Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2405.18383

Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge

Dominic LaBella·Valeriia Abramova·Mehdi Astaraki·Andre Ferreira·Zhifan Jiang·Mason C. Cleveland·Ramandeep Kang·Uma M. Lal-Trehan Estrada·Cansu Yalcin·Rachika E. Hamadache·Clara Lisazo·Adrià Casamitjana·Joaquim Salvi·Arnau Oliver·Xavier Lladó·Iuliana Toma-Dasu·Tiago Jesus·Behrus Puladi·Jens Kleesiek·Victor Alves·Jan Egger·Daniel Capellán-Martín·Abhijeet Parida·Austin Tapp·Xinyang Liu·Maria J. Ledesma-Carbayo·Jay B. Patel·Thomas N. McNeal·Maya Viera·Owen McCall·Albert E. Kim·Elizabeth R. Gerstner·Christopher P. Bridge·Katherine Schumacher·Michael Mix·Kevin Leu·Shan McBurney-Lin·Pierre Nedelec·Javier Villanueva-Meyer·David R. Raleigh·Jonathan Shapey·Tom Vercauteren·Kazumi Chia·Marina Ivory·Theodore Barfoot·Omar Al-Salihi·Justin Leu·Lia M. Halasz·Yuri S. Velichko·Chunhao Wang·John P. Kirkpatrick·Scott R. Floyd·Zachary J. Reitman·Trey C. Mullikin·Eugene J. Vaios·Christina Huang·Ulas Bagci·Sean Sachdev·Jona A. Hattangadi-Gluth·Tyler M. Seibert·Nikdokht Farid·Connor Puett·Matthew W. Pease·Kevin Shiue·Syed Muhammad Anwar·Shahriar Faghani·Peter Taylor·Pranav Warman·Jake Albrecht·András Jakab·Mana Moassefi·Verena Chung·Rong Chai·Alejandro Aristizabal·Alexandros Karargyris·Hasan Kassem·Sarthak Pati·Micah Sheller·Nazanin Maleki·Rachit Saluja·Florian Kofler·Christopher G. Schwarz·Philipp Lohmann·Phillipp Vollmuth·Louis Gagnon·Maruf Adewole·Hongwei Bran Li·Anahita Fathi Kazerooni·Nourel Hoda Tahon·Udunna Anazodo·Ahmed W. Moawad·Bjoern Menze·Marius George Linguraru·Mariam Aboian·Benedikt Wiestler·Ujjwal Baid·Gian-Marco Conte·Andreas M. Rauschecker·Ayman Nada·Aly H. Abayazeed
View original source ↗Read source document ↗Export metadata ↓

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.

Explore related subjects

cs.CVcs.AIcs.HCcs.LG

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Dominic LaBella, Valeriia Abramova, Mehdi Astaraki, Andre Ferreira, Zhifan Jiang, Mason C. Cleveland, Ramandeep Kang, Uma M. Lal-Trehan Estrada, Cansu Yalcin, Rachika E. Hamadache, Clara Lisazo, Adrià Casamitjana, Joaquim Salvi, Arnau Oliver, Xavier Lladó, Iuliana Toma-Dasu, Tiago Jesus, Behrus Puladi, Jens Kleesiek, Victor Alves, Jan Egger, Daniel Capellán-Martín, Abhijeet Parida, Austin Tapp, Xinyang Liu, Maria J. Ledesma-Carbayo, Jay B. Patel, Thomas N. McNeal, Maya Viera, Owen McCall, Albert E. Kim, Elizabeth R. Gerstner, Christopher P. Bridge, Katherine Schumacher, Michael Mix, Kevin Leu, Shan McBurney-Lin, Pierre Nedelec, Javier Villanueva-Meyer, David R. Raleigh, Jonathan Shapey, Tom Vercauteren, Kazumi Chia, Marina Ivory, Theodore Barfoot, Omar Al-Salihi, Justin Leu, Lia M. Halasz, Yuri S. Velichko, Chunhao Wang, John P. Kirkpatrick, Scott R. Floyd, Zachary J. Reitman, Trey C. Mullikin, Eugene J. Vaios, Christina Huang, Ulas Bagci, Sean Sachdev, Jona A. Hattangadi-Gluth, Tyler M. Seibert, Nikdokht Farid, Connor Puett, Matthew W. Pease, Kevin Shiue, Syed Muhammad Anwar, Shahriar Faghani, Peter Taylor, Pranav Warman, Jake Albrecht, András Jakab, Mana Moassefi, Verena Chung, Rong Chai, Alejandro Aristizabal, Alexandros Karargyris, Hasan Kassem, Sarthak Pati, Micah Sheller, Nazanin Maleki, Rachit Saluja, Florian Kofler, Christopher G. Schwarz, Philipp Lohmann, Phillipp Vollmuth, Louis Gagnon, Maruf Adewole, Hongwei Bran Li, Anahita Fathi Kazerooni, Nourel Hoda Tahon, Udunna Anazodo, Ahmed W. Moawad, Bjoern Menze, Marius George Linguraru, Mariam Aboian, Benedikt Wiestler, Ujjwal Baid, Gian-Marco Conte, Andreas M. Rauschecker, Ayman Nada, Aly H. Abayazeed. 2025-07-21. Analysis of the 2024 BraTS Meningioma Radiotherapy Planning Automated Segmentation Challenge. https://arxiv.org/abs/2405.18383

