This review aims to comprehensively analyse the current state of brain tumour segmentation using deep learning techniques, with a particular focus on advanced techniques such as U-Net architectures and transformers based models. To ensure a systematic and thorough examination of the literature, a structured search was conducted using electronic databases, including PubMed, IEEE Xplore, Science Direct, and Google Scholar.

Search Strategy: The search terms used included “brain tumour segmentation,” “deep learning,” “U-Net,” “MRI,” “BraTS challenge,” “glioma segmentation,” “convolutional neural networks,” “activation functions,” “transformers”, and “medical image analysis.” The review studies were considered to capture the most recent advancements, especially those related to the BraTS challenges. Only articles published in English were included.

Inclusion Criteria: The review included peer-reviewed journal articles and conference papers that focused on brain tumour segmentation using deep learning techniques. Research involving U-Net architectures or their variants was prioritised. Papers discussing next generation advances techniques in brain tumor segmentations.

Exclusion Criteria: Studies not related to brain tumour segmentation or those not utilising deep learning methods were excluded. Non-English publications were also excluded.

Data Extraction and Synthesis: Relevant information from the selected studies was extracted, including the proposed methods, datasets used, CNN, activation functions, performance metrics, and key findings. Emphasis was placed on studies that provided critical insights into the advancements, challenges, and future directions of U-Net architectures, and transformers in brain tumour segmentation.

The BraTS (Brain Tumor Segmentation) challenges have served as a pivotal benchmark for evaluating AI-driven segmentation methods, catalyzing remarkable progress in the field [ 3 , 5 , 8 ]. Traditional machine learning techniques, while initially useful, struggled with the heterogeneous appearance of tumors in BraTS multi-modal MRI datasets, achieving limited Dice scores (typically 60% - 75%). The introduction of deep learning, particularly U-Net variants [ 9 ], dramatically improved performance (Dice ~85% - 90%) by automatically learning discriminative features across T1, T2, FLAIR, and T1ce sequences. Subsequent BraTS editions witnessed transformer-based models like Swin UNETR [ 10 ] pushing boundaries further (Dice > 90%) through global context modeling, while diffusion models enhanced edge detection in tumor sub-regions. The challenges also spurred innovations in federated learning [ 11 ] to address data privacy concerns and weakly supervised techniques [ 12 ] to mitigate annotation bottlenecks. Notably, BraTS-2023 highlighted how ensemble methods combining CNNs and transformers achieved state-of-the-art results (Dice ~92%), demonstrating the synergistic potential of hybrid architectures [ 6 ]. These methodological advances, rigorously tested through BraTS, have not only improved algorithmic performance but also translated to more reliable clinical decision-support systems, reducing inter-rater variability from 15% - 20% to under 5% in tumor volume estimation.

Before the deep learning era, classical ML techniques formed the foundation of brain tumor analysis by leveraging statistical models and manually engineered features.

Texture and intensity-based approaches were pivotal in early tumor characterization. Haralick features and Gabor filters extracted textural patterns from MRIs, while Local Binary Patterns (LBP) captured local contrast variations [ 13 ]. Histogram-based methods analyzed intensity distributions across T1, T2, and FLAIR sequences to identify abnormal tissue. For morphological analysis, Active Contours (Snakes) and Level Sets evolved initial contours to match tumor boundaries [ 14 ], with Region Growing techniques propagating seeds based on intensity similarity [ 15 ].

Supervised methods enabled automated tumor classification using labeled data. Support Vector Machines (SVMs) with RBF kernels effectively separated tumor and healthy tissue by maximizing margin hyperplanes in high-dimensional feature spaces [ 16 ]. Random Forests improved robustness through ensemble decision trees that handled multi-class segmentation tasks [ 17 ]. While simpler, k-Nearest Neighbors (k-NN) provided baseline performance by classifying voxels based on neighboring annotated samples [ 18 ].

These techniques identified tumor regions without prior labels. K-means and Fuzzy C-Means (FCM) clustered voxels based on intensity similarity, with FCM allowing partial membership to account for tissue heterogeneity [ 19 ]. Gaussian Mixture Models (GMMs) offered probabilistic segmentation by fitting MRI intensities to multiple Gaussian distributions, particularly effective for differentiating tumor sub-regions.

Deep Learning has revolutionized brain tumor segmentation through its ability to automatically learn hierarchical features from medical images without manual feature engineering.

