One-Prompt Segmentation: A Versatile Approach for Universal Medical Image Segmentation

Core Concepts

One-Prompt Segmentation combines the strengths of one-shot and interactive segmentation methods to enable zero-shot generalization across diverse medical imaging tasks, requiring only a single prompted sample during inference.

Abstract

The paper introduces a novel paradigm called "One-Prompt Segmentation" for universal medical image segmentation. The key idea is to train a foundation model that can adapt to unseen tasks by leveraging a single prompted sample during inference, without the need for retraining or fine-tuning.
The authors first gather a large-scale dataset of 78 open-source medical imaging datasets, covering a wide range of organs, tissues, and anatomies. They then train the One-Prompt Model, which consists of an image encoder and a sequence of One-Prompt Former modules as the decoder. The One-Prompt Former efficiently integrates the prompted template feature with the query feature at multiple scales.
The paper also introduces four different prompt types - Click, BBox, Doodle, and SegLab - to cater to the diverse needs of medical image segmentation tasks. These prompts are annotated by a team of clinicians across the dataset.
The authors extensively evaluate the One-Prompt Model on 14 previously unseen medical imaging tasks, demonstrating its superior zero-shot segmentation capabilities compared to a wide range of related methods, including few-shot and interactive segmentation models. The model exhibits robust performance and stability when provided with different prompted templates during inference.
The paper highlights the significant practical benefits of the One-Prompt Segmentation approach, including its user-friendly interface, cost-effectiveness, and potential for building automatic pipelines in clinical settings.

Stats

The paper trains the One-Prompt Model on 64 open-source medical imaging datasets and evaluates it on 14 previously unseen datasets.
The authors collected over 3,000 clinician-labeled prompts across the datasets.

Quotes

"One-Prompt Segmentation combines the strengths of one-shot and interactive methods to meet the real clinical requirements."
"Our model is trained on 64 datasets, with clinicians prompting a part of the data."
"One-Prompt Segmentation learns a more general function y = fθ(xd_j, kd) performing on any task d, where kd = {xd_c, pd_c} comprising one fixed template image xd_c and a paired prompt pd_c available for task d."

Key Insights Distilled From

One-Prompt to Segment All Medical Images

by Junde Wu,Jia... at arxiv.org 04-12-2024

https://arxiv.org/pdf/2305.10300.pdf

Deeper Inquiries

How can the One-Prompt Segmentation approach be extended to handle dynamic or time-series medical imaging data?

The One-Prompt Segmentation approach can be extended to handle dynamic or time-series medical imaging data by incorporating temporal information into the model architecture. This can be achieved by modifying the encoder-decoder structure to account for sequential data input. For dynamic imaging data, such as videos or time-series scans, the model can be designed to process each frame or time step sequentially, capturing temporal dependencies and changes over time. This would involve adapting the prompt mechanism to provide temporal cues or prompts at different time points to guide the segmentation process. Additionally, recurrent neural networks (RNNs) or temporal convolutional networks (TCNs) can be integrated into the model to effectively capture temporal patterns and improve segmentation accuracy for dynamic medical imaging data.

What are the potential limitations of the current prompt types, and how could they be further improved to better suit specific clinical needs?

The current prompt types, including Click, BBox, Doodle, and SegLab, may have limitations in addressing specific clinical needs due to their inherent characteristics. Click prompts may not capture detailed structures, BBox prompts may struggle with irregular shapes, Doodle prompts may lack precision, and SegLab prompts may require additional preprocessing steps. To enhance these prompt types for specific clinical needs, several improvements can be considered:

Customizable Prompts: Introduce a feature that allows clinicians to customize prompts based on the specific characteristics of the target anatomy or pathology.
Multi-Modal Prompts: Incorporate multi-modal prompts that combine different types of prompts (e.g., combining BBox and SegLab) to provide comprehensive guidance for segmentation.
Interactive Prompt Refinement: Implement a mechanism for clinicians to interactively refine prompts during the segmentation process to improve accuracy and address challenging cases.
Prompt Quality Assessment: Integrate a prompt quality assessment module to evaluate the effectiveness of prompts and provide feedback to clinicians for improvement.
Semantic Segmentation Prompts: Develop prompts tailored for semantic segmentation tasks to capture fine-grained details and complex structures accurately.

Given the versatility of the One-Prompt Model, how could it be leveraged to support other medical image analysis tasks beyond segmentation, such as disease diagnosis or treatment planning?

The One-Prompt Model's versatility can be leveraged to support various medical image analysis tasks beyond segmentation by adapting the model architecture and prompt mechanisms to suit specific tasks. Here are some ways the One-Prompt Model can be extended for other medical image analysis tasks:

Classification Tasks: Modify the model to output class labels instead of segmentation masks, enabling it to perform tasks like disease classification or subtype identification based on the input image and prompt.
Localization Tasks: Enhance the model to predict the spatial location of specific anatomical landmarks or abnormalities within the image, aiding in tasks such as tumor localization or organ detection.
Registration Tasks: Integrate registration modules into the model to align images from different modalities or time points, facilitating tasks like image fusion or longitudinal analysis.
Quantification Tasks: Extend the model to quantify specific features or biomarkers within medical images, supporting tasks such as measuring tumor volume or assessing disease progression.
Treatment Planning: Incorporate decision-making modules into the model to recommend personalized treatment plans based on image analysis results, assisting clinicians in treatment decision-making processes.

By customizing the prompt types and model architecture for specific medical image analysis tasks, the One-Prompt Model can serve as a versatile tool for a wide range of clinical applications beyond segmentation.