# PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts arXiv: https://arxiv.org/abs/2404.15028 ยท HTML: https://mlsanity.com/paper/2404.15028/ ## Key facts - Cited by 15 papers in the map (data through 14 Oct 2026). - Builds on 23 of its 35 references that are in the map. - First posted on arXiv 23 Apr 2024. ## Builds on (most cited first, 23 of 23) - [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023) - [The Medical Segmentation Decathlon](https://mlsanity.com/paper/2106.05735/) (2021) - [The Liver Tumor Segmentation Benchmark (LiTS)](https://mlsanity.com/paper/1901.04056/) (2019) - [Self-Supervised Pre-Training of Swin Transformers for 3D Medical Image Analysis](https://mlsanity.com/paper/2111.14791/) (2021) - [The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 Challenge](https://mlsanity.com/paper/1912.01054/) (2019) - [Reviving Iterative Training with Mask Guidance for Interactive Segmentation](https://mlsanity.com/paper/2102.06583/) (2021) - [SAM-Med2D](https://mlsanity.com/paper/2308.16184/) (2023) - [Interactive Medical Image Segmentation using Deep Learning with Image-specific Fine-tuning](https://mlsanity.com/paper/1710.04043/) (2017) - [SimpleClick: Interactive Image Segmentation with Simple Vision Transformers](https://mlsanity.com/paper/2210.11006/) (2022) - [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017) - [SAM-Med3D: Towards General-purpose Segmentation Models for Volumetric Medical Images](https://mlsanity.com/paper/2310.15161/) (2023) - [3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2209.15076/) (2022) - [MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation](https://mlsanity.com/paper/2309.08842/) (2023) - [3DSAM-adapter: Holistic adaptation of SAM from 2D to 3D for promptable tumor segmentation](https://mlsanity.com/paper/2306.13465/) (2023) - [MIDeepSeg: Minimally Interactive Segmentation of Unseen Objects from Medical Images Using Deep Learning](https://mlsanity.com/paper/2104.12166/) (2021) - [DeSAM: Decoupled Segment Anything Model for Generalizable Medical Image Segmentation](https://mlsanity.com/paper/2306.00499/) (2023) - [I-MedSAM: Implicit Medical Image Segmentation with Segment Anything](https://mlsanity.com/paper/2311.17081/) (2023) - [Promise:Prompt-driven 3D Medical Image Segmentation Using Pretrained Image Foundation Models](https://mlsanity.com/paper/2310.19721/) (2023) - [CFR-ICL: Cascade-Forward Refinement with Iterative Click Loss for Interactive Image Segmentation](https://mlsanity.com/paper/2303.05620/) (2023) - [False Negative/Positive Control for SAM on Noisy Medical Images](https://mlsanity.com/paper/2308.10382/) (2023) - [Cats: Complementary CNN and Transformer Encoders for Segmentation](https://mlsanity.com/paper/2208.11572/) (2022) - [ScribbleSeg: Scribble-based Interactive Image Segmentation](https://mlsanity.com/paper/2303.11320/) (2023) - [Assessing Test-time Variability for Interactive 3D Medical Image Segmentation with Diverse Point Prompts](https://mlsanity.com/paper/2311.07806/) (2023) ## Built on by (most cited first, 15 of 15) - [nnInteractive: Redefining 3D Promptable Segmentation](https://mlsanity.com/paper/2503.08373/) (2025) - [MedSAM-U: Uncertainty-Guided Auto Multi-Prompt Adaptation for Reliable MedSAM](https://mlsanity.com/paper/2409.00924/) (2024) - [LesionLocator: Zero-Shot Universal Tumor Segmentation and Tracking in 3D Whole-Body Imaging](https://mlsanity.com/paper/2502.20985/) (2025) - [RadioActive: 3D Radiological Interactive Segmentation Benchmark](https://mlsanity.com/paper/2411.07885/) (2024) - [Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound images](https://mlsanity.com/paper/2407.08020/) (2024) - [Promptable cancer segmentation using minimal expert-curated data](https://mlsanity.com/paper/2505.17915/) (2025) - [Enhancing the Safety of Medical Vision-Language Models by Synthetic Demonstrations](https://mlsanity.com/paper/2506.09067/) (2025) - [A methodology for clinically driven interactive segmentation evaluation](https://mlsanity.com/paper/2510.09499/) (2025) - [PRISM Lite: A lightweight model for interactive 3D placenta segmentation in ultrasound](https://mlsanity.com/paper/2408.05372/) (2024) - [Prompt Group-Aware Training for Robust Text-Guided Nuclei Segmentation](https://mlsanity.com/paper/2603.06384/) (2026) - [RoadGIE: Towards A Global-Scale Aerial Benchmark for Generalizable Interactive Road Extraction](https://mlsanity.com/paper/2605.26862/) (2026) - [Understanding From Human Perspective: A Multi-agent System for Interactive Egocentric Medical Image Segmentation](https://mlsanity.com/paper/2607.17341/) (2026) - [SLIP: Segmentation with Low-latency Interactive Prompting for 3D Medical Images](https://mlsanity.com/paper/2607.22332/) (2026) - [IMVS: Interactive Medical Volume Segmentation with Test-Time Adaptation - A New Method for Annotating Radiology Datasets](https://mlsanity.com/paper/2609.16775/) (2026) - [From Human Corrections to Learned Segmentations: A Critical Mathematical Review of Information, Learning, and Human Effort](https://mlsanity.com/paper/2610.20396/) (2026) ## Abstract (arXiv) In this paper, we present PRISM, a Promptable and Robust Interactive Segmentation Model, 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.