Revolutionizing Medical Image Segmentation: A Deep Dive into Challenges and Future of Federated Learning

1. Introduction

Medical image segmentation is an important step in medical image analysis, which is to find particular regions or structures in an image and do segmentation. The work presented here does not appear to be essential for clinical diagnostics and treatment planning, but is foundation to a standard process in clinical diagnostics and treatment planning, serving roles from assessing therapeutic responses to complex surgical planning, and image guided intervention [1,2,3]. Large volumes of data have been generated by various imaging modality such as MRI, CT scan, etc. which gives insight at anatomical features and pathology like tumors [4,5,6,7]. Medical image segmentation in complex cases like brain tumors is difficult and manual segmentation is even harder. They include variability in tumor appearance, which is high and human error, which is likely to grow, there is a steady demand for automated segmentation solutions [8]. In this area, significantly advanced progress has been made over the past decade by the use of convolutional neural networks (CNNs) to improve in imaging analyses for the diagnosis and management of cases of Covid 19 and autism spectrum disorder [9,10]. It is still very challenging to apply DL in medical image segmentation. Medical datasets are often complex with huge sizes which the processing capabilities of conventional DL models are not sufficient at scale. Carefully data privacy regulations for patient data (e.g. the Health Insurance Portability and Accountability Act, or HIPAA), make it difficult to concentrate data for training of DL models. These regulations ensure a remarkably high level of data privacy, which precludes the sharing of personal patient information and therefore acts as a barrier to the accumulating of data across providers of the healthcare. Federated learning (FL) introduced in 2016 is a novel paradigm that can address such barriers with a decentralized data training process. Machine learning models are trained across edge devices/remote servers with local samples in FL and only receive the model updates to a central server. Not only does this method secure patient privacy, but it also makes the bandwidth needed for moving large datasets impractically low [12,13]. There are not as many challenges with federated learning as with learning on a central server. Data is non-IID thus, model performance and accuracy can be impacted by the non-independent and identically distributed (non-IID) nature of data across the diverse healthcare facilities. Most supervised learning methods used in FL are resource intensive and error prone when relying on acquiring enough data for training. To ensure federated learning frameworks for medical image segmentation are well performing, these challenges need continuous improvement in local training processes as well as global model aggregation to optimize the effectiveness of the framework. We then critically review existing studies under the domain of medical image segmentation on federated learning, highlighting the statistical significance improvements obtained by FL in terms of accuracy and efficiency in training the model [14]. We also discuss statistical methods for overcoming variance related problems posed by data distribution and annotation in FL, in order to give a representative summary of the body of work and to identify directions for future research and development in this novel space [15].

1.1. Related Surveys

It is in the landscape of federated learning (FL) in the area of medical image analysis rich with many different applications from enhanced privacy in data sharing to more robust machine learning models in various databases. A review of the present literature suggests that many studies do not provide sufficient technical details regarding how FL improves healthcare systems. Some research reflects on healthcare applications in the broader sense of electronic health records [17], Internet of Medical Things [18], but there is a lack of special discussions concerning medical imaging. Unlike these general surveys, the most recent ones such as Sohan et al., feature more detailed reviews of federated learning applications in medical image segmentation and close gaps in previous reviews.

1.1.1. Distinct Coverage

Most prior reviews from December 2022 cover the most recent advancement in FL models and methods for medical image segmentation [19], but we study from January 2017 to March 2024. The extended timeline enables us to examine more diverse kinds of innovations in FL, especially those that render the segmentation accuracy improvement in the context of the federated environment which was not discussed in depth in earlier works.

1.1.2. Tools and Software

Existing literature surveys have, for the most part, failed to identify the exact tools and software underpinning FL in medical image analysis. Our review details some of the necessary FL software systems and their uses in medical imaging, thereby addressing this oversight. There is a deficiency in existing literature as we provide a comprehensive overview of benchmark datasets and latest FL platforms that are essential for the advancement of research in the field.

1.1.3. Future Direction and Research Challenges

Our survey not only synthesizes current research but also outlines significant challenges and future directions in FL for medical image segmentation. We highlight the need for model generalizability across unseen client data—a critical issue as the adoption of FL grows. We identify potential research opportunities that could further the capabilities of FL in handling diverse and complex medical datasets [21].

