default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDHNTS-MRG@MICCAI 2024: Marrakesh, Morocco
- Kareem A. Wahid
, Cem Dede, Mohamed A. Naser, Clifton D. Fuller:
Head and Neck Tumor Segmentation for MR-Guided Applications - First MICCAI Challenge, HNTS-MRG 2024, Held in Conjunction with MICCAI 2024, Marrakesh, Morocco, October 17, 2024, Proceedings. Lecture Notes in Computer Science 15273, Springer 2025, ISBN 978-3-031-83273-4 - Kareem A. Wahid, Cem Dede, Dina El-Habashy, Serageldin Kamel, Michael K. Rooney, Yomna Khamis, Moamen Abobakr Abdelaal, Sara Ahmed, Kelsey L. Corrigan, Enoch Chang, Stephanie O. Dudzinski, Travis C. Salzillo, Brigid McDonald, Samuel L. Mulder, Lucas McCullum, Qusai Alakayleh, Carlos Sjogreen, Renjie He, Abdallah Sherif Radwan Mohamed, Stephen Y. Lai, John P. Christodouleas, Andrew J. Schaefer, Mohamed A. Naser, Clifton D. Fuller:
Overview of the Head and Neck Tumor Segmentation for Magnetic Resonance Guided Applications (HNTS-MRG) 2024 Challenge. 1-35 - Jintao Ren, Kim Hochreuter, Mathis Rasmussen, Jesper Folsted Kallehauge, Stine Sofia Korreman:
Gradient Map-Assisted Head and Neck Tumor Segmentation: A Pre-RT to Mid-RT Approach in MRI-Guided Radiotherapy. 36-49 - Jessica Kächele, Maximilian Zenk, Maximilian Rokuss, Constantin Ulrich, Tassilo Wald, Klaus H. Maier-Hein:
Enhanced nnU-Net Architectures for Automated MRI Segmentation of Head and Neck Tumors in Adaptive Radiation Therapy. 50-64 - Zihao Wang, Mengye Lyu:
Head and Neck Tumor Segmentation for MRI-Guided Radiation Therapy Using Pre-trained STU-Net Models. 65-74 - Litingyu Wang, Wenjun Liao, Shichuan Zhang, Guotai Wang:
Head and Neck Tumor Segmentation of MRI from Pre- and Mid-Radiotherapy with Pre-Training, Data Augmentation and Dual Flow UNet. 75-86 - Elias Tappeiner, Christian Gapp, Martin Welk, Rainer Schubert:
Head and Neck Tumor Segmentation on MRIs with Fast and Resource-Efficient Staged nnU-Nets. 87-98 - Xin Tie, Weijie Chen, Zachary Huemann, Brayden Schott, Nuohao Liu, Tyler J. Bradshaw:
Deep Learning for Longitudinal Gross Tumor Volume Segmentation in MRI-Guided Adaptive Radiotherapy for Head and Neck Cancer. 99-111 - Mehdi Astaraki, Iuliana Toma-Dasu:
Enhancing Head and Neck Tumor Segmentation in MRI: The Impact of Image Preprocessing and Model Ensembling. 112-122 - Jintao Ren, Kim Hochreuter, Jesper Folsted Kallehauge, Stine Sofia Korreman:
UMamba Adjustment: Advancing GTV Segmentation for Head and Neck Cancer in MRI-Guided RT with UMamba and NnU-Net ResEnc Planner. 123-135 - Nikoo Moradi, André Ferreira, Behrus Puladi, Jens Kleesiek, Emad Fatemizadeh, Gijs Luijten, Victor Alves, Jan Egger:
Comparative Analysis of nnUNet and MedNeXt for Head and Neck Tumor Segmentation in MRI-Guided Radiotherapy. 136-153 - Jing Ni, Qiulei Yao, Yanfei Liu, Haikun Qi:
A Coarse-to-Fine Framework for Mid-Radiotherapy Head and Neck Cancer MRI Segmentation. 154-165 - Abdul Qayyum, Moona Mazher, Steven A. Niederer:
Assessing Self-supervised xLSTM-UNet Architectures for Head and Neck Tumor Segmentation in MR-Guided Applications. 166-178 - Frank N. Mol, Luuk van der Hoek, Baoqiang Ma, Bharath Chowdhary Nagam, Nanna Maria Sijtsema, Lisanne van Dijk, Kerstin Bunte, Rifka Vlijm, Peter M. A. van Ooijen:
MRI-Based Head and Neck Tumor Segmentation Using nnU-Net with 15-Fold Cross-Validation Ensemble. 179-190 - Mikko Saukkoriipi, Jaakko Sahlsten, Joel Jaskari, Ahmed Al-Tahmeesschi, Laura Ruotsalainen, Kimmo Kaski:
Head and Neck Tumor Segmentation Using Pre-RT MRI Scans and Cascaded DualUNet. 191-203 - Marek Wodzinski:
Benchmark of Deep Encoder-Decoder Architectures for Head and Neck Tumor Segmentation in Magnetic Resonance Images: Contribution to the HNTSMRG Challenge. 204-213 - Maria Baldeon Calisto:
Ensemble of LinkNet Networks for Head and Neck Tumor Segmentation. 214-221 - Yichen An, Zhimin Wang, Eric Ma, Hao Jiang, Weiguo Lu:
Enhancing nnUNetv2 Training with Autoencoder Architecture for Improved Medical Image Segmentation. 222-229 - Andrei Iantsen:
Improving the U-Net Configuration for Automated Delineation of Head and Neck Cancer on MRI. 230-240 - Awj Twam, Adrian Celaya, Evan Lim, Khaled Elsayes, David Fuentes, Tucker J. Netherton:
Head and Neck Gross Tumor Volume Automatic Segmentation Using PocketNet. 241-249 - Kaiyuan Ji, Zhihan Wu, Jing Han, Jun Jia, Guangtao Zhai, Jiannan Liu:
Application of 3D nnU-Net with Residual Encoder in the 2024 MICCAI Head and Neck Tumor Segmentation Challenge. 250-258 - Dominic LaBella:
Ensemble Deep Learning Models for Automated Segmentation of Tumor and Lymph Node Volumes in Head and Neck Cancer Using Pre- and Mid-Treatment MRI: Application of Auto3DSeg and SegResNet. 259-273
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
last updated on 2026-09-05 00:24 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated, funded, and maintained by:
Schloss Dagstuhl - Leibniz Center for Informatics is a member of the Leibniz Association and is funded by the federal government of Germany and the states of Germany, especially Saarland and Rhineland‑Palatinate.
