arXiv:2606. 04705v1 Announce Type: cross Abstract: Semantic segmentation in medical imaging is a critical yet challenging task due to data scarcity and high variability across modalities.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.
Pixel-level annotation remains a major bottleneck in medical image segmentation, making weak supervision an attractive yet under-constrained alternative. We propose OBBSeg, an intermediate supervision paradigm guided by Oriented Bounding Boxes (OBBs) that bridges the gap between full and weak supervision.
MedSAM-3 is a text‑promptable medical segmentation model that builds on the Segment Anything Model (SAM) by fine‑tuning it with medical images and semantic concept labels. It enables precise anatomical segmentation through open‑vocabulary text descriptions, moving beyond purely geometric prompts. The accompanying MedSAM-3 Agent incorporates multimodal large language models to perform complex reasoning and iterative refinement, and experiments across X‑ray, MRI, ultrasound, CT, and video modalities show it outperforms existing specialist and foundation models.
The paper introduces the first adaptation of the MedSAM2 foundation model for interactive 3D segmentation of interstitial lung disease (ILD) on thoracic CT scans. It evaluates three fine‑tuning strategies and four prompt types—bounding‑boxes, points, lassos, and scribbles—finding that full model fine‑tuning yields the best performance, improving Dice scores by 4.7 percentage points over the baseline. A proof‑of‑concept workflow is presented where MedSAM2 is first initialized with an automatic prior and then refined by radiologist prompts, with all resources released on GitHub.
arXiv:2509. 25594v2 Announce Type: replace-cross Abstract: Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented.
arXiv:2608.22619v1 Announce Type: cross Abstract: Generative segmentation provides an alternative to direct pixel-wise prediction by operating on learned latent representations, but effective image-t...
The paper introduces Segment Anything Small (SAS), a data‑augmentation method that improves deep‑learning segmentation of small anatomical structures in ultrasound images. SAS uses two transformations: resizing and embedding organ thumbnails into a black background to vary organ scale, and adding noise to regions of interest to mimic tissue texture variability. Experiments on one internal and five external datasets show Dice score gains up to 0.35, with an average improvement of 0.16, and demonstrate that SAS enhances model robustness and generalizability without adding hallucinations or artifacts.