of refining the 2D model's predictions and potentially training one 3D model instead of two, our approach was driven by a desire for granularity in model performance. The decision to train two models allowed for more focused optimization on this specific segmentation challenges, thus enhancing overall accuracy.  However, we acknowledge that consolidating training into a single 2D model and a single 3D model could offer advantages in terms of training time and resource allocation, making it a valuable point for future exploration.

### *Minor question: how was this implemented? Was something like MONAI Label used or were the steps done in more of a manual way?*

For the implementation, we trained the 2D model using our own U-Net architecture and pipeline, granting us flexibility for fine-tuning and exploring strategies like adjusting regularization and layer modifications. Ultimately, we achieved best results with a simple training approach, that could easily be implemented using MONAI. The 3D model was trained with the nnUNet framework, using its default settings without additional modifications.
We thank the reviewers for their insightful comments on our paper, which have highlighted its strong aspects and, more crucially, the sections that demand further clarification and improvement which helped us to increase the quality of the manuscript. We address each of their points as follows: 

### *The introduction states several annotation-efficient efforts, like learning with noisy, weak annotations or pseudo labels. Among annotation-efficient approaches, the task in this paper is 3D segmentation with 2D sparse annotations, several related work should be introduced*

We greatly appreciate the feedback regarding the need to introduce and discuss annotation-efficient methods, particularly in the context of 3D segmentation with 2D sparse annotations. This revision not only broadens the scope of our literature review but also strengthens the foundation of our study by showcasing the effectiveness of combining semi- and weakly-supervised segmentation approaches. 
We included a paragraph to the introduction about how sparse annotations can significantly improve efficiency and accuracy by leveraging the correlation among slices, outperforming traditional methods that use scribbles or bounding boxes. It mentions approaches using registration modules to generate pseudo labels ((Li et al., 2022), (Cai et al., 2023a), (Bitarafan et al., 2020)) and introduces a cross-teaching method ((Cai et al., 2023b)) that increases consistency between 3D and 2D network predictions.

### *… and discussed. It would be interesting to compare proposed method with related work stated above, and 2D semi-supervised methods, to further validate the effectiveness of proposed method.*

The discussion of these approaches raises intriguing possibilities for future research, particularly in the context of segmentation tasks such as body composition for the abdomen and pelvis, which comes with unique challenges. The scans used in this analysis cover an extensive field of view, which encompasses different anatomical structures and therefore high variability. The segmentation problem includes challenging details in classes, like IMAT and VAT. It would be interesting to see how other methods perform confronted with such conditions and what challenges arise.
Other methods reviewed in the introduction were evaluated on MMWHS (CT, heart substructures) (Cai et al., 2023b), on LiTs (CT, liver tumor), KiTS19 (CT, kidney tumor), LA (MR, left atrium) (Li et al., 2022, Cai et al., 2023a). The closest related validation was performed by (Bitarafan et al., 2020), where they benchmarked their method against other weakly supervised approaches using the Visceral dataset, encompassing various organs and the psoas muscle for segmentation, as well as the CHAOS dataset (CT scans of the kidney, liver, and spleen). Their technique demonstrated significant improvements in segmentation accuracy on sparsely annotated data compared to similar methods. However, the outcomes on the Visceral dataset showed greater variability, potentially attributable to the broader field of view involved in the segmentation tasks.

The goal for our paper was the development of a 3D segmentation model for body composition and we use this method to overcome the challenges that come this it. For further technical validation of our method in comparison to sparse annotation approaches and 2D semi-supervised methods, we would need to annotate more data, (e.g. annotations from different planes as it is required for the method of Cai et al., 2023b). We look forward to further discussions with our peers and potentially collaborating with other researchers to apply these advanced methods to our dataset, despite the current limitations in annotated data.

We added a paragraph to the discussion to highlight future directions: 

Further promising idea to explore involves 2.5D approaches for multi-view fusion (Zhang et al., 2022). Leveraging additional views for sparse annotations introduces more variability and has demonstrated encouraging outcomes (Cai et al., 2023b). Future research could focus on comparing semi- and weakly supervised methods on datasets with wide field of views, such as body composition segmentation.

