A Bayesian deep segmentation framework for glioblastoma tumor segmentation using follow-up MRIs
Our proposed model incorporates brain structural changes following surgical and therapeutic interventions and leverages uncertainty metrics to refine estimates of tumor, demonstrating the potential for improved patient management.
Authors
- Juan Carlos Rodriguez Quintero
- Kang‐Lin Hsieh (ORCID: https://orcid.org/0000-0002-2043-5605)
- Octavio Arevalo (ORCID: https://orcid.org/0000-0001-5307-5012)
- Tanjida Kabir
- Shayan Shams (ORCID: https://orcid.org/0000-0002-9089-2051)
- Mahboubeh Madadi (ORCID: https://orcid.org/0000-0002-2787-722X)
- Xiaoqian Jiang (ORCID: https://orcid.org/0000-0001-9933-2205)
- Jay‐Jiguang Zhu (ORCID: https://orcid.org/0000-0003-4635-0914)
- Kangyi Zhao
- Roy Riascos (ORCID: https://orcid.org/0000-0002-3081-0413)
- Yu‐Chun Hsu (ORCID: https://orcid.org/0000-0002-0714-2250)
- Luis Nunez
Institutions
- University of Pittsburgh (US)
- The University of Texas Health Science Center (US)
- Louisiana State University Health Sciences Center Shreveport (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Frontiers in Neuroimaging
- Published
- 2025-10-23
- DOI
- https://doi.org/10.3389/fnimg.2025.1630245
- Citations
- 1
- Primary Topic
- Brain Tumor Detection and Classification
- Type
- article
- Field-Weighted Citation Impact
- 0.84