2D CNNs established foundational architectures for medical image analysis. The U-Net architecture [ 7 ] became the gold standard by combining an encoder-decoder structure with skip connections to preserve spatial details. SegNet [ 20 ] improved computational efficiency by using pooling indices for precise upsampling. For volumetric analysis, 3D U-Net [ 21 ] extended this approach to process whole MRI volumes, while V-Net [ 22 ] incorporated residual connections to enhance gradient flow in deep networks. DeepMedic [ 23 ] introduced parallel processing pathways to capture multi-scale tumor features simultaneously.

Attention mechanisms significantly improved segmentation precision. Attention U-Net [ 24 ] learned to focus computational resources on tumor regions while suppressing irrelevant areas, enhancing the localization of complex tumor boundaries. Squeeze-and-Excitation blocks [ 25 ] dynamically recalibrate channel-wise feature responses, allowing the network to emphasize informative features while diminishing noise. Hybrid architectures like DenseUNet [ 26 , 27 ] employed pyramid pooling to capture contextual information at various receptive fields, which is essential in recognizing tumors of varying sizes and shapes. These designs offer richer semantic understanding and more robust feature representation. Further advancements incorporated residual connections, dilated convolutions, and deep supervision to enhance learning stability and accuracy.

Networks like DeepLabV3+, and High Resolution Network (HRNet) pushed the boundaries by integrating high-resolution representations and spatial hierarchies, improving both edge delineation and intra-tumoral heterogeneity recognition. Collectively, these innovations in CNN architecture have led to notable improvements in segmentation accuracy, sensitivity, and clinical applicability, marking a significant leap from early deep learning models.

U-Net is a convolutional neural network (CNN) architecture designed for biomedical image segmentation [ 7 ], featuring a symmetric encoder-decoder structure with skip connections to preserve spatial details.

Figure 3 illustrates a 3D U-Net architecture for image segmentation, detailing the input and output dimensions along with the constraints and costs associated with the network’s operations. The input image undergoes a series of transformations through the network, with dimensions progressively changing from 64 × 64 to 1024 × 1024 and then downsampling back to 64 × 64. The network employs upsampling (denoted as “Up”) with a cost of 2 × 2 and standard convolutions with a cost of 1 × 1. A key constraint is that the architecture avoids 1 × 2 operations, ensuring specific dimensional transformations. The output image mirrors certain dimensions of the input (e.g., 256 × 256, 32 × 32, etc.), highlighting the U-Net’s symmetric structure where high-resolution features are combined with upsampled outputs to achieve precise segmentation. This design is critical for tasks requiring detailed spatial accuracy, such as medical image segmentation, where the U-Net’s ability to capture both local and global features is essential. The inclusion of cost metrics suggests an emphasis on computational efficiency, which is vital for practical implementations. Overall, the image encapsulates the U-Net’s hierarchical approach, balancing depth, resolution, and computational constraints to optimize performance.

Activation functions are crucial components of deep learning models, introducing non-linearity that a lows networks to approximate complex, non-linear functions and learn intricate patterns from input data [ 28 , 29 ]. In the context of medical image segmentation, the choice of activation function can significantly influence model performance, convergence speed, and the ability to mitigate issues like the vanishing gradient problem [ 30 , 31 ].

Table 1 shows the comparative analysis for various activation functions used in deep learning such as ReLU, Leaky ReLU, Swish, Mish, ELiSH, HardELiSH, Softsign, and Tanh, and these are among the most widely used in neural network models. The table highlights their individual strengths, such as computational efficiency (ReLU), smooth gradient flow (Swish and Mish), and ability to avoid vanishing gradients (HardELiSH). Limitations like dead neuron problems (ReLU) or computational cost (Mish) are also noted. The table further outlines their contributions to model performance in terms of convergence speed, stability, and accuracy.

These functions are critical for shaping the learning dynamics and overall performance of neural networks, influencing the convergence speed, gradient flow, and ultimately the model’s accuracy.

Mushtaq Salih et al. (2019) conducted a comparative study on activation functions and concluded that the HardELiSH activation function outperformed ReLU, particularly in addressing the vanishing gradient problem. Their findings demonstrated that HardELiSH not only mitigated this issue more effectively than ReLU but also led to an overall improvement in detection accuracy [ 37 ].