### *For Kits 21 challenge which focus on kidney tumor segmentation, is there ground truth annotations of targets in this work? Or did the authors annotate corresponding targets. I didn’t see this information in the manuscript.*

We only used the CT scans from the Kits dataset for our reader study, since they are publicly available. We automatically generated masks for these scans using our method and asks experts to assess the quality. No manual labels were created. We decided in favour of this public data set in order to have an additional external data set for evaluation.
We thank the reviewers for their insightful comments on our paper, which have highlighted its strong aspects and, more crucially, the sections that demand further clarification and improvement which helped us to increase the quality of the manuscript. We address each of their points as follows: 

### *In their rebuttal, it would be valuable for the authors to offer further details on the criteria guiding the selection of the 20 cases in the internal test set, ensuring transparency and addressing concerns of potential biases.* 

We agree with the reviewer on the importance of ensuring transparency, thoroughly investigating potential biases, and reporting them.
We selected our samples based on the field of view, focusing on opportunistic scans from patients undergoing routine CT procedures in clinical practice. Our sampling was stratified by age and sex to ensure diversity within our study population. However, we regret to inform that we cannot supply additional information regarding BMI or specific diagnoses for these individuals, as this information is not so easy to extract due to the anonymisation process. In our future work, we will also try to obtain this information in order to further increase the interpretability of the results. For our external test set, we chose 100 cases out of 300 available in the KiTS dataset, again prioritising the field of view (L1 - L5) and ensuring stratification by BMI, age, and sex to foster a comprehensive analysis.
To aid in the understanding of our dataset composition, we have included a table in the appendix that provides an overview of the sex, age, and acquisition parameters for our internal test and training sets, as well as a separate overview for the external test set used in the reader study, which additionally includes BMI data. We believe this detailed breakdown will offer clarity and support the validity of our findings.

### *A more in-depth discussion on the limitations and challenges associated with the proposed iterative self-learning strategy, along with concrete strategies for improvement, would strengthen the paper.* 

We included a paragraph in the discussion about challenges associated with self-learning (such as the risk of overfitting to noise) and suggest future improvements (such as uncertainty assessments and exploring strategies like modifying loss functions and sample weighting to mitigate negative impacts on learning).

### *To enhance the novelty of their approach, the authors are encouraged to provide a more thorough review of related work, particularly focusing on existing methods for 3D body composition segmentation.*

To provide a more thorough overview of related work, we included additional references for studies focusing on single slice segmentation (for L3, L3/L4, pelvis, multiple heights, end-to-end solutions). Regarding existing methods for 3D body composition, we added two references for non-DL methods and three references for DL models (Liu et al., 2020), (Fu et al., 2020), (Dai et al., 2024). We focused on DL approaches for muscle and adipose tissue segmentations using CT scans and excluded methods that only segmented adipose tissue or muscles and methods that work for MR images. 

### *Additionally, emphasizing the real-world implications of the model by discussing its performance in the presence of imaging artifacts, noise, and variations in scan qualities would contribute to a more comprehensive understanding of its robustness.* 

To enhance our model's robustness across diverse clinical settings and scan protocol variances, we incorporated scans from various manufacturers and acquisition parameters. Our training set specifically included low-dose scans and contrast-enhanced scans, alongside a broad spectrum of kernel types that introduce different levels of noise, all aimed at improving the model's adaptability. Based on this, we expect a robust performance, however this remains to be investigated in a larger validation study with additional external data. We included an overview of failure cases in the appendix F. This section illustrates challenges stemming from artifacts, truncation, as well as conditions such as dilated bowel structures and fluid in subcutaneous adipose tissue.

### *Consider expanding the discussion on the clinical implications of the proposed model, emphasizing how it can contribute to improving patient care and outcomes in real-world medical scenarios.*

We propose to add this to the conclusion (enabling precise monitoring of changes, providing deeper insights into patients’ health status, assessing these measurements holds potential for refining outcome predictions and diagnostics)

### *If available, consider providing publicly accessible source code or datasets to facilitate reproducibility and allow the research community to further validate and build upon the proposed model.*

We are planning on making the trained model available on grand-challenge up on request for inference.