PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts
Open accessAbstract
In this paper, we present PRISM, a P romptable and R obust I nteractive S egmentation M odel, aiming for precise segmentation of 3D medical images. PRISM accepts various visual inputs, including points, boxes, and scribbles as sparse prompts, as well as masks as dense prompts. Specifically, PRISM is designed with four principles to achieve robustness: (1) Iterative learning. The model produces segmentations by using visual prompts from previous iterations to achieve progressive improvement. (2) Confidence learning. PRISM employs multiple segmentation heads per input image, each generating a continuous map and a confidence score to optimize predictions. (3) Corrective learning. Following each segmentation iteration, PRISM employs a shallow corrective refinement network to reassign mislabeled voxels. (4) Hybrid design. PRISM integrates hybrid encoders to better capture both the local and global information. Comprehensive validation of PRISM is conducted using four public datasets for tumor segmentation in the colon, pancreas, liver, and kidney, highlighting challenges caused by anatomical variations and ambiguous boundaries in accurate tumor identification. Compared to state-of-the-art methods, both with and without prompt engineering, PRISM significantly improves performance, achieving results that are close to human levels. The code is publicly available at https://github.com/MedICL-VU/PRISM.
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To make the comparison with PRISM controlled, we use the released PRISM data splits without resampling the train/test partition, resulting in 26 test cases for MSD-Colon and 61 test cases for KiTS21.
“To make the comparison with PRISM [16] controlled, we use the released PRISM data splits without resampling the train/test partition, resulting in 26 test cases for MSD-Colon and 61 test cases for KiTS21.”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [16]
The Experiments > Datasets passage says PRISM used colon and kidney datasets with 126 and 300 cases, respectively, and adopted a 70/10/20 training/validation/testing split from a prior study. It does not give the exact test counts of 26 and 61 or state that those partitions were released; the citing authors’ decision not to resample is their own procedure. (LLM, conf=0.82)
Experimental — accuracy not yet measuredPRISM combines clicks, 3D bounding boxes, and scribbles with an iterative refinement head, making it a closely matched baseline for interactive 3D tumor segmentation.
“PRISM [16] combines clicks, 3D bounding boxes, and scribbles with an iterative refinement head, making it a closely matched baseline for interactive 3D tumor segmentation.”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [16]
If a voxel is labeled as foreground in one round and background in a later round, PRISM's two-map refinement input can activate both positive and negative prompt channels at that location.
“If a voxel is labeled as foreground in one round and background in a later round, PRISM's two-map refinement input can activate both positive and negative prompt channels at that location.”
From Correction-aware interactive 3D tumor segmentation with sparse and revisable prompts (2026) · cites this paper as [16]
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Self-citation
- Authors
- Li, H., Liu, H., Hu, D., Wang, J., & Oguz, I.
- Affiliations
- Vanderbilt University (US)
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024
- Volume
- 15003
- Pages
- 389-399
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- PMC
- PMC12128912
- OpenAlex
- W4403071691
- Cited by
- 12
- References
- 32
- Citations by year
- 2026: 3 · 2025: 7 · 2024: 2
- FWCI
- 2.48
- Type
- conference paper
- Language
- en
- Open access
- green
- pmc.ncbi.nlm.nih.gov
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Science Foundation (2220401)
- National Institutes of Health
- National Institute of Neurological Disorders and Stroke (U01NS106845)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD109739)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Medical Image Segmentation Techniques
- Life Sciences › Neuroscience › Neurology › Brain Tumor Detection and Classification
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- interactive segmentation, 3D medical image segmentation, visual prompts, point prompts, iterative learning, hybrid encoder, tumor segmentation, colon, pancreas, liver, kidney, prompt engineering
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