These advanced activation functions enable deep learning models to overcome the limitations of traditional functions, such as Sigmoid or Tanh, which suffer from the vanishing gradient problem in deeper networks. In the context of brain tumour segmentation, they provide essential benefits:

Incorporating these advanced activation functions in brain tumour segmentation models can lead to more accurate, robust, and clinically viable results, making them a vital component in modern medical imaging techniques. Continued research into novel activation functions remains crucial for further improving the performance and efficiency of deep learning models in medical image analysis, Mushtaq et al. (2025) [38] provided a detailed review of various activation functions, including their mathematical formulations and graphical representations, offering clear insights into their operational behaviors.

The advent of Vision Transformers (ViTs) has revolutionized medical imaging by overcoming the limitations of traditional CNNs, particularly in capturing long-range dependencies and global context. Swin UNETR [ 10 ] pioneered the use of hierarchical Swin Transformers for 3D medical segmentation, enabling more precise delineation of complex anatomical structures through shifted window-based self-attention. TransBTS [ 39 ] further bridged the gap between CNNs and Transformers, synergizing the local feature extraction strength of convolutional networks with the global contextual reasoning of Transformers, leading to robust performance in tumor and lesion segmentation. Meanwhile, the Medical Transformer (MedT) [ 40 ] introduced a breakthrough with gated axial attention, significantly improving computational efficiency while processing high-resolution MRI scans, making it feasible to handle large volumetric data without compromising accuracy. These innovations underscore the transformative potential of Transformer-based models in medical imaging, paving the way for more interpretable, scalable, and high-performance AI-driven diagnostic systems.

The Swin 3D U-Net is an advanced deep learning architecture that integrates the hierarchical Swin Transformer with the 3D U-Net framework to enhance volumetric medical image segmentation [ 41 ]. Unlike traditional U-Nets that rely solely on convolutional operations, this hybrid model leverages the self-attention mechanism of Swin Transformers to capture long-range dependencies in 3D medical scans (e.g., MRI, CT) while maintaining the U-Net’s ability to preserve spatial hierarchies through its encoder-decoder structure. The shifted window (Swin) mechanism improves computational efficiency by processing non-overlapping local windows in 3D space, reducing memory overhead compared to standard Vision Transformers (ViTs). By combining shifted window-based self-attention with 3D convolutions, the Swin 3D U-Net achieves superior performance in tasks like tumor segmentation, organ delineation, and multimodal image analysis, offering better scalability and accuracy than purely convolutional or transformer-based approaches. This architecture is particularly effective for high-resolution 3D datasets where global context and fine-grained localization are critical.

The Swin 3D U-Net architecture consists of an encoder, bottleneck, and decoder with specialized layers for hierarchical feature learning in volumetric medical images.

Encoder: Processes input patches (224 × 224, patch size 4) using Swin Transformer blocks, maintaining feature resolution while progressively downsampling via patch merging layers (2 × reduction in resolution, 2 × increase in dimension). This repeats three times.

Bottleneck: Two Swin Transformer blocks capture deep features without altering resolution or dimension.

Decoder: Uses patch expanding layers to upsample features (2 × resolution increase, halving dimension) and skip connections to fuse multi-scale encoder features, preserving spatial details [ 42 ].

Generative approaches have significantly expanded the possibilities of tumor analysis by leveraging advanced deep learning techniques to improve accuracy and realism in medical imaging. SegAN [ 43 ] introduced an adversarial training framework to generate more realistic and precise segmentation masks, enhancing the delineation of tumor boundaries in MRI and CT scans. Meanwhile, CycleGAN [ 44 ] addressed the challenge of unpaired data by enabling image-to-segmentation translation without requiring exact correspondences between input and output images, thus facilitating domain adaptation in heterogeneous datasets. More recently, diffusion models like DDPM [ 45 ] have achieved state-of-the-art results in tumor segmentation and synthesis by employing iterative denoising processes, which enhance the model’s ability to capture fine-grained details and reduce artifacts. These generative approaches not only improve diagnostic accuracy but also enable synthetic data generation for training robust models in scenarios where annotated medical data is scarce. Furthermore, their application extends to treatment planning, where realistic synthetic images can aid in simulating tumor progression and therapeutic responses. As these methods continue to evolve, they hold great promise for advancing personalized medicine and improving clinical decision-making in oncology.

