# K-Prism: A Knowledge-Guided and Prompt Integrated Universal Medical Image Segmentation Model arXiv: https://arxiv.org/abs/2509.25594 ยท HTML: https://mlsanity.com/paper/2509.25594/ ## Key facts - Cited by 3 papers in the map (data through 14 Oct 2026). - Placed in Medical and biomedical MoE, one of 12 directions in Mixture-of-experts. - Builds on 32 of its 47 references that are in the map. - First posted on arXiv 29 Sep 2025. ## Placed in - Mixture-of-experts / [Medical and biomedical MoE](https://mlsanity.com/moe/medical-and-biomedical-moe/) ## Builds on (most cited first, 30 of 32) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [SAM 2: Segment Anything in Images and Videos](https://mlsanity.com/paper/2408.00714/) (2024) - [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021) - [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021) - [Skin Lesion Analysis Toward Melanoma Detection 2018: A Challenge Hosted by the International Skin Imaging Collaboration (ISIC)](https://mlsanity.com/paper/1902.03368/) (2019) - [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022) - [The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification](https://mlsanity.com/paper/2107.02314/) (2021) - [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019) - [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022) - [A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises](https://mlsanity.com/paper/2008.09104/) (2020) - [CLIP-Driven Universal Model for Organ Segmentation and Tumor Detection](https://mlsanity.com/paper/2301.00785/) (2023) - [Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation](https://mlsanity.com/paper/2106.05210/) (2021) - [Putting the Object Back into Video Object Segmentation](https://mlsanity.com/paper/2310.12982/) (2023) - [The KiTS19 Challenge Data: 300 Kidney Tumor Cases with Clinical Context, CT Semantic Segmentations, and Surgical Outcomes](https://mlsanity.com/paper/1904.00445/) (2019) - [Reviving Iterative Training with Mask Guidance for Interactive Segmentation](https://mlsanity.com/paper/2102.06583/) (2021) - [SimpleClick: Interactive Image Segmentation with Simple Vision Transformers](https://mlsanity.com/paper/2210.11006/) (2022) - [UniverSeg: Universal Medical Image Segmentation](https://mlsanity.com/paper/2304.06131/) (2023) - [Generalized Radiograph Representation Learning via Cross-supervision between Images and Free-text Radiology Reports](https://mlsanity.com/paper/2111.03452/) (2021) - [NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection](https://mlsanity.com/paper/2107.05023/) (2021) - [UniSeg: A Prompt-driven Universal Segmentation Model as well as A Strong Representation Learner](https://mlsanity.com/paper/2304.03493/) (2023) - [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025) - [Training Like a Medical Resident: Context-Prior Learning Toward Universal Medical Image Segmentation](https://mlsanity.com/paper/2306.02416/) (2023) - [Medical Image Analysis on Left Atrial LGE MRI for Atrial Fibrillation Studies: A Review](https://mlsanity.com/paper/2106.09862/) (2021) - [AtrialJSQnet: A New Framework for Joint Segmentation and Quantification of Left Atrium and Scars Incorporating Spatial and Shape Information](https://mlsanity.com/paper/2008.04729/) (2020) - [Rethinking Interactive Image Segmentation with Low Latency, High Quality, and Diverse Prompts](https://mlsanity.com/paper/2404.00741/) (2024) - [Tyche: Stochastic In-Context Learning for Medical Image Segmentation](https://mlsanity.com/paper/2401.13650/) (2024) - [Atrial Scar Quantification via Multi-scale CNN in the Graph-cuts Framework](https://mlsanity.com/paper/1902.07877/) (2019) - [AtrialGeneral: Domain Generalization for Left Atrial Segmentation of Multi-Center LGE MRIs](https://mlsanity.com/paper/2106.08727/) (2021) - [Show and Segment: Universal Medical Image Segmentation via In-Context Learning](https://mlsanity.com/paper/2503.19359/) (2025) - [MultiverSeg: Scalable Interactive Segmentation of Biomedical Imaging Datasets with In-Context Guidance](https://mlsanity.com/paper/2412.15058/) (2024) ## Built on by (most cited first, 3 of 3) - [Synergizing Discriminative Exemplars and Self-Refined Experience for MLLM-based In-Context Learning in Medical Diagnosis](https://mlsanity.com/paper/2603.27737/) (2026) - [Learning Where to Look: Anatomical Grounding and Guided Attention for Cardiac MRI Vision-Language Models](https://mlsanity.com/paper/2609.39899/) (2026) - [UniPro: Unified Multi-Mode Medical Image Segmentation from 2D Images to 3D Volumes via Propagation](https://mlsanity.com/paper/2610.06938/) (2026) ## Abstract (arXiv) Medical image segmentation is fundamental to clinical decision-making, yet existing models remain fragmented. They are usually trained on single knowledge sources and specific to individual tasks, modalities, or organs. This fragmentation contrasts sharply with clinical practice, where experts seamlessly integrate diverse knowledge: anatomical priors from training, exemplar-based reasoning from reference cases, and iterative refinement through real-time interaction. We present K-Prism, a unified segmentation framework that mirrors this clinical flexibility by systematically integrating three knowledge paradigms: (i) textitsemantic priors learned from annotated datasets, (ii) textitin-context knowledge from few-shot reference examples, and (iii) textitinteractive feedback from user inputs like clicks or scribbles. Our key insight is that these heterogeneous knowledge sources can be encoded into a dual-prompt representation: 1-D sparse prompts defining textitwhat to segment and 2-D dense prompts indicating textitwhere to attend, which are then dynamically routed through a Mixture-of-Experts (MoE) decoder. This design enables flexible switching between paradigms and joint training across diverse tasks without architectural modifications. Comprehensive experiments on 18 public datasets spanning diverse modalities (CT, MRI, X-ray, pathology, ultrasound, etc.) demonstrate that K-Prism achieves state-of-the-art performance across semantic, in-context, and interactive segmentation settings.