Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries : MICCAI 2025 Challenges: BraTS-Lighthouse 2025 and AIMS-TBI 2025, held in conjunction with MICCAI 2025, Daejeon, South Korea, September 23, 2025, Proceedings. Part I / Spyridon Bakas [and fourteen others] editors

Format:

Book

Conference/Event

Contributor:

Bakas, Spyridon, editor.

Conference Name:

BraTS-Lighthouse (Challenge) (2025 : Taejŏn-si, Korea)

AIMS-TBI (Challenge) (2025 : Taejŏn-si, Korea)

International Conference on Medical Image Computing and Computer-Assisted Intervention (28th : 2025 : Taejŏn-si, Korea)

Series:

Lecture notes in computer science ; 16376.

Lecture notes in computer science, 1611-3349 ; 16376

Language:

English

Subjects (All):

Brain--Tumors--Imaging--Congresses.

Brain.

Brain--Wounds and injuries--Imaging--Congresses.

Brain--Tumors--Imaging.

Brain--Wounds and injuries--Imaging.

Genre:

Conference papers and proceedings

Physical Description:

1 online resource (xxxi, 552 pages) : illustrations

Other Title:

BraTS-Lighthouse 2025

AIMS-TBI 2025

MICCAI

Place of Publication:

Cham, Switzerland : Springer, [2026]

Summary:

"The MICCAI Challenges proceedings book set LNCS 16376 + 16377 includes contributions from the BraTS 2025 Lighthouse Challenge focusing on brain tumor image analysis, including longitudinal assessment of brain tumor response, generalizability of tumor segmentation methods across different entities, and inclusion of tumor entities for which there is currently limited annotated data. It also contains contributions from the AIMS-TBI 2025 Challenge that deals with the detection and segmentation of lesions in T1-weighted MRI data from moderate-severe traumatic brain injury. The 79 papers included in the proceedings were carefully reviewed and selected from a total of 90 submissions. The papers were organized in topical sectios as follows: Part I: Invited Paper.- Challenge 1 - BraTS-GLI Challenge; Challenge 2 - BraTS-MEN; Challenge 3 - BraTS-MEN-RT; Challenge 4 - BraTS-METS; Challenge 5 - BraTS-Africa; Challenge 6 - BraTS-PED; Challenge 7 - BraTS-GOAT; Part II: Challenge 8 - BraTS-Synth; Challenge 9 - BraTS-Inpainting; Challenge 10 - BraTS-Path; Challenge 11 - BraTS-PRO; Challenge 12 - AIMS-TBI"-- Springer Nature Link

Contents:

My model is better than yours! Statistically-aware ranking for fair benchmarking of AI models / Spyridon Bakas, Siddhesh Thakur, Ujjwal Baid, Akis Linardos, Sarthak Pati, Jimit Doshi, and Russell T. Shinohara

EGASegNet : An extreme group-aware segmentation network for glioma segmentation / Liwei Jin and Yanjun Peng

Pre- and post-treatment glioma segmentation with the medical imaging segmentation toolkit / Adrian Celaya, Tucker Netherton, Dawid Schellingerhout, Caroline Chung, Beatrice Riviere, and David Fuentes

µPUA-Net : PowerMLP model size shrinking method with accuracy maintaining / Yu-Shan Chou, You-Jin Liu, Kai-Lun Pien, Tong-Hou Cheong, Chieh-Chen Yu, Ying-Hui Cheng, Yu-Hsuan Chiang, E. Ray Hsieh, and Chien-Chang Chen

On-the-fly data augmentation for brain tumor segmentation / Ishika Jain, Siri Willems, Steven Latre, and Tom De Schepper

Segmentation of pre- and post-operative glioma tumors using Swin UNETR and BraTS‑25 challenge data / Mohammad Tufail Sheikh, Satyajit Maurya, and Anup Singh

Enhancing tumor subregion segmentation using domain adaptation, pseudo-labeling, and post-processing optimization / Ajesh Saviour Paravila

PTransBTS : A hybrid transformer integrating priors for brain tumor segmentation / Haitao Yu and Yanjun Peng

Efficient meningioma tumor segmentation using ensemble learning / Mohammad Mahdi Danesh Pajouh and Sara Saeedi

Brain tissue context for enhancing brain tumor segmentation : A contribution to BraTS 2025 / Mehdi Astaraki, Farangis Sajadi Moghadam, and Iuliana Toma-Dasu

DeSURVAE : A dual-encoder dual-decoder neural network for GTV semantic segmentation of meningioma brain tumor in radiotherapy planning / Nima Sadeghzadeh, Jason A. Correia, Samantha J. Holdsworth, Poul M. F. Nielsen, Michael Dragunow, Richard L. M. Faull, and Hamid Abbasi

Boundary-aware approach for meningioma segmentation in radiotherapy planning MRI / Valeriia Abramova, Agustin Cartaya Lathulerie, Uma M. Lal‑Trehan Estrada, Cansu Yalçın, Rachika E. Hamadache, Clara Lisazo, Micaela Rivas Díaz, Adrià Casamitjana, Arnau Oliver, and Xavier Lladó