This survey contributes to the field through several key activities:

• Literature Search and Refinement: We initiate our review with a meticulous search through Google Scholar, focusing exclusively on studies related to medical imaging modalities like MRI and CT scans. This targeted approach helps refine the scope of our review to include only the most relevant studies.
• Understanding the Shift to Federated Learning: We trace the evolution from traditional deep learning approaches in medical image segmentation to the adoption of federated learning, emphasizing the benefits of enhanced privacy and efficiency.
• Comprehensive Review of Federated Learning: Our analysis goes in-depth into the architectural frameworks of FL, discussing recent advancements and the technical challenges that arise with these new methods.

The remainder of this paper is structured to systematically discuss the methodologies, key phases of FL architecture, and a detailed evaluation of its applications in medical image segmentation. We discuss organ-specific segmentation tasks and present a detailed analysis of the tools available for implementing FL, setting the stage for future research explorations in this promising field [22].

Figure 1. A comprehensive demonstration of the study.

Figure 1. A comprehensive demonstration of the study.

2. Methodology

2.1. Searching Method

For this search, we merged our web scraper from Assignment 7 to specifically search for literature on Federated Learning related medical image segmentation [23]. By taking this focused approach, we guarantee the relevance of collected data for our synthesis, which is explained in Figure 2.

Figure 2. Flow chart for study selection.

Figure 2. Flow chart for study selection.

2.2. Database for Searching

Google Scholar was used as a primary database to find relevant articles that had been published. In order to have a more comprehensive collection of relevant literature, specific studies were retrieved from specialized databases (IEEE Xplore, PubMed) and stemmed from IDEAL. This approach provided a way to improve search results, drop duplicates, and remove articles that are not medically imaging related.

2.3. Keywords for Search

Search terms that we used included "deep learning", "medical image analysis", "segmentation", "federated learning", "privacy preservation". The keywords chosen were to catch the most latest and relevant advancements appeared in this field.

2.4. Eligibility Criterion

From the year federated learning was introduced in 2016, we reduced our search area to papers published from 2017 until 2024. At this stage there was a sudden increase in the number of relevant studies, from which we will choose 21 which meet the strict requirements of eligibility, referring to their straight application to medical image analysis [25]. This method of analysis included a large body of current and relevant research in the field.

3. Fundamentals of Medical Image Segmentation

Medical image segmentation can be defined as the task of partitioning an image into regions of interest which can then be used to accurately separate anatomical structures or pathologies [26]. Clinicians need to effectively plan diagnostics, treatments, identify abnormalities, and take quantitative assessments, and this segmentation is vital for that. For the purposes of enhancing the efficacy and precision of medical image analysis, accurate segmentation remains a key step in tumor delineation, organ delineation and image reconstruction processes [27]. We review four traditional segmentation methods used in medical image analysis for finding essence features (tumor) and degrade their performance and limitations. We also evaluate how effective a variety of different deep learning models are for segmentation tasks and describe their algorithms, architecture and challenges in their application. The models while proficient suffer from various issues:

• Small Sample Size Problem: Deep learning models require large, annotated datasets to effectively capture the complexity and variability of medical images. A major challenge is posed when there is not enough annotated data, which inhibits the performance of the models to operate with high accuracy in various medical settings. And it's not helped by the reliance on medical professionals to not only annotate comprehensively pieces of this data-intensive model, but then to annotate them again and again and again as the model is trained on them.
• Data Privacy Preservation: Large datasets in medical imaging raise challenges to data privacy, especially in respect to private modalities, such as CT scans and MRIs. This poses the problem of aggregating and sharing medical imaging data for research and application purposes under rules such as the Health Insurance Portability and Accountability Act (HIPAA), that lays down strict guidelines in management of personal patient data.

Table 1. Deep Learning Models for segmentation tasks.

Segmentation Methods	Algorithms	Architecture	Limitations
CNN Architectures	U-Net [23]	Expanding symmetric path for context collection and accurate localization.	It degrades with complex anatomical structures.
	Deep Lab [24]	Uses atrous convolution to expand the field of vision without extra parameters.	High computational cost.
	Mask R-CNN [25]	Instance segmentation with additional branch for bounding box and segmentation mask prediction.	Requires a large amount of annotated data; class imbalance is challenging.
	Graph-CNN [26]	Models’ spatial interactions between image pixels using graph structures.	Requires careful design of graph-building algorithms; struggles with irregular graph topologies.
Region-Based Methods	SRM [27]	Combines regions based on statistical metrics.	Data heterogeneity with varying intensity distributions degrades performance.
	Atlas-Based	Uses anatomical atlases or templates for segmentation.	Errors occur in cases of anatomical variations or pathological conditions; time-consuming.
Edge Detection	Segmentation [28]	technique based on edge detection.
Other Methods	Domain Adaptation Techniques [29]	Enhances segmentation models with cross-domain generalization.	Target domain data with annotations not widely available; training instability.
	Generative Adversarial Networks (GANs) [30]	Uses adversarial networks to generate realistic images matching the input for improved segmentation.	Training instability

Table 1. Deep Learning Models for segmentation tasks.

