The paper introduces nnInteractive Agent, an open‑source system that lets general‑purpose AI agents perform 3D image annotation tasks by interacting with the nnInteractive segmentation model. Users issue plain‑language requests, and the agent automatically identifies targets, places prompts, evaluates and corrects the resulting volumetric masks, and can even generate code for additional processing. On a diverse benchmark of CT, MRI, PET, micro‑CT, and insect scans, agents such as Claude Code and Codex achieve mean Dice scores of 0.81–0.84 from the request alone, matching near‑oracle performance when given target locations.
arXiv:2609.01554v1 Announce Type: cross Abstract: Automated lesion segmentation in whole-body PET/CT is complicated by the variety of physiological tracer uptake patterns and by the differing appeara...
arXiv:2604.16729v2 Announce Type: replace-cross Abstract: State-of-the-art large language models (LLMs) show high performance in general visual question answering. However, a fundamental limitation r...
arXiv:2609.16775v1 Announce Type: new Abstract: Annotating large radiology datasets is bottlenecked by the manual effort of delineating structures slice-by-slice in 3D volumes. Interactive methods re...
arXiv:2609.25743v1 Announce Type: new Abstract: Interactive segmentation of 3D medical images supports quantitative analysis of anatomical structures and disease while allowing users to specify and r...
NV-Reason-CT is a generative vision‑language model designed for chest and abdominal CT analysis that preserves native 3D visual encoding and incorporates radiologist‑guided reasoning. The system couples a 3D vision transformer with a language model, feeding all visual tokens and their 3D coordinates directly into language decoding to maintain volumetric spatial information. Trained on a curated corpus of about 550,000 multimodal instruction examples, the model supports abnormality classification, report generation, and interactive reasoning, achieving strong performance on CT benchmarks and reducing expert interpretation time by 50%.
The paper introduces SPAR‑Bench, a set of eight probes designed to test whether medical vision models can reason about anatomy in abdominal CT scans. Experiments across five architectures and three foundation models—both frozen and fine‑tuned—show that while models can recall canonical organ locations, they fail to perform relational reasoning or spatial comparisons within a patient, even under zero‑shot transfer. The study also demonstrates that pooled probing underestimates a model’s relational capabilities and that open‑weight multimodal large language models perform poorly on these tasks.
DALE-CT introduces depth‑aware 2D slice encoders that learn an anatomical world model of chest CT scans without 3D or positional supervision. By sampling self‑supervised views across a physical $z$‑axis slab, the encoder captures how anatomy changes between neighboring slices, enabling it to recover slice ordering and distinguish slices by anatomy alone. The model, trained on a large 287k‑scan corpus, achieves state‑of‑the‑art performance on CT‑RATE and is released with full code and evaluation tools.
arXiv:2609.37648v1 Announce Type: new Abstract: Preoperative liver-tumor assessment requires segmentation, physical-space measurement, visual evidence, and resection planning from the same three-dime...
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:2611.02581v1 Announce Type: cross Abstract: Automated liver ultrasound report generation improves the diagnosis of liver diseases by producing clinically structured descriptions of anatomical a...
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:2611.05693v1 Announce Type: new Abstract: Sli2Vol propagates a binary mask through affinities between adjacent slices, but an error can persist because each prediction becomes the input to the...