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Privacy Leakage on DNNs: A Survey of Model Inversion Attacks and Defenses

Deep Neural Networks (DNNs) have revolutionized various domains with their exceptional performance across numerous applications. However, Model Inversion (MI) attacks, which disclose private information about the training dataset by abusing access to the trained models, have emerged as a formidable privacy threat. Given a trained network, these attacks enable adversaries to reconstruct high-fidelity data that closely aligns with the private training samples, posing significant privacy concerns. Despite the rapid advances in the field, we lack a comprehensive and systematic overview of existing MI attacks and defenses. To fill this gap, this paper thoroughly investigates this realm and presents a holistic survey. Firstly, our work briefly reviews early MI studies on traditional machine learning scenarios. We then elaborately analyze and compare numerous recent attacks and defenses on Deep Neural Networks (DNNs) across multiple modalities and learning tasks. By meticulously analyzing their distinctive features, we summarize and classify these methods into different categories and provide a novel taxonomy. Finally, this paper discusses promising research directions and presents potential solutions to open issues. To facilitate further study on MI attacks and defenses, we have implemented an open-source model inversion toolbox on GitHub (https://github.com/ffhibnese/Model-Inversion-Attack-ToolBox).

cs.CV↗
arXiv2026

ALINA: Advanced Line Identification and Notation Algorithm

Labels are the cornerstone of supervised machine learning algorithms. Most visual recognition methods are fully supervised, using bounding boxes or pixel-wise segmentations for object localization. Traditional labeling methods, such as crowd-sourcing, are prohibitive due to cost, data privacy, amount of time, and potential errors on large datasets. To address these issues, we propose a novel annotation framework, Advanced Line Identification and Notation Algorithm (ALINA), which can be used for labeling taxiway datasets that consist of different camera perspectives and variable weather attributes (sunny and cloudy). Additionally, the CIRCular threshoLd pixEl Discovery And Traversal (CIRCLEDAT) algorithm has been proposed, which is an integral step in determining the pixels corresponding to taxiway line markings. Once the pixels are identified, ALINA generates corresponding pixel coordinate annotations on the frame. Using this approach, 60,249 frames from the taxiway dataset, AssistTaxi have been labeled. To evaluate the performance, a context-based edge map (CBEM) set was generated manually based on edge features and connectivity. The detection rate after testing the annotated labels with the CBEM set was recorded as 98.45%, attesting its dependability and effectiveness.

cs.CV↗
arXiv2026

DisasterInsight: A Building-Centric Benchmark for Evaluating Vision--Language Models in Disaster Response

Vision--language models (VLMs) show promise for disaster-response remote sensing, but existing benchmarks mainly emphasize scene-level or damage-centric assessment. To study this building-centric gap, we introduce \method{}, a diagnostic benchmark built on xBD, a pre/post-disaster satellite dataset with building-level damage labels. \method{} enriches building instances with OpenStreetMap-derived functional labels and contains 134{,}108 task-specific instruction records across 15 task types, spanning instance-level assessment, scene-level counting, multi-instance reasoning, and structured report generation. The benchmark supports RGB pre/post-disaster imagery, single- and multi-view instance formulations, and scene-level RGB/SAR diagnostic inputs. Experiments with general-domain and remote-sensing VLMs show that models perform better on visible damage cues than on building-function understanding, multi-instance reasoning, counting, and grounded reporting. Instruction tuning improves performance on several tasks but does not close this building-centric gap.

cs.CV↗