# The 2024 Brain Tumor Segmentation (BraTS) Challenge: Glioma Segmentation on Post-treatment MRI arXiv: https://arxiv.org/abs/2405.18368 ยท HTML: https://mlsanity.com/paper/2405.18368/ ## Key facts - Cited by 69 papers in the map (data through 14 Oct 2026). - Builds on 10 of its 39 references that are in the map. - First posted on arXiv 28 May 2024. ## Builds on (most cited first, 10 of 10) - [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021) - [Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge](https://mlsanity.com/paper/1811.02629/) (2018) - [Automated brain extraction of multi-sequence MRI using artificial neural networks](https://mlsanity.com/paper/1901.11341/) (2019) - [The ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge 2023: Intracranial Meningioma](https://mlsanity.com/paper/2305.07642/) (2023) - [The Brain Tumor Segmentation (BraTS) Challenge 2023: Glioma Segmentation in Sub-Saharan Africa Patient Population (BraTS-Africa)](https://mlsanity.com/paper/2305.19369/) (2023) - [The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)](https://mlsanity.com/paper/2305.17033/) (2023) - [The Brain Tumor Segmentation (BraTS-METS) Challenge 2023: Brain Metastasis Segmentation on Pre-treatment MRI](https://mlsanity.com/paper/2306.00838/) (2023) - [Analysis of the BraTS 2023 Intracranial Meningioma Segmentation Challenge](https://mlsanity.com/paper/2405.09787/) (2024) - [The Rio Hortega University Hospital Glioblastoma dataset: a comprehensive collection of preoperative, early postoperative and recurrence MRI scans (RHUH-GBM)](https://mlsanity.com/paper/2305.00005/) (2023) - [Automated head and neck tumor segmentation from 3D PET/CT](https://mlsanity.com/paper/2209.10809/) (2022) ## Built on by (most cited first, 30 of 69) - [Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning](https://mlsanity.com/paper/2506.07044/) (2025) - [MedTrinity-25M: A Large-scale Multimodal Dataset with Multigranular Annotations for Medicine](https://mlsanity.com/paper/2408.02900/) (2024) - [BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification](https://mlsanity.com/paper/2506.14318/) (2025) - [Multimodal Data Integration for Precision Oncology: Challenges and Future Directions](https://mlsanity.com/paper/2406.19611/) (2024) - [Visual Instruction Pretraining for Domain-Specific Foundation Models](https://mlsanity.com/paper/2509.17562/) (2025) - [A large-scale heterogeneous 3D magnetic resonance brain imaging dataset for self-supervised learning](https://mlsanity.com/paper/2506.14432/) (2025) - [Adult Glioma Segmentation in Sub-Saharan Africa using Transfer Learning on Stratified Finetuning Data](https://mlsanity.com/paper/2412.04111/) (2024) - [MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework](https://mlsanity.com/paper/2407.21343/) (2024) - [An Ensemble Approach for Brain Tumor Segmentation and Synthesis](https://mlsanity.com/paper/2411.17617/) (2024) - [XLSTM-HVED: Cross-Modal Brain Tumor Segmentation and MRI Reconstruction Method Using Vision XLSTM and Heteromodal Variational Encoder-Decoder](https://mlsanity.com/paper/2412.07804/) (2024) - [VAEVQ: Enhancing Discrete Visual Tokenization through Variational Modeling](https://mlsanity.com/paper/2511.06863/) (2025) - [A Large Scale Benchmark for Test Time Adaptation Methods in Medical Image Segmentation](https://mlsanity.com/paper/2512.02497/) (2025) - [Improved Multi-Task Brain Tumour Segmentation with Synthetic Data Augmentation](https://mlsanity.com/paper/2411.04632/) (2024) - [No Modality Left Behind: Adapting to Missing Modalities via Knowledge Distillation for Brain Tumor Segmentation](https://mlsanity.com/paper/2509.15017/) (2025) - [Agentic Large Language Models for Training-Free Neuro-Radiological Image Analysis](https://mlsanity.com/paper/2604.16729/) (2026) - [BrainDINO: A Brain MRI Foundation Model for Generalizable Clinical Representation Learning](https://mlsanity.com/paper/2604.27277/) (2026) - [Fair Foundation Models for Medical Image Analysis: Challenges and Perspectives](https://mlsanity.com/paper/2502.16841/) (2025) - [The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning](https://mlsanity.com/paper/2512.06206/) (2025) - [Effective Segmentation of Post-Treatment Gliomas Using Simple Approaches: Artificial Sequence Generation and Ensemble Models](https://mlsanity.com/paper/2409.08143/) (2024) - [Predicting brain tumour enhancement from non-contrast MR imaging with artificial intelligence: a multi-cohort retrospective diagnostic accuracy study](https://mlsanity.com/paper/2508.16650/) (2025) - [PREDICT-GBM: A multi-center platform to advance personalized glioblastoma radiotherapy planning](https://mlsanity.com/paper/2509.13360/) (2025) - [The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection](https://mlsanity.com/paper/2509.15947/) (2025) - [Multimodal LLM With Hierarchical Mixture-of-Experts for VQA on 3D Brain MRI](https://mlsanity.com/paper/2509.25889/) (2025) - [Contrast-X: A Multi-Modal Contrast Image Synthesis Benchmark and Universal Modality Flow Matching](https://mlsanity.com/paper/2601.15884/) (2026) - [UKAN-EP: Enhancing U-KAN with Efficient Attention and Pyramid Aggregation for 3D Multi-Modal MRI Brain Tumor Segmentation](https://mlsanity.com/paper/2408.00273/) (2024) - [Multi-encoder nnU-Net outperforms transformer models with self-supervised pretraining](https://mlsanity.com/paper/2504.03474/) (2025) - [BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis](https://mlsanity.com/paper/2507.09036/) (2025) - [A Structured Review and Quantitative Profiling of Public Brain MRI Datasets for Foundation Model Development](https://mlsanity.com/paper/2510.20196/) (2025) - [Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques](https://mlsanity.com/paper/2512.14937/) (2025) - [BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features](https://mlsanity.com/paper/2602.16006/) (2026) ## Abstract (arXiv) Gliomas are the most common malignant primary brain tumors in adults and one of the deadliest types of cancer. There are many challenges in treatment and monitoring due to the genetic diversity and high intrinsic heterogeneity in appearance, shape, histology, and treatment response. Treatments include surgery, radiation, and systemic therapies, with magnetic resonance imaging (MRI) playing a key role in treatment planning and post-treatment longitudinal assessment. The 2024 Brain Tumor Segmentation (BraTS) challenge on post-treatment glioma MRI will provide a community standard and benchmark for state-of-the-art automated segmentation models based on the largest expert-annotated post-treatment glioma MRI dataset. Challenge competitors will develop automated segmentation models to predict four distinct tumor sub-regions consisting of enhancing tissue (ET), surrounding non-enhancing T2/fluid-attenuated inversion recovery (FLAIR) hyperintensity (SNFH), non-enhancing tumor core (NETC), and resection cavity (RC). Models will be evaluated on separate validation and test datasets using standardized performance metrics utilized across the BraTS 2024 cluster of challenges, including lesion-wise Dice Similarity Coefficient and Hausdorff Distance. Models developed during this challenge will advance the field of automated MRI segmentation and contribute to their integration into clinical practice, ultimately enhancing patient care.