The paper introduces Missing Modality-Aware Local Temperature Scaling (MMA‑LTS), a post‑hoc voxel‑wise confidence calibration technique designed for multimodal brain tumor segmentation. MMA‑LTS adapts a temperature field based on which MRI modalities are present and a voxel‑wise difficulty score, addressing calibration errors that arise when specific modalities are missing. Experiments on BraTS 2020 and FeTS 2024 demonstrate that MMA‑LTS improves calibration while maintaining segmentation accuracy across various missing‑modality scenarios.
Multi-parametric MRI is the gold standard for neuro-oncology, yet acquiring complete 4-modality protocols may be unfeasible in real-world clinical practice. When handling missing modalities, existing...
CAPS is a Clinically‑Verifiable Anatomy‑Prioritized Synthesis framework designed to impute missing MRI modalities for brain tumor segmentation. It uses a hybrid loss (L₁ + L_AFP + L_DualDice) to preserve deep anatomical features and topological boundaries, achieving high synthesis fidelity and robust segmentation (82.38% mean Dice) on the BraTS Adult Glioma dataset. The method also generalizes well to unseen cohorts and has been validated by blinded radiologists, who report improved image quality and clinical utility.
arXiv:2606. 19300v1 Announce Type: cross Abstract: Glioma segmentation in multiparametric MRI is a critical component of treatment planning.
arXiv:2607. 22727v1 Announce Type: cross Abstract: Medical image segmentation models often report high benchmark accuracy under ideal imaging conditions, yet their failures under clinical degradation can be quiet: sensor noise, patient motion, low- resolution acquisition, and contrast variability may all alter model behavior without producing an obvious warning.
arXiv:2606. 30374v1 Announce Type: cross Abstract: Multimodal MRI is essential for accurate brain tumor segmentation.
The paper introduces MSM‑Seg, a dual‑memory segmentation framework for 3D multi‑modal brain tumor segmentation. It combines a modality‑and‑slice memory attention module to capture cross‑modal and spatial‑slice dependencies, a multi‑scale category‑agnostic prompt encoder for whole‑tumor guidance, and a modality‑adaptive fusion decoder to integrate complementary decoding information. Experiments on various MRI datasets show that MSM‑Seg surpasses state‑of‑the‑art methods for metastases and glioma tumor segmentation.
arXiv:2608. 19788v1 Announce Type: cross Abstract: Trustworthy multimodal fusion in clinical settings requires handling incomplete and heterogeneous modality subsets across institutions, where privacy constraints prohibit centralized data sharing.
The study evaluates the reliability of deep‑ensemble uncertainty for brain tumour segmentation on the BraTS‑GoAT dataset. A 5‑fold cross‑validated nnU‑Net baseline and a 3‑seed deep ensemble were compared for calibration and error detection; the ensemble showed modest gains in calibration on in‑distribution data but the single model’s confidence remained flat while accuracy degraded under synthetic corruptions. Disagreement among ensemble members rose sharply with corruption severity, proving to be a more sensitive indicator of acquisition shift than single‑model confidence.
arXiv:2610.09392v1 Announce Type: new Abstract: Reliable volumetric segmentation is critical for clinical diagnostics, yet foundation models such as MedSAM remain deterministic and lack calibrated un...
arXiv:2610.01452v1 Announce Type: new Abstract: While state-of-the-art automated models for medical image segmentation achieve high mean performance, they frequently suffer from localized, catastroph...
BrainIAC is a unified framework for 3D brain lesion segmentation that handles heterogeneous MRI modalities and adapts online during interactive segmentation. It combines a multi‑modal backbone trained with zero‑filling and random modality dropping, 3D interactive prompts that default to fully automatic predictions, and a two‑stage online adaptation guided by pseudo‑labels and a Click‑Centered Gaussian loss. Experiments on seven MRI datasets show that the components work synergistically, outperforming existing methods and generalizing to unseen modalities and pathologies.
arXiv:2608.23745v1 Announce Type: cross Abstract: Accurate brain tumor segmentation from magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, surgical guidance, and diseas...