# Advanced Tumor Segmentation in Medical Imaging: An Ensemble Approach for BraTS 2023 Adult Glioma and Pediatric Tumor Tasks

arXiv: https://arxiv.org/abs/2403.09262 · HTML: https://mlsanity.com/paper/2403.09262/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 22 references that are in the map.
- First posted on arXiv 14 Mar 2024.

## Builds on (most cited first, 12 of 12)

- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Identity Mappings in Deep Residual Networks](https://mlsanity.com/paper/1603.05027/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation](https://mlsanity.com/paper/2303.09975/) (2023)
- [nnU-Net for Brain Tumor Segmentation](https://mlsanity.com/paper/2011.00848/) (2020)
- [MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation](https://mlsanity.com/paper/2110.01406/) (2021)
- [The Brain Tumor Segmentation (BraTS) Challenge 2023: Focus on Pediatrics (CBTN-CONNECT-DIPGR-ASNR-MICCAI BraTS-PEDs)](https://mlsanity.com/paper/2305.17033/) (2023)
- [Multimodal CNN Networks for Brain Tumor Segmentation in MRI: A BraTS 2022 Challenge Solution](https://mlsanity.com/paper/2212.09310/) (2022)

## Built on by (most cited first, 12 of 12)

- [BraTS-PEDs: Results of the Multi-Consortium International Pediatric Brain Tumor Segmentation Challenge 2023](https://mlsanity.com/paper/2407.08855/) (2024)
- [BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis](https://mlsanity.com/paper/2506.13807/) (2025)
- [Optimizing Brain Tumor Segmentation with MedNeXt: BraTS 2024 SSA and Pediatrics](https://mlsanity.com/paper/2411.15872/) (2024)
- [On Enhancing Brain Tumor Segmentation Across Diverse Populations with Convolutional Neural Networks](https://mlsanity.com/paper/2405.02852/) (2024)
- [Multimodal Fusion at Three Tiers: Physics-Driven Data Generation and Vision-Language Guidance for Brain Tumor Segmentation](https://mlsanity.com/paper/2507.09966/) (2025)
- [Improving Pre-trained Adult Glioma Segmentation Models Using only Post-processing Techniques](https://mlsanity.com/paper/2512.14937/) (2025)
- [SAFE Quantum Machine Learning with Variational Quantum Classifiers](https://mlsanity.com/paper/2605.16067/) (2026)
- [3D Graph Attention Networks for High Fidelity Pediatric Glioma Segmentation](https://mlsanity.com/paper/2412.06743/) (2024)
- [On-the-Fly Data Augmentation for Brain Tumor Segmentation](https://mlsanity.com/paper/2509.24973/) (2025)
- [Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble](https://mlsanity.com/paper/2512.14648/) (2025)
- [Fairboard: a quantitative framework for equity assessment of healthcare models](https://mlsanity.com/paper/2604.09656/) (2026)
- [Parameter-Efficient Multimodal Brain Tumor Segmentation With Stage-Wise Shared Adapters](https://mlsanity.com/paper/2610.18571/) (2026)

## Abstract (arXiv)

Automated segmentation proves to be a valuable tool in precisely detecting tumors within medical images. The accurate identification and segmentation of tumor types hold paramount importance in diagnosing, monitoring, and treating highly fatal brain tumors. The BraTS challenge serves as a platform for researchers to tackle this issue by participating in open challenges focused on tumor segmentation. This study outlines our methodology for segmenting tumors in the context of two distinct tasks from the BraTS 2023 challenge: Adult Glioma and Pediatric Tumors. Our approach leverages two encoder-decoder-based CNN models, namely SegResNet and MedNeXt, for segmenting three distinct subregions of tumors. We further introduce a set of robust postprocessing to improve the segmentation, especially for the newly introduced BraTS 2023 metrics. The specifics of our approach and comprehensive performance analyses are expounded upon in this work. Our proposed approach achieves third place in the BraTS 2023 Adult Glioma Segmentation Challenges with an average of 0.8313 and 36.38 Dice and HD95 scores on the test set, respectively.