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PRISM: A Promptable and Robust Interactive Segmentation Model with Visual Prompts.

Hao Li¹, Han Liu¹, Dewei Hu¹

¹Vanderbilt University.

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention

|June 4, 2025

Summary

This summary is machine-generated.

PRISM, a Promptable and Robust Interactive Segmentation Model, precisely segments 3D medical images using iterative and confidence learning. This robust model achieves near-human accuracy for tumor identification across multiple datasets.

Keywords:

Interactive medical image segmentation Iterative correction Segment Anything Model (SAM)Tumor segmentation Visual prompts

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Area of Science:

Medical imaging analysis
Artificial intelligence in healthcare
Computational anatomy

Background:

Accurate segmentation of 3D medical images is crucial for diagnosis and treatment planning.
Existing methods face challenges with anatomical variations and ambiguous boundaries, particularly in tumor identification.

Purpose of the Study:

To introduce PRISM (Promptable and Robust Interactive Segmentation Model) for precise 3D medical image segmentation.
To enhance robustness and accuracy in medical image segmentation through novel design principles.

Main Methods:

PRISM utilizes sparse (points, boxes, scribbles) and dense (masks) visual prompts.
Employs iterative learning, confidence learning with multiple segmentation heads, corrective learning with a refinement network, and a hybrid encoder design.
Validated on four public datasets for tumor segmentation in the colon, pancreas, liver, and kidney.

Main Results:

PRISM significantly improves segmentation performance compared to state-of-the-art methods.
Achieves results comparable to human-level accuracy in complex tumor segmentation tasks.
Demonstrates robustness across datasets with anatomical variations and ambiguous boundaries.

Conclusions:

PRISM offers a robust and accurate solution for 3D medical image segmentation.
The model's interactive and promptable nature facilitates precise tumor identification.
Publicly available code enables further research and application in medical AI.