Segmentation Methods	Algorithms	Architecture	Limitations
CNN Architectures	U-Net [23]	Expanding symmetric path for context collection and accurate localization.	It degrades with complex anatomical structures.
	Deep Lab [24]	Uses atrous convolution to expand the field of vision without extra parameters.	High computational cost.
	Mask R-CNN [25]	Instance segmentation with additional branch for bounding box and segmentation mask prediction.	Requires a large amount of annotated data; class imbalance is challenging.
	Graph-CNN [26]	Models’ spatial interactions between image pixels using graph structures.	Requires careful design of graph-building algorithms; struggles with irregular graph topologies.
Region-Based Methods	SRM [27]	Combines regions based on statistical metrics.	Data heterogeneity with varying intensity distributions degrades performance.
	Atlas-Based	Uses anatomical atlases or templates for segmentation.	Errors occur in cases of anatomical variations or pathological conditions; time-consuming.
Edge Detection	Segmentation [28]	technique based on edge detection.
Other Methods	Domain Adaptation Techniques [29]	Enhances segmentation models with cross-domain generalization.	Target domain data with annotations not widely available; training instability.
	Generative Adversarial Networks (GANs) [30]	Uses adversarial networks to generate realistic images matching the input for improved segmentation.	Training instability

4. Federated Learning Key Phases

Federated learning (FL) is a branch of machine learning where multiple entities collaborate to train a model while keeping their data decentralized. Unlike traditional centralized systems, FL operates under a structure that preserves privacy by distributing data across various locations [28]. Key phases of FL include the local training of models on dispersed datasets and the centralized aggregation of model updates, enhancing privacy and efficiency. This architecture and its operations are further detailed in subsections and illustrated in Figure 3.

4.1. Federated Learning Architecture

Federated learning (FL) represents a shift in machine learning, moving away from centralized data storage [29]. It utilizes model aggregation to train a global model collaboratively across multiple sites, which is then refined locally at each site as shown in Figure 4. This approach allows for enhanced privacy and local fine-tuning without compromising data security.

4.2. Principle of Federated Learning

Federated learning begins by selecting a healthcare application, like image segmentation, and preparing a global model with initial parameters [30]. This model is distributed to various clients, such as hospitals or research centers, selected based on specific criteria. Each client trains the model locally on their dataset, enhancing privacy since data never leaves its original location.

After local training, clients upload only their model's updated parameters to a central server. This server aggregates these parameters to refine the global model, ensuring it benefits from diverse data without actual data transfer. This cycle of training and aggregation continues iteratively until the model achieves optimal performance, as monitored by the convergence of the server’s loss function [31]. This method not only protects data privacy but also leverages decentralized data sources to improve model accuracy and robustness, as depicted in Figure 5.

4.3. Data Distribution Methods in FL

Federated Learning (FL) employs various data distribution strategies to handle diverse datasets across different clients while maintaining data privacy and security. The three primary methods used are

HFL / Sample partitioned learning: Also called HFL, HFL is used where various healthcare institutions possessing separate patient datasets and intending to learn a model [32]. Across these institutions, the datasets are similar in their features but differ in individual samples. The model is learnt on each participant’s local data and each participant sends those updates to a central server for aggregation. This method is widely applied in situations when the data privacy is very important, for example, healthcare and IoT devices, since it allows to use the shared statistical features without revealing the individual data points.

Vertical Federated Learning (VFL): In VFL, samples are held by all participants with some being different (and relevant) subsets of some of these samples. This model is appropriate when the institutions that collect data on the same individuals may have collected different types of data. Such as, in one hospital, we could have lab results and in the other, we could possibly have radiographic images. These diverse features are integrated into a multi feature model across data silos without data sharing, thus keeping privacy [34].