These methods addressed data scarcity challenges. Contrastive learning frameworks [ 46 ] are effective pre-training on unlabeled MRI datasets. Pseudo-labeling and scribble learning approaches [ 12 ] significantly reduced annotation requirements while maintaining competitive performance.

Federated learning has emerged as a groundbreaking paradigm for collaborative model development while addressing data privacy concerns in healthcare. FedAvg (Federated Averaging) and FedBN (Federated Batch Normalization) [ 11 ] enable multiple medical institutions to jointly train segmentation models without sharing raw patient data. These approaches work by distributing model training across institutions and aggregating only the learned parameters, not the sensitive imaging data. This is particularly valuable for brain tumor segmentation, where datasets are often small and fragmented across hospitals. Recent implementations have demonstrated that FL can achieve comparable performance to centralized training while complying with strict medical data regulations like Health Insurance Portability and Accountability Act (HIPAA), and General Data Protection Regulation (GDPR). Advanced variants now incorporate differential privacy and secure multi-party computation to further enhance data protection during the federated training process.

The increasing complexity of deep learning models has necessitated the development of explainability techniques to facilitate clinical adoption. Grad-CAM (Gradient-weighted Class Activation Mapping) and SHAP (SHapley Additive exPlanations) [ 47 , 48 ] provide intuitive visualizations that highlight which image regions most influenced the model’s segmentation decisions. These methods help clinicians understand why a model classified certain areas as tumorous, enabling them to verify the algorithm’s reasoning against their medical expertise. Recent advances in XAI for medical imaging now combine attention maps with uncertainty quantification, providing not only localization of important features but also confidence estimates in the predictions. This dual approach has been shown to improve radiologists’ trust and diagnostic efficiency when working with AI-assisted segmentation systems.

The translation of segmentation algorithms into clinical practice requires models that can operate efficiently on medical hardware. Lightweight architectures like MobileNet [ 49 ] and EfficientNet [ 50 ] have been specifically adapted for this purpose through techniques such as depthwise separable convolutions and neural architecture search. These optimizations enable near real-time tumor segmentation on standard hospital workstations and even mobile devices, with inference times often under one second per MRI slice. Recent work has focused on developing hybrid models that maintain this efficiency while incorporating 3D contextual information crucial for accurate tumor volume estimation. Such models are now being integrated into surgical navigation systems and intraoperative MRI suites, providing surgeons with continuously updated tumor delineations during procedures.

Modern brain tumor characterization benefits immensely from combining information across multiple imaging modalities. Early fusion networks process concatenated MRI, PET, and DTI inputs through shared feature extractors, while late fusion approaches combine predictions from modality-specific networks [ 51 , 52 ]. The latest architectures employ attention-based fusion mechanisms that dynamically weight the contribution of each modality based on contextual relevance. Advanced implementations now incorporate cross-modal contrastive learning during pretraining to better align feature spaces across modalities. This multimodal approach has proven particularly valuable for distinguishing tumor recurrence from radiation necrosis and for precisely delineating infiltrative tumor margins that appear ambiguous in single-modality scans.

Table 2 presents a comprehensive comparison of techniques used in brain tumor segmentation across various categories. Traditional machine learning methods like SVM and Random Forests are interpretable but limited by manual feature extraction. CNN architectures such as U-Net and 3D U-Net improve spatial understanding, though they require high computational resources. Advanced CNNs (e.g., Attention U-Net) introduce focus mechanisms but add complexity. Transformer-based models like Swin UNETR capture global context yet demand large datasets. Generative models enhance realism but suffer from training instability. Weak supervision reduces labeling costs at the expense of accuracy. Federated learning ensures privacy in multi-center settings but involves communication challenges. Explainable AI tools foster clinical trust, though their outputs are sometimes unreliable. Real-time models and multimodal fusion enable mobile deployment and cross-modality synergy, respectively, but face trade-offs in accuracy and data alignment.

Traditional machine learning methods, such as SVM [ 64 ], Random Forests [ 65 ], and texture-based feature extraction [ 66 ], laid the foundation for automated brain tumor segmentation. These approaches were interpretable and computationally efficient, but their reliance on handcrafted features limited their ability to generalize across diverse datasets [ 5 ]. The introduction of deep learning, particularly U-Net [ 7 ]and its 3D variants [ 21 ] marked a turning point by enabling end-to-end learning of hierarchical features directly from imaging data. This shift significantly improved segmentation accuracy, with Dice scores rising from ~70% (traditional ML) to over 85% (DL) on BraTS benchmarks [ 9 ]. However, early CNNs faced challenges in handling multi-modal MRI inconsistencies and required large annotated datasets for training, a limitation partially addressed by data augmentation and transfer learning.

