UniverSeg: Universal Medical Image Segmentation (2304.06131v1)

Abstract: While deep learning models have become the predominant method for medical image segmentation, they are typically not capable of generalizing to unseen segmentation tasks involving new anatomies, image modalities, or labels. Given a new segmentation task, researchers generally have to train or fine-tune models, which is time-consuming and poses a substantial barrier for clinical researchers, who often lack the resources and expertise to train neural networks. We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training. Given a query image and example set of image-label pairs that define a new segmentation task, UniverSeg employs a new Cross-Block mechanism to produce accurate segmentation maps without the need for additional training. To achieve generalization to new tasks, we have gathered and standardized a collection of 53 open-access medical segmentation datasets with over 22,000 scans, which we refer to as MegaMedical. We used this collection to train UniverSeg on a diverse set of anatomies and imaging modalities. We demonstrate that UniverSeg substantially outperforms several related methods on unseen tasks, and thoroughly analyze and draw insights about important aspects of the proposed system. The UniverSeg source code and model weights are freely available at https://universeg.csail.mit.edu

• The paper introduces a universal deep learning model that segments medical images across diverse tasks without retraining.
• It employs a novel CrossBlock mechanism within an encoder-decoder architecture to transfer segmentation cues from support data to query images in a single pass.
• Experiments on 22,000 scans across 53 datasets show performance comparable to state-of-the-art networks, marking a major advance in domain adaptability.

UniverSeg: Universal Medical Image Segmentation

The paper introduces an innovative approach to medical image segmentation, termed UniverSeg, which is capable of performing segmentation tasks across different anatomies, image modalities, and label definitions without the need for retraining. This work addresses a critical limitation in the domain of deep learning-based medical image segmentation, where models typically require retraining to tackle new segmentation tasks, which can be resource-intensive and impractical for many clinical researchers.

Methodology

UniverSeg leverages a newly developed "CrossBlock" mechanism to facilitate the segmentation of unseen tasks. Given a query image and a support set of image-label pairs that define the segmentation task, the CrossBlock employs cross-convolutional operations to effectively transfer information from the support set to the query image. This enables the system to predict segmentation maps for new tasks in a single forward pass.

The CrossBlock operates within an encoder-decoder architecture, which processes image data at multiple scales, inspired by architectures like UNet but generalized to work universally across tasks. A unique feature is its ability to handle variable-sized support sets during both training and inference, enhancing its flexibility and practical usability.

Training UniverSeg involved assembling a diverse dataset termed MegaMedical, which consisted of 53 datasets spanning 26 biomedical domains and 16 image modalities, resulting in over 22,000 scans. The diversity of MegaMedical ensures that UniverSeg is exposed to a broad range of segmentation scenarios, which is critical for its generalization capacity.

Results

The paper presents extensive evaluations demonstrating that UniverSeg significantly outperforms existing few-shot segmentation methods and approaches the performance of fully supervised task-specific networks in various unseen tasks. For instance, UniverSeg achieved Dice scores comparable to state-of-the-art networks (nnUNet) for datasets like PanDental and WBC, exemplifying its potential to replace supervised methods in scenarios where model retraining is impractical.

Implications

UniverSeg marks a substantial shift in medical image analysis by providing a singular model that can generalize across diverse segmentation tasks. This capability could greatly expedite clinical research and applications by removing the bottleneck of task-specific model development and retraining. The implications of this approach extend beyond immediate performance metrics: by simplifying the segmentation workflow and reducing dependence on machine learning expertise among practitioners, UniverSeg can democratize AI tools in medical research.

Theoretically, this work contributes to the ongoing discourse on domain adaptability in machine learning models. It demonstrates that with sufficient training task diversity, a single model can effectively internalize a wide array of potential tasks, a result that could inspire future research in both medical imaging and other domains requiring model generalization across tasks.

Future Work

The authors suggest several avenues for future research, including expanding the dimensional scope to full 3D segmentation and incorporating multi-label tasks, which could further increase the model's applicability. Additionally, exploring more efficient architectures or mechanisms for task specification beyond current support set sampling could enhance both performance and efficiency.

In conclusion, the UniverSeg framework represents a notable advancement in medical image segmentation, offering a universal solution to a longstanding problem in the field. Its successful implementation and impressive performance on unseen tasks highlight its potential to revolutionize how segmentation tasks are approached in medical imaging, offering broader implications for AI deployment in medical contexts.