Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology. To ease the high annotation cost of PET/CT segmentation, semi-supervised learning (SSL) provides a practical and effective solution for developing deep models with limited labeled data.
arXiv:2606. 15611v1 Announce Type: cross Abstract: Organ segmentation from PET/CT is critical for quantitative analysis and radiotherapy planning in oncology.
arXiv:2608. 19666v1 Announce Type: new Abstract: Deep learning-based whole-body PET-CT lesion segmentation can support cancer staging, treatment planning, and response assessment, but generalization is limited by scarce annotations and domain shifts.
arXiv:2608.30844v1 Announce Type: cross Abstract: Interactive lesion segmentation in whole-body PET/CT requires a model to provide a strong initial prediction while also responding efficiently to spa...
arXiv:2609.06729v2 Announce Type: replace Abstract: Accurate 3D brain tumour segmentation from multi-modal Magnetic Resonance Imaging (MRI) is essential for clinical diagnosis and treatment planning....
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.
The paper proposes a two‑stage learning framework for multi‑organ segmentation that handles partially annotated datasets and domain shifts. First, the model learns accurate segmentations from available annotations to build robust feature representations. Second, it introduces learnable organ prototypes and a Sinkhorn‑triplet loss to enforce organ‑wise feature consistency across datasets, keeping embeddings of the same organ close while separating different organs, even when annotations are missing.
arXiv:2606. 28392v1 Announce Type: cross Abstract: Accurate lesion segmentation in PET/CT is critical for oncology, yet remains challenging because physiologic tracer uptake and artifacts can mimic malignant signal.
arXiv:2609.23815v1 Announce Type: new Abstract: Medical image segmentation models typically rely on large amounts of densely annotated volumetric data, limiting their scalability across tasks and ima...
arXiv:2606. 03888v1 Announce Type: cross Abstract: Self-supervised learning has enabled large-scale pre-training on 2D natural images, producing general-purpose visual representations that transfer effectively across tasks.
Merlin Plus is a new, large-scale CT dataset that provides radiologist‑created tumor masks for nine different organs, adding 1,153 per‑voxel masks and longitudinal metadata to the existing Merlin collection. The dataset was built using a report‑based active‑learning framework, where radiology reports flag tumor cases, a segmentation model generates initial masks, and radiologists review and correct them, thereby reducing annotation effort while preserving high quality. The added longitudinal data enables temporal modeling of cancer progression, supporting scalable multi‑organ cancer detection, segmentation, and longitudinal analysis in CT.
The paper introduces SSS, a semi‑supervised framework that builds on the Vision Foundation Model SAM‑2 to improve medical image segmentation. It combines a weak‑to‑strong consistency regularization with a Discriminative Feature Enhancement mechanism and a prompt generator that uses Physical Constraints with a Sliding Window to supply prompts for unlabeled data. Experiments on the ACDC and BHSD datasets show that SSS outperforms prior methods, achieving a 53.15 Dice score on BHSD, a +3.65 improvement over the state of the art.
arXiv:2609.24627v1 Announce Type: new Abstract: Multi-organ segmentation using deep learning requires large amounts of annotated patient data; however, institutions often lack sufficiently large and...