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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Published on: November 30, 2022

Unleashing Diffusion and State Space Models for Medical Image Segmentation.

Rong Wu^1,2, Ziqi Chen³, Liming Zhong⁴

¹Department of Biostatistics, School of Global Public Health, New York University, New York, NY, USA.

Journal of Imaging Informatics in Medicine

|April 6, 2026

Summary

This summary is machine-generated.

This study introduces DSM, a novel framework using diffusion and state space models for robust medical image segmentation. DSM effectively segments unseen tumor categories, significantly improving accuracy and outperforming existing methods.

Keywords:

Diffusion process Medical image segmentation State space model Text embeddings

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Area of Science:

Artificial Intelligence
Computer Vision
Medical Imaging Analysis

Background:

Current medical image segmentation models struggle with robustness on unseen data.
Identifying rare or novel tumor types requires advanced segmentation capabilities.

Purpose of the Study:

To develop a robust segmentation framework (DSM) for identifying unseen tumor categories.
To enhance the model's ability to generalize across diverse medical imaging scenarios.

Main Methods:

Utilized a novel framework combining diffusion and state space models (DSM).
Employed dual object query sets within modified attention decoders for enhanced classification.
Incorporated object-aware feature grouping for organ queries and diffusion-based prompts for tumor queries.
Integrated diffusion-guided feature fusion and CLIP text embeddings for improved semantic segmentation and linguistic transfer.

Main Results:

DSM demonstrated superior performance in segmenting unseen tumor categories.
Achieved significant improvements in out-of-distribution detection metrics: 0.1962 in mean AUROC, 0.2675 in mean FPR95, and 0.1736 in mean DSC.
Showcased enhanced robustness in multi-label tasks and diverse segmentation scenarios.

Conclusions:

DSM offers a robust solution for medical image segmentation, particularly for unseen and rare tumor types.
The framework significantly advances the capabilities of AI in medical diagnostics and analysis.