Training Diverse Medical Image Segmentation Models for Universal Performance

Grunnleggende konsepter

A single unified model can effectively handle diverse medical image segmentation tasks across various regions, anatomical structures, and imaging modalities by leveraging the synergy and commonality across tasks.

Sammendrag

The content discusses a shift towards a universal medical image segmentation paradigm, which aims to build a comprehensive, unified segmentation model that can handle diverse medical imaging tasks, in contrast to the prevailing practice of developing task-specific segmentation models.
The key highlights and insights are:

The prevailing paradigm of developing separate models for specific medical objects and image modalities is constrained by limited training data from the same domain, resulting in compromised model robustness and generalizability.

Inspired by the training program of medical radiology residents, the authors propose a universal medical image segmentation paradigm that seeks to harness the diversity and commonality of medical images to build a comprehensive, unified segmentation model.

The authors introduce Hermes, a novel context-prior learning approach that addresses the challenges of data heterogeneity and annotation differences in medical image segmentation. Hermes learns two important types of prior knowledge - task and modality - directly from medical images.

Through experiments on a large collection of eleven diverse datasets across five modalities and multiple body regions, the authors demonstrate the merit of the universal paradigm over the traditional paradigm. Hermes achieves state-of-the-art performance on all testing datasets and shows superior model scalability.

The authors further evaluate Hermes on downstream tasks, including transfer learning, incremental learning, and generalization, and find that Hermes exhibits strong performance, affirming the efficacy of the universal paradigm in acquiring robust and generalizable image representations.

The analysis of the learned priors by Hermes reveals that they can accurately capture the intricate relations among tasks and modalities, aligning with the established anatomical and imaging principles in radiology.

Statistikk

"A major focus of clinical imaging workflow is disease diagnosis and management, leading to medical imaging datasets strongly tied to specific clinical objectives."
"To date, the prevailing paradigm for medical image segmentation promotes the development of separate models for specific medical objects (e.g., organs or tumors) and image modalities (e.g., CT or MR)."
"Given these challenges, intriguing questions remain to be fully explored. How can segmentation tasks with different target ROIs mutually benefit from each other? Is the underlying feature representation transferable across different body regions? Despite the visual difference in imaging modalities, how can a model discern and utilize the meaningful commonalities between them?"

Sitater

"Inspired by radiology residency programs, we recognize that radiologists' expertise arises from routine exposure to wide-ranging images across body regions, diseases, and imaging modalities."
"Despite the fact that the human body exhibits anatomical variability, it is fundamentally composed of various types of tissues and structures whose appearance in images are often statistically similar."
"Hermes's learned priors demonstrate an appealing trait to reflect the intricate relations among tasks and modalities, which aligns with the established anatomical and imaging principles in radiology."

Viktige innsikter hentet fra

Training Like a Medical Resident

by Yunhe Gao,Zh... klokken arxiv.org 04-09-2024

https://arxiv.org/pdf/2306.02416.pdf

Dypere Spørsmål

How can the universal medical image segmentation paradigm be further extended to incorporate self-supervised or semi-supervised learning techniques to address the issue of missing labels?

Incorporating self-supervised or semi-supervised learning techniques into the universal medical image segmentation paradigm can help address the issue of missing labels by leveraging unlabeled data to improve model performance. One approach could be to use self-supervised learning to pretrain the model on a large amount of unlabeled data before fine-tuning on the labeled medical image datasets. This pretraining phase can help the model learn meaningful representations from the unlabeled data, which can then be transferred to the downstream segmentation tasks. Additionally, semi-supervised learning techniques can be employed to make use of both labeled and unlabeled data during training, allowing the model to learn from the available data more effectively.

What other types of medical prior knowledge, beyond task and modality, can be integrated into the model inference to potentially improve the performance of Hermes?

In addition to task and modality priors, other types of medical prior knowledge that can be integrated into the model inference to potentially improve the performance of Hermes include anatomical priors, spatial priors, and temporal priors. Anatomical priors can provide information about the expected shapes and structures of different anatomical regions, helping the model make more accurate segmentation predictions. Spatial priors can capture the spatial relationships between different anatomical structures, guiding the model in understanding the context of the image. Temporal priors can be useful for tasks involving dynamic medical imaging data, such as cine MRI, by incorporating information about how structures change over time.

Given the promising results of Hermes in medical image segmentation, how can the universal paradigm be applied to develop foundational models for broader medical imaging tasks beyond segmentation?

The universal paradigm demonstrated by Hermes in medical image segmentation can be applied to develop foundational models for broader medical imaging tasks beyond segmentation by expanding the scope of the training data and tasks. By incorporating diverse datasets covering a wide range of medical imaging modalities, body regions, and clinical targets, the model can learn comprehensive representations that generalize well across different imaging tasks. Additionally, the model can be trained on a variety of medical imaging tasks, such as classification, detection, and reconstruction, to create a foundational model that can address multiple aspects of medical image analysis. This approach can lead to the development of versatile models that can be applied to a wide range of medical imaging tasks, paving the way for advancements in various areas of medical image analysis.