The introduction of Vision Transformers (ViTs) and their medical adaptations (e.g., Swin UNETR [ 63 ], TransBTS [ 39 ]) addressed CNNs’ inability to model long-range spatial dependencies [ 10 ]. These models excel in capturing global context, making them particularly effective for segmenting diffuse tumor margins in glioblastoma [ 67 ]. Hybrid architectures, such as CNN-Transformer ensembles, further bridged the gap between local feature extraction and global reasoning, achieving Dice scores > 90% on BraTS 2021 [ 8 ]. Despite these advances, Transformers demand substantial computational resources and training data, limiting their accessibility for smaller institutions.

Generative approaches, including GANs [ 43 ] and diffusion models [ 45 ], have enhanced segmentation by generating synthetic training data or refining predictions through iterative denoising. Weakly supervised methods [ 12 ] reduced reliance on pixel-level annotations by leveraging scribbles or bounding boxes, making AI more feasible for rare tumor subtypes. However, GANs suffer from training instability, while diffusion models are computationally expensive for real-time applications.

Manual annotation of 3D brain tumor volumes is time-consuming and requires domain expertise, limiting the scalability of supervised learning. Generative models such as GANs and VAEs have been successfully employed to synthesize realistic tumor images and augment training data, improving model generalization and robustness. For instance, Mok & Chung (2018) [ 68 ] introduced a coarse-to-fine GAN-based augmentation strategy (CB-GAN) that improved segmentation performance by 3.5% Dice score over traditional augmentation on the BraTS15 dataset. Their two-stage GAN was effective in generating anatomically plausible tumors with refined boundaries, reducing dependence on manual labeling.

In parallel, weakly supervised learning offers a practical alternative by utilizing partial annotations such as image-level labels or scribbles. Mlynarski et al. (2019) [ 69 ] demonstrated that combining a small number of fully annotated MRI scans (e.g., 5 or 15) with a larger set of weakly labeled data could achieve up to 78.3% Dice score, closely matching models trained on extensive full supervision. This underscores the potential of mixed supervision in minimizing annotation costs while maintaining segmentation accuracy.

Together, these strategies offer scalable and efficient training solutions, particularly valuable for clinical environments with limited annotation resources.

Federated learning [ 11 ] has emerged as a privacy-preserving solution for multi-institutional collaborations, crucial for rare tumor types. This innovation is especially vital for brain tumor segmentation, where access to large, diverse, and annotated datasets is limited, particularly for rare subtypes such as pediatric gliomas or atypical meningiomas. By harnessing the statistical power of geographically distributed data, FL mitigates bias associated with single-institution models and fosters the development of robust, generalizable algorithms applicable across a wide range of patient populations and imaging protocols [ 70 ].

Deep learning models, particularly those trained on standard datasets like BraTS, have demonstrated strong performance in segmenting high-grade gliomas (HGGs). However, their effectiveness varies significantly when applied to different brain tumor subtypes, such as pediatric gliomas and atypical meningiomas, which often exhibit distinct morphological and radiographic characteristics.

Pediatric gliomas often present with distinct imaging characteristics, such as more diffuse growth patterns and lower contrast on MRI compared to adult high-grade gliomas (HGGs). These differences contribute to challenges in segmentation, including decreased Dice scores and higher boundary uncertainty. Liu et al. (2023) [ 71 ] investigated this issue and reported that models trained on adult glioma datasets exhibit a Dice score reduction of over 10% when applied to pediatric brain tumor cases, underscoring a significant domain shift. Similarly, atypical meningiomas, which are less frequent and show considerable heterogeneity, present additional challenges for model generalization due to limited annotated data and intra-class variability. These findings emphasize the need for subtype-specific training approaches, domain adaptation methods, and more diverse datasets to enhance the accuracy and generalizability of brain tumor segmentation models across different tumor types.