Federated Transfer Learning (FTL): It is often the case that one is interested in FTL where the feature space or sample space are not fully shared across all participants, and such a case occurs in many real-world applications. This is featured knowledge transfer from two related but distinct areas. Hospitals in different region may have different data due to differences in populations served. This enables FTL to leverage differences, using one model as a starting point and adapting it to another in order to successfully generalize across many different settings.

Figure 6. Categories of data distribution (1) HFL (2) VFL (3) FTL.

Figure 6. Categories of data distribution (1) HFL (2) VFL (3) FTL.

4.4. Data Privacy and Security Methods in Federated Models

Since data privacy and security remains paramount to conducting Federated Learning (FL) in some markets such as medical imaging that do not want to share the patient data, the additional measures needed to enforce privacy are critical to the success of Federated Learning. In FL, the term privacy actually refers to how data is handled, namely that it is used and accessed as per strict regulatory requirements without the data being exposed or shared. The first is security which means protecting the data from NOT authorized access and protecting the data from being susceptible to cyber threats while ingesting and forwarding the data within the FL processes [36].

The widely used technique in FL to improve data privacy is Differential Privacy (DP). The basic idea is to add noise to the dataset or results of the algorithm so the sensitivity of each input or one data entry’s feature is masked [37]. By this method, we make sure that the result of the analysis doesn’t change much when a single data point is either included or excluded, and it prevents attackers from deducing the personal data from checks on the shared model updates. Differential Privacy is ideal in preserving patient data anonymity while allowing information from aggregate data updates to be extracted.

Another approach to FL frameworks’ security is Blockchain Technology. In the context of FL, it can be enlisted as a secure ledger for model updates [38]. Upon each transaction in a blockchain, it is encrypted and linked to the previous one, so that a transaction can’t be tamed. While this method also improves the security of data exchanges in FL, it also provides a verifiable audit trail of all operations for a crucial element in complying to medical data regulations.

These methodologies show that FL has the capability to uphold the strictest data privacy and security standards and to aggregate large amounts of distributed data for medical research and application [39]. These strategies help mitigating risks of data breach and such unauthorized access to FL so that FL still retains its robust nature as a tool for collaborative machine learning, while still keeping patient confidentiality intact.

Figure 7. Work flow of differential privacy.

Figure 7. Work flow of differential privacy.

Blockchain technique: Federated learning (FL) systems are made more secure and transparent through the use of blockchain technology. As we mentioned in the study of [40], they have specifically deployed blockchain to protect the FL model by means of a novel approach with the use of Data Management Agents (DMAs). Each piece of patient data (medical images) is given a unique id. This ID is essential because any data request must be traceable and secured.

When the system has received a data analysis request, it checks first for the presence of the IDs in a secure database. When data entry ID is missing, meaning a new change is what is being entered, the system will generate a new block and ID on the blockchain. After storing the transaction in this block and connecting it to corresponding patient data, these 2 are sent to the database [41]. This process secures the data and records an immutable ledger of every such transaction as shown in Figure 8, making the system more audit able as well. The combination of these two gives a pipe to the privacy and security structure of federated learning models, ensuring data traceability and securing it from unauthorized one.

5. Comprehensive Study of Federated Learning Frameworks for Medical Image Segmentation

The strengths and the challenge of 14 recently studies on federated learning models used for medical image segmentation. The results are summarized in Table 2 in concise form.

5.1. Federated Learning Framework for Medical Image Segmentation

Dealing with medical image segmentation in a federated learning (FL) setting involves a series of consecutive steps. First data from different imaging technologies such as MRI scans, CT scans, and ultrasound are initially gathered from multiple sources. These data are preprocessed by resizing, augmentation and cropping them to standardize input into the segmentation tasks. Every participating institution or client then individually trains a local model on their own processed data so the sensitive medical data stays on the ground. Instead, the updates to the model are sent to a centralized server and aggregated to improve a global model [42]. Using information from other datasets, this model is more accurate and robust [43]. After updates to the global model are applied on each client specializing on local data, the updates are distributed back to each client to use locally or for additional training. In the case where the results of the federated segmentation process are not utilized locally but shared across the federated network, the data privacy is also preserved; collaborative learning is achieved. The results of evaluation of this FL framework and its effect on medical image segmentation are presented in Figure 9.