Condition-based ensemble modelling of Swin UNETR and 3D U-Net for meningioma segmentation in radiotherapy planning / Sanskriti Srivastava, Kuldeep Raghuwanshi, and Anup Singh

Segmentation of pre- and post-treatment brain metastases using nnU-Nets / Maria Bancerek, Piotr Rudzki, and Jakub Nalepa

Automated segmentation for the brain tumor segmentation (BraTS) metastases 2025 challenge using multi-architectural deep learning / Wes Krikorian and Ananya Purwar

Taking advantage of MONAI and DiNTS frameworks to develop a state-of-the-art algorithm for automatic segmentation of brain metastases / Fabian Umeh, Nikolay Y. Yordanov, Nazanin Maleki, Raisa Amiruddin, Ahmed Moawad, Monika Pytlarz, Crystal Chukwurah, and Mariam Aboian

Ensembling CNN, transformer, and Mamba with stacking for brain tumor segmentation / Trung D. Q. Dang, Huy Hoang Nguyen, and Aleksei Tiulpin

SenTumorNet : A lightweight 3D U-Net model for brain tumor segmentation in sub-Saharan African MRI data / Papa Seydou Wane, Abdourahamane Balde, Guy Mbatchou, Dieu‑Donné Okalas Ossami, Mariama Dione, Dieumbe Khoule, Mor Diop, Ndeye Maty Bousso, Adama Traore, Aondona Iorumbur, Raymond Confidence, and Udunna Anazodo

TerangaNet : An optimized 3D U-Net for brain tumor segmentation in sub-Saharan African MRI volumes / Kéba Faye, Abdourahmane Balde, Racky Barro Diatta, Abdoul Wahab Soumare, Penda Ka, Mohameth Dia, Khoudia Sow, Doudou Mohamet Gaye, Mohamadou Bamba Diop, Magatte Diouf, Marième Dieng Fall, Guy Mbatchou, Aondona Iorumbur, Raymond Confidence, and Udunna Anazodo

EMedNeXt : An enhanced brain tumor segmentation framework for sub-Saharan Africa using MedNeXt V2 with deep supervision / Ahmed Jaheen, Abdelrahman Elsayed, Damir Kim, Daniil Tikhonov, Matheus Scatolin, Mohor Banerjee, Qiankun Ji, Mostafa Salem, Hu Wang, Sarim Hashmi, and Mohammad Yaqub

Improving pre-trained adult glioma segmentation models using only post-processing techniques / Abhijeet Parida, Daniel Capellán-Martín, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, María J. Ledesma‑Carbayo, and Marius George Linguraru

Domain adaptation for adult glioma segmentation in sub-Saharan Africa : An ensemble of nnU-Net v2 and MedNeXt / Willem P. E. Boonzaier, Farhana Moosa, Kagiso Lebang, Hanifa Jabaar, Aondona Iorumbur, Dong Zhang, and Confidence Raymond

GLIMS-MedNeXt : An ensemble framework for brain MRI segmentation in sub-Saharan Africa / Ali Azmoudeh, İlkay Öksüz, and Hazım Kemal Ekenel

MAPS-Glioma : Modality-specific augmentation and tissue-adaptive postprocessing for robust glioma segmentation in resource-limited settings / Ayomide B. Oladele, Helena Machibya, Mariam Kaoneka, Frederick Lyimo, Debora Hoza, Immaculata Kafumu, Idris Olalekan, Jeremiah Fadugba, Dong Zhang, Aondona Iorumbur, Raymond Confidence, Nicephorus Rutabasibwa, and Ugumba M. Kwikima

BRAIN-CATS : Brain tumour reliability-aware imaging with neural networks using calibration-aware training and segmentation / Abba Mohammed, Zulyadaini Muhammad Aminu, Ummulkhairi Ibrahim, Amina Suleiman Damo, Theodore Barfoot, Alexander Hammers, Raymond Confidence, Aondona Iorumbur, Abdulrazaq Zubair, and Mubaraq Yakubu

How we won BraTS‑SSA 2025 : Brain tumor segmentation in the sub-Saharan African population using segmentation-aware data augmentation and model ensembling / Claudia Takyi Ankomah, Livingstone Eli Ayivor, Ireneaus Nyame, Leslie Wambo, Patrick Yeboah Bonsu, Aondona Moses Iorumbur, Raymond Confidence, and Toufiq Musah

Training beyond convergence : Grokking nnU-Net for glioma segmentation in sub-Saharan MRI / Mohtady Barakat, Omar Salah, Ahmed Yasser, Mostafa Ahmed, Zahirul Arief, Waleed Khan, Dong Zhang, Aondona Iorumbur, Confidence Raymond, Mohannad Barakat, and Noha Magdy

Robust glioblastoma segmentation across multi-modal MRI : A study on BraTS 2025 challenge, task 5 (Sub-Saharan Africa) / Abbas Mohamed Rezk, Abdulkhalek Al‑Fakih, Abdullah Shazly, Kanghyun Ryu, and Mohammed A. Al‑Masni

