arXiv:2602. 05387v2 Announce Type: replace-cross Abstract: MRI provides superior soft tissue contrast without ionizing radiation; however, the absence of electron density information limits its direct use for dose calculation.
arXiv:2510.14383v4 Announce Type: replace Abstract: Accurate brain tumor segmentation is significant for clinical diagnosis and treatment but remains challenging due to tumor heterogeneity. Mamba-bas...
arXiv:2509. 21913v2 Announce Type: replace-cross Abstract: Background: Cone-beam computed tomography CBCT is a commonly used modality for image guided radiotherapy.
arXiv:2606. 15370v1 Announce Type: cross Abstract: This work demonstrates a full reproduction and extension of MNet, a hybrid 2D/3D convolutional network designed for anisotropic medical image segmentation.
arXiv:2605.20470v2 Announce Type: replace-cross Abstract: Cone-beam CT (CBCT) is routinely acquired during radiotherapy for patient setup, but its quantitative reliability is degraded by scatter, noi...
nnFoundation introduces complementary convolutional and transformer-based 3D foundation models for radiology, trained on 2.1 million CT, MRI, and PET volumes from 125 datasets. The models are evaluated on 108 tasks—including segmentation, detection, classification, report generation, and image retrieval—under domain shift, low-data, and low-compute scenarios, consistently outperforming prior 3D foundation models and training from scratch. Performance varies by task type, with convolutional models excelling at spatially localized tasks and transformer models at global semantic reasoning, and dynamic alignment with dataset characteristics further enhances transferability.
arXiv:2607. 28858v1 Announce Type: cross Abstract: Automatic brain tumor segmentation from magnetic resonance imaging (MRI) has become a fundamental task in computer-assisted diagnosis, treatment planning, and disease monitoring.
arXiv:2605.30972v2 Announce Type: replace Abstract: Accurate 3D medical image segmentation requires both fine spatial detail and long-range volumetric context. Although Mamba provides efficient long-...
The paper introduces SWIFT, a Swin V2‑based model pretrained on 10,444 3D CT volumes and fine‑tuned for rectal cancer segmentation on T2‑weighted MRI. Four configurations—full fine‑tuning (SWIFT), decoder compression (SWIFTe), low‑rank adaptation (SWIFTe‑LoRA), and a LoRA‑decoder ensemble (SWIFTe‑LDE4)—were evaluated on 247 cases, showing that SWIFTe reduces parameters by 70.1% while improving tumor detection and radiomic agreement. The study also demonstrates a trade‑off between detection and boundary agreement, and highlights that SWIFTe‑LDE4 achieves the lowest calibration errors after temperature scaling.
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:2606. 24313v1 Announce Type: new Abstract: AI-driven image-to-image synthesis is rapidly advancing, with growing applications in medical imaging.
The paper introduces DAMM‑Net++, a 2.5D neural network for thoracic organ‑at‑risk and target volume segmentation that tackles inter‑slice surface incoherence, small low‑contrast target failure, and lack of per‑case reliability signals. Its core is an anatomy‑change‑aware bidirectional selective state‑space memory that propagates context across axial slices, complemented by a boundary‑aware decoder and an uncertainty head for calibrated per‑voxel confidence. Evaluations on 2,146 patients, an external cohort, and a reader study show high Dice scores (0.955), low HD95 (3.78 mm), significant time savings (75‑80 %) for clinicians, and improved junior‑reader performance, with the system fully integrated into a clinical workflow.