5.2. Effectiveness of Existing Methods

The current federated learning (FL) approaches for medical image segmentation have demonstrated good performance [44]. These methods are good at dealing with decentralized data, to add robustness and generalizability of the models. Federated averaging is one prominent technique that merges updates from many clients to enhance the global model’s performance while avoiding the direct data access, and preserve privacy [45]. It uses transfer learning and semi-supervised learning strategies to make the best out of scarce annotated data. In these methods, segmentation accuracy refinement is based on pre-trained models and unlabeled data. Though we have achieved some success with FL in medical image segmentation, there are still major hurdles that have limited the breadth of what could be done with it, which are discussed in full in order to understand their effects comprehensively.

5.3. Challenges of Existing Methods

Existing methods for federated learning (FL) for medical image segmentation suffer from several challenges in the real domain. The main problem concerns nonuniform distribution of data across clients: data in each site come from different imaging vendors and in different amounts. In real world segmentation tasks, this heterogeneity causes errors and poor performance of the federated models [46]. There is also a strong need of heavy annotated data for training these models. Creating large datasets that are labeled by expert clinicians is very expensive and labor intensive. Due to the fact that we have so little annotated data, it severely impacts the optimization processes on both the client and server sides, leading to overall inefficiency and scalability of medical imaging FL models [47].

6. Organ Specific Segmentation Task in Existing Federated Learning Models

6.1. Federated Learning Models for Breast Tumor Segmentation

In the area of image segmentation medical images (with organ specific tasks), include Federated learning (FL) for image segmentation, specialized models such as FedMix and FedA3I are tailored for the organ specific tasks. In this work, a deep Categorization network Unet model to classified four different datasets namely the BUS, BUSIRS, UDIAT, and HAM10K datasets along with last one inclusive of the multi-source dermatoscopic images of common pigmented skin lesions. We employ a fully supervised setting where we use weak as well as strong fully labeled data to dynamically adjust aggregation weights, improving feature representation and reducing the difficulty in the annotation of medical experts [48]. It does not study unsupervised learning techniques that would potentially be a disadvantage in less formal data environment.

The FedA3I model, developed for federated medical image segmentation (FMSI), implements an annotation quality aware aggregation as a non IID data aggregation mechanism and estimate pixel wise noise per each client data. We test this model on datasets such as ISIC 2017, BUS, BUSIS, UDIAT, among others, 2D ultrasound images, and using two local clients [49]. It enhances accuracy and robustness at the cost of complexity of maintaining the annotation quality and the pixel-wise noise, and both of which raise the computational and implementation challenges.

6.2. Federated Learning Models for Heart CT Images Segmentation

Cardiac image segmentation is vital to obtain quantitative imaging biomarker for personalized and accurate diagnostic and therapeutic strategies for cardiac diseases. Combining a U–net architecture with a federated equal choices method greatly boosts the performance in segmentation on cardiac CT images and diminishes the risk of overfitting [50]. The performance of the FedAvg approach is improved compared to with this, especially with a large data set like NSCLC-Radiomics, Pediatric-CT-SEG and LCTSC, which are important data for heart segmentation. Although it is one of the best methods, it has a lower score on the NSCLC-Radiomics set because of data distribution.

An experiment using Generative Adversarial Networks (GANs) based novel self-supervised federated learning model is tested over cardiac MRI datasets of 'M&Ms' challenge 2020 and ACDC challenge [51]. The achieved segmentation accuracy on this model is highly segmented and the effectiveness of convergence, scalability and adaptability to various federated domains make this model not only distinguishable from other models but is also an impressive model. Although this has not yet been implemented clinically, its potential in the federated context for improving cardiac image segmentation is promising for overcoming traditional obstacles in medical image segmentation [52].

6.3. Federated Learning Models for Multi-Organ Segmentation

Despite the large strides in multi-organ segmentation taken in recent research of federated learning (FL), there has been little work on making these models more efficient. In this work, the SegViz model shows high performance with respect to data heterogeneity and partially annotated data challenges, which it applies to liver and spleen CT images in the Medical Segmentation Decathlon (MSD) dataset [53]. SegViz also improves adaptation to domain shifts between datasets and is robust, and hence more useful in clinical settings, through its reduction of labeling efforts and its parameter sharing across clients.

The FedCross method is also aggregation-free, it is specifically adapted to tackle non-IID data distributions arising in multi-organ MRI datasets [54]. This model improves the system performance while producing uncertainty maps of the segmentation outcomes so that physicians are armed with more precise diagnostic tools. But there is a fear of consistency of the model performance on unexperienced datasets.

