The paper defines four matching strategies for panoptic quality via degree-bounded bipartite assignment and introduces vertex-based TP/FN/FP counting that extends to part-aware evaluation.
Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge
7 Pith papers cite this work. Polarity classification is still indexing.
7
Pith papers citing it
abstract
The 2024 Brain Tumor Segmentation Meningioma Radiotherapy (BraTS-MEN-RT) challenge aimed to advance automated segmentation algorithms using the largest known multi-institutional dataset of 750 radiotherapy planning brain MRIs with expert-annotated target labels for patients with intact or postoperative meningioma that underwent either conventional external beam radiotherapy or stereotactic radiosurgery. Each case included a defaced 3D post-contrast T1-weighted radiotherapy planning MRI in its native acquisition space, accompanied by a single-label "target volume" representing the gross tumor volume (GTV) and any at-risk post-operative site. Target volume annotations adhered to established radiotherapy planning protocols, ensuring consistency across cases and institutions, and were approved by expert neuroradiologists and radiation oncologists. Six participating teams developed, containerized, and evaluated automated segmentation models using this comprehensive dataset. Team rankings were assessed using a modified lesion-wise Dice Similarity Coefficient (DSC) and 95% Hausdorff Distance (95HD). The best reported average lesion-wise DSC and 95HD was 0.815 and 26.92 mm, respectively. BraTS-MEN-RT is expected to significantly advance automated radiotherapy planning by enabling precise tumor segmentation and facilitating tailored treatment, ultimately improving patient outcomes. We describe the design and results from the BraTS-MEN-RT challenge.
representative citing papers
A radiomic-guided, lesion-aware ensemble with per-cluster post-processing yields strong segmentation across pediatric, meningioma, and metastasis MRI cohorts, though 'top-ranked' parity is asserted rather than shown.
BraTS orchestrator is a new open-source package that provides uniform, tutorial-based access to winning BraTS segmentation and synthesis algorithms for brain tumor MRI.
A survey categorizing self-supervised pretraining methods for image segmentation into predictive, generative, and contrastive approaches, with a list of benchmark datasets.
An ensemble of nnU-Net, MedNeXt and SwinUNETR with radiomic-subtype-tuned post-processing achieves whole-tumor lesion-wise Dice of 0.926, 0.801 and 0.688 on the BraTS 2024 PED, MEN-RT and MET test sets.
A challenge report showing a MedNeXt small model winning Task 1 (DSC 0.8254) and an nnUNet ensemble placing 8th in Task 2 (0.7005) for MRI-guided radiotherapy tumor segmentation.
BrainLesion Suite is a modular open-source toolkit for brain lesion image analysis that combines previously published preprocessing, segmentation, and evaluation components, though the paper reports no new quantitative results.
citing papers explorer
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Redefining Instance Matching: A Unified Framework for Part-Aware Matching in Panoptic Segmentation Evaluation
The paper defines four matching strategies for panoptic quality via degree-bounded bipartite assignment and introduces vertex-based TP/FN/FP counting that extends to part-aware evaluation.
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Adaptable Segmentation Pipeline for Diverse Brain Tumors with Radiomic-Guided Subtyping and Lesion-Wise Model Ensemble
A radiomic-guided, lesion-aware ensemble with per-cluster post-processing yields strong segmentation across pediatric, meningioma, and metastasis MRI cohorts, though 'top-ranked' parity is asserted rather than shown.
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BraTS orchestrator : Democratizing and Disseminating state-of-the-art brain tumor image analysis
BraTS orchestrator is a new open-source package that provides uniform, tutorial-based access to winning BraTS segmentation and synthesis algorithms for brain tumor MRI.
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Self-Supervised Learning for Image Segmentation: A Comprehensive Survey
A survey categorizing self-supervised pretraining methods for image segmentation into predictive, generative, and contrastive approaches, with a list of benchmark datasets.
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Magnetic Resonance Imaging Feature-Based Subtyping and Model Ensemble for Enhanced Brain Tumor Segmentation
An ensemble of nnU-Net, MedNeXt and SwinUNETR with radiomic-subtype-tuned post-processing achieves whole-tumor lesion-wise Dice of 0.926, 0.801 and 0.688 on the BraTS 2024 PED, MEN-RT and MET test sets.
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Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-guided Radiotherapy
A challenge report showing a MedNeXt small model winning Task 1 (DSC 0.8254) and an nnUNet ensemble placing 8th in Task 2 (0.7005) for MRI-guided radiotherapy tumor segmentation.
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BrainLesion Suite: A Flexible and User-Friendly Framework for Modular Brain Lesion Image Analysis
BrainLesion Suite is a modular open-source toolkit for brain lesion image analysis that combines previously published preprocessing, segmentation, and evaluation components, though the paper reports no new quantitative results.