Reassessing glioma segmentation strategies : nnU-Net as a strong baseline on limited sub-Saharan MRI data / Uwimana Lowami, Andrew Blayama Stephen, Confidence Raymond, Dong Zhang, Maruf Adewole, Udunna C. Anazodo, Mehmet Kurt, Damilare Olatunji, and Bernes Lorier Atabonfack

A fast, lightweight nnUNet-based brain tumor segmentation model optimized for low-resource African settings / John Emeka, Nwokoma Chidiebube, and Chika Ojiako

A self-supervised framework for glioma segmentation using Swin UNETR / Lesly Tsoptio Fougang, Joseph Muthui Wacira, Amal Jlassi, Dong Zhang, Aondona Iorumbur, and Confidence Raymond

LiMSA-UNet : A lightweight modality-selective attention ResUNet for brain-tumor segmentation / Freedmore Sidume, Nkuebe Clement Moleko, Botsile Gorata Masalela, Preference Mangwayana, Lame Kaisara, Refilwe Goitsemang, Topo Lefika Rapula, Dong Zhang, Aondona Iorumbur, and Confidence Raymond

Topology-driven fusion of nnU-Net and MedNeXt for accurate brain tumor segmentation on sub-Saharan Africa dataset / Prabin Bohara, Pralhad Kumar Shrestha, Arpan Rai, Usha Poudel Lamgade, Confidence Raymond, Dong Zhang, Aondona Iorumbur, Craig Jones, Mahesh Shakya, Bishesh Khanal, and Pratibha Kulung

Enhancing pediatric brain tumor segmentation with attention-guided 3D U-Net and a multi-step tumor-aware compositional augmentation pipeline in BraTS 2025 / Amin Tavallaii and Shamim Shah Ghasi

Enabling uncertainty measurement in multi-subregion tumor segmentation : BraTS 2025 pediatrics / Khashayar Namdar, Saeidehsadat Mirjalili, Sangwook Kim, Dominik Deniffel, Keith Brunt, Leo Anthony Celi, Michael Cusimano, and Pascal Tyrrell

Using a radiologically informed, deep learning cascade to refine segmentations of pediatric brain tumors from MRI / Timothy Mulvany, Heather Rose, Jan Novak, and Daniel Griffiths‑King

An advanced nnU‑Net framework for BraTS‑2025 PED / Xiaolong Li, Zhi‑Qin John Xu, Yan Ren, Tianming Qiu, and Xiaowen Wang

Memory-constrained, noise-resilient pediatric brain tumor segmentation via decoupled feature learning and domain adaptation : MICCAI BraTS‑PEDs 2025 challenge solution / Meng‑Yuan Chen and Hsiang‑Kuang Tony Liang

Frequency-aware ensemble learning for BraTS 2025 pediatric brain tumor segmentation / Yuxiao Yi, Qingyao Zhuang, Zhi‑Qin John Xu, Xiaowen Wang, Yan Ren, and Tianming Qiu

Adaptable segmentation pipeline for diverse brain tumors with radiomic-guided subtyping and lesion-wise model ensemble / Daniel Capellán‑Martín, Abhijeet Parida, Zhifan Jiang, Nishad Kulkarni, Krithika Iyer, Austin Tapp, Syed Muhammad Anwar, María J. Ledesma‑Carbayo, and Marius George Linguraru

A multitask learning approach for segmenting brain tumor sub-regions : Towards better generalization / Mumu Aktar, Tasneem Nasser, and Roberto Souza

BraTS‑FL : Enhancing generalization in brain tumor segmentation via federated learning / Simone Bendazzoli and Rodrigo Moreno

Scaling high-capacity ResUNet with dynamic batch for universal brain tumor segmentation : A BraTS 2025 “generalizable to all tumors” (GoAT) challenge solution / Meng‑Yuan Chen and Hsiang‑Kuang Tony Liang

Towards label-free brain tumor segmentation : Unsupervised learning with multimodal MRI / Gerard Comas‑Quiles, Carles Garcia‑Cabrera, Julia Dietlmeier, Noel E. O’Connor, and Ferran Marques

ADMFNet : Enhancing cross-tumor generalization in multi-modal MRI segmentation / Hongjuan Wang, Yixin Zhang, Jindong Sun, Xinjun An, Liying Zhu, and Chunyao Li

Enhancing brain tumor segmentation generalizability via pseudo-labeling and ratio-adaptive postprocessing / To‑Liang Hsu, Dang Khoa Nguyen, Pai Lin, Ching‑Ting Lin, and Wei‑Chun Wang

Ensemble-based generalization for brain tumor segmentation using nnU‑Net

Notes:

Includes bibliographical references and index

Online resource; title from PDF title page (Springer Nature Link, viewed April 28, 2026).

Local Notes:

variants and Swin UNETR / Vaidehi Satushe, Madhav Arora, Vibha Vyas, and Shilpa Metkar

Other Format:

Print version: BraTS-Lighthouse (Challenge) (2025 : Taejŏn-si, Korea) Segmentation, classification, and synthesis for brain tumors and traumatic brain injuries

ISBN:

9783032163653

303216365X

OCLC:

1585481503

Access Restriction:

Restricted for use by site license