CT image segmentation of multiple organs has been tailored for a FL model, U-DANet [55]. In particular, on pancreatic segmentation, it provides better performance than earlier methods with fewer limitations noted in the literature. This ability to learn from limited training data and coordinate with uneven class distribution provides another indicator that U-DANet is well suited to boosting diagnostic accuracy for each organ type beyond the organ types used in the training. These studies demonstrate how FL can offer a solution for improving medical image segmentation in the face of critical issues such as data privacy, non-IID data and lack of annotations (see Table 3).

6.4. Characteristic Illustration of FL

Medical image segmentation using deep learning architectures, such as U-Net, ResNet, V-Net, use Federated learning (FL) to leverage federated data for which their architectures were customized in training anatomical contours from MRI, CT, and X ray scans. The training of these models is delegated to the institutions, but remains decoupled from the data while the rigorous privacy standards are met by keeping the data confined locally [56]. With the approach, it is demonstrated by key frameworks, such as FedAvg and FedProx, that model training can be optimized while preserving sensitive information from not being transmitted. In health care, these include the tools such as TensorFlow Federated (TFF), PySyft and NVIDIA Clara, which are the same credits as providing accessible, open-source platforms to deploy FL solutions to health care. TFF fits nicely with TensorFlow projects, PySyft enhances the security by sending the communications encrypted, and NVIDIA Clara centers on high performance medical imaging applications [57].

The performance evaluation of these FL systems is relying on the degree of handling data heterogeneity, scalability, and convergence efficiency [58]. We demonstrate that these FL models are robust and generalize effectively across the different healthcare settings on standard datasets such as BraTS, ACDC, MMs without compromising privacy of the patients’ data. The models learned in studies were based on 3–10 volunteer clients, and on the other hand, both generalization capabilities and optimization processes of federated models are influenced by the studies. In Table 3 and Table 4, the effectiveness and characteristics of these FL frameworks in medical image segmentation are detailed, and they are shown to work in real world scenarios [59].

(1)

Medical image segmentation for delineating organs and tumors is depended on deep learning architectures such as U-Net, V-Net, ResNet, and DenseNet [60]. Convolutional layers and skip connections, both of which are important when it comes to getting the precise segmentation, make these models unique for having the ability to capture intricate features in medical images. These models are adapted for distributed environments of federated learning (FL) in the context of privacy. Solving for the training on localized data overcomes the main privacy concerns, namely literary having the local data centralized in the same place.

(2)

Topics addressed by the fourteen reviewed studies are with regard to the application of FL in medical image segmentation as opposed to conventional deep learning ones generally relying on centralized data pools [62]. FL models keep different entities’ raw data private and ensure data integrity as different entities can be involved in model training without sharing data. The effectiveness of these FL models in medical image segmentation is evaluated in this review, looking at their capabilities, issues and how the model deals with challenges of both data privacy and institutional heterogeneity [63].

(3)

Datasets serve as an important part for the performance evaluation of FL models. Training and testing these models depend on such benchmark datasets, which usually include many medical images like MRIs and CT scans. This review studies the datasets used in the studied studies, with an emphasis on their size, kind and diversity, to understand how the FL models behave compared to different segmentation tasks [64].

(4)

Another important factor for medical image segmentation is image modality Mri, Ct, Xray and Ultrasound scan in their own ways for making an input about human anatomy resulting in different segmentation tasks respectively. This review highlights the fact that each dataset is evaluated with a specific image modality and, thus, a performance of FL models with different image modalities varies significantly [66].

(5)

Federated learning faces its own challenges in terms of the modalities of different images. An example of this is that MRI provides clear pictures of moisture tissues, whereas CT scans are better at observing bones and tough structures. Not only does each modality have different image quality, but also different data distribution among the participating clients, having different training dynamics and accuracy on FL models. We would like to discuss which modalities may require a finer attention in terms of focus and enhancement in FL frameworks, and also whether some of these modalities may be more difficult to provide to an appropriate training outcome and model precision [67].

(6)

The review closely reviews all specific organs that are under attack within current federated learning (FL) segmentation models for medical images. It points out to the focus of the segmentation on important organs of the body such as the brain, the lungs, the heart, and the liver, as well as the amount of research that has been done on each organ. The importance of this segmentation is to present research issues that are already well researched, and identify organs that require further study as the data on a particular organ is not complete or FL models are not working on it as expected [68]. These insights are important for judging in what direction research must focus to improve segmentation accuracy of organs that are less represented.