In parallel, the push for explainable AI (XAI) has become indispensable for clinical adoption. Tools such as Gradient-weighted Class Activation Mapping (Grad-CAM), SHAP (SHapley Additive exPlanations), and Integrated Gradients allow clinicians to peer inside the “black box” of deep neural networks, offering intuitive visualizations of which regions in the MRI contribute most to the model’s prediction [ 47 , 48 ]. These methods have increased clinician trust and enabled model auditing, especially in high-stakes applications like preoperative planning and radiotherapy targeting. However, FL introduces communication overhead, and XAI methods sometimes produce unreliable explanations for complex models.

Lightweight deep learning models such as MobileNet [ 49 ], EfficientNet [ 50 ], and their derivatives are playing a pivotal role in enabling real-time intraoperative segmentation of brain tumors. These architectures, optimized for computational efficiency and low-latency inference, allow deployment on edge devices and integration into surgical navigation systems, thereby assisting neurosurgeons in achieving maximal tumor resection while preserving healthy tissue [ 49 , 50 ]. For example, MobileNetV3 and EfficientNet-Lite have demonstrated promising accuracy-speed trade-offs when applied to real-time segmentation tasks on resource-constrained hardware like portable workstations or intraoperative imaging consoles [ 72 ].

Concurrently, the integration of multimodal imaging, such as combining structural MRI with functional modalities like Positron Emission Tomography (PET), Diffusion Tensor Imaging (DTI), and MR spectroscopy, has emerged as a powerful strategy for comprehensive tumor characterization [ 73 ]. These multimodal approaches leverage complementary features: while MRI offers high-resolution anatomical details, PET reveals metabolic activity, and DTI provides insight into white matter tract integrity.

Deep learning models equipped with multimodal fusion layers, such as attention-guided or cross-modal transformers, are showing promise in exploiting these heterogeneous data streams to improve segmentation precision and tumor sub-region delineation [ 74 ]. However, a major challenge lies in the harmonization of imaging protocols across different scanners, vendors, and institutions. Variability in acquisition parameters, field strengths, and contrast agent usage can significantly degrade model generalizability. To address this, techniques such as domain adaptation, intensity normalization, and synthetic modality generation using GANs are actively being explored [ 75 ]. Moreover, federated learning paradigms are gaining traction as a means to train models collaboratively across institutions without sharing raw patient data, thereby enhancing data diversity while preserving privacy [ 70 ].

Despite significant advancements in brain tumor segmentation, several critical challenges persist that hinder widespread clinical adoption. A primary obstacle is the inherent data heterogeneity stemming from variations in MRI scanner protocols and acquisition parameters across institutions, which compromises model generalizability [ 3 ]. Additionally, class imbalance remains a persistent issue, as certain tumor subregions like necrotic cores are often underrepresented in training datasets, leading to biased predictions. Perhaps most crucially, the majority of segmentation models have not undergone rigorous prospective clinical validation, creating a translational gap between algorithmic performance and real-world clinical utility. These limitations underscore the need for more robust and standardized approaches to ensure reliable deployment in healthcare settings.

Looking ahead, the field must prioritize several key directions to address these challenges:

These strategic focuses will be instrumental in bridging the current gaps between technical innovation and clinical application in neuro-oncology.

The evolution of brain tumor segmentation techniques has brought forth significant strengths that have transformed neuro-oncology research and clinical practice. Deep learning approaches, particularly U-Net and its variants, have demonstrated remarkable capabilities in automatically extracting hierarchical features from multi-modal MRI data, achieving unprecedented segmentation accuracy with Dice scores exceeding 90% in some cases [ 9 ]. The advent of transformer-based architectures has further enhanced performance by effectively capturing long-range spatial dependencies in volumetric scans, while generative models have shown promise in addressing data scarcity through synthetic data generation [ 10 , 45 ]. These technical advancements have been complemented by the development of privacy-preserving federated learning frameworks and explainable AI techniques, which facilitate multi-institutional collaboration and improve clinical interpretability [ 20 , 48 ]. However, these approaches are not without limitations that require careful consideration.

The path forward for brain tumor segmentation lies in developing more efficient, interpretable, and clinically validated models that can seamlessly integrate into existing healthcare workflows. Future research should focus on creating adaptive systems that can learn from limited annotations, generalize across diverse imaging protocols, and provide clinically actionable insights with measurable confidence intervals. By addressing these challenges through collaborative efforts between computer scientists and clinicians, the next generation of segmentation tools could significantly improve diagnostic accuracy, treatment planning, and patient outcomes in neuro-oncology.