(7)

The other critical aspect is covered in the review is sample size. FL models’ training and evaluation effectiveness is directly dependent on the number of samples, or images that are available in each dataset [69]. The size of the dataset increases, the model performance improves by giving the model a wider basis for learning and generalization, though smaller dataset can potentially reduce these capabilities. It also calls the attention to the use of the sample sizes used in the studies and highlights how they are important to ensure the robustness and reliability of the federated learning models.

(8)

The number of clients participating in the federated learning setup also has an important influence in the efficiency of FL models. It is documented by this review that the number of participating clients, usually, is on three to ten institutions [70]. Thus, diversity and number of clients in particular affect generalization capabilities of models and bring in new variables (e.g., non IID data, increased communication overhead) that are major challenges when training models in federated settings. An analysis of client numbers facilitates evaluating of the scalability and practical performance of FL models for different institutional environments [80].

(9)

The review looks into the tools and the software platforms used for medical image analysis in federated learning frameworks [81]. This is a comprehensive overview of these tools, with description regarding the accessibility, architecture, ease of implementation, and also performance. This analysis helps in identifying the most appropriate and effective sources for FL tools for different medical imaging applications for matching tools to the right application. Table 4 of the review shows this detailed tool assessment, which provides a resource for researcher to find optimal tools for their particular needs [82].

7. Future Directions and Research Challenges

(1)

Scarcity of Annotated Data: One of the primary hurdles in federated learning is the limited availability of annotated data, which is vital for training accurate and reliable models. Self-supervised learning (SSL) strategies are increasingly recognized as a solution to leverage large volumes of unlabeled data alongside smaller annotated datasets [83]. SSL can significantly enhance model training by using unlabeled data to pre-train models before fine-tuning them on the scarce annotated data, improving both model accuracy and robustness.

(2)

Data Heterogeneity: Variability in medical data arises due to differences in imaging equipment, acquisition techniques, and processing protocols across various institutions [84]. This heterogeneity can severely impact the performance of FL models. Addressing these discrepancies involves implementing sophisticated preprocessing strategies, including normalization of imaging protocols and harmonization techniques. Current federated learning models like federated averaging may not always handle such data diversity effectively, especially when labeled data is unevenly distributed across participants. This calls for more advanced algorithms that can adapt to heterogeneous data without compromising model performance and accuracy.

(3)

Distribution Variance: Real world applications, for example, distribution variance across multicenter data set can present a wall to model convergence and deteriorate the local performance [85]. To address this, PFL attempts [86] to mitigate this by enabling sites to retain local parameters that better fit their data distributions. PFL works more on convolutional networks and hardly pay much attention to integrate the self-attentions to capture the long-range dependencies in data [87]. This reflects the lack of research for further personalization techniques that span a wider variety of network architectures, particularly those based on self-attention [88].

(4)

Generalizability to Unseen Data: A major bottleneck in multi-site federated learning framework is the extrapolation of models for data sources unseen [89]. Personalized models can outperform on local datasets, but underperform for new unseen datasets [90]. Strategies of continuous learning, fine tuning, assembling models could improve model adaptability and produce effective performance in a new participant site, leading to more good integration and scalability of federated learning systems in various clinical environment [91].

(5)

Aggregation Challenges: Model training and convergence rates vary depending on the quantity of data at a network site [92]. Federated learning approaches that are typical may not consider these discrepancies whose presence could distort the aggregate model performance [93,94]. It is important to counter this by using re-weighting of site contribution based on sample size or improving data representation from less represented sites. In particular [95,96], mentoring models to react to local data properties during training will facilitate consistency as well as effectiveness on domain-specific datasets [97].

8. Conclusion

We critically evaluate current state of FL in the field of medical image segmentation to highlight both the progress and the challenge in FL as well. According to the findings, FL has significantly helped the artificial intelligence in securing distribution of the data and keeping the privacy of users intact. The problem of developing models that generalize to different medical datasets is not a simple problem. The synthesis of these studies focuses a strong shift towards federated implementation of deep learning techniques with the implication of how the method can be applied to improve segmentation accuracy. By and large, however, and despite their great promise, the insights that we have drawn from it require further work on refining these models and dealing with the intricacies of practical, real world medical imaging settings.