toplogo
Sign In

Versatile Medical Image Segmentation Model Trained on Diverse Partially and Sparsely Labeled Datasets


Core Concepts
A cost-effective approach to train a versatile medical image segmentation model using multi-source datasets with partial or sparse annotations, leveraging model self-disambiguation, prior knowledge incorporation, and imbalance mitigation strategies.
Abstract
The content presents a novel weakly-supervised medical image segmentation approach that effectively utilizes multi-source partially and sparsely labeled data for training. The key highlights are: Motivation and Objective: Addresses the challenge of obtaining large, diverse, and fully annotated datasets for training versatile medical image segmentation models. Proposes a cost-effective alternative that harnesses multi-source data with partial or sparse segmentation labels. Methodology: Devises strategies for model self-disambiguation, prior knowledge incorporation, and imbalance mitigation to tackle challenges associated with inconsistently labeled multi-source data. Employs a hierarchical sampling technique to generate training examples from the multi-source multi-modality datasets. Adopts a 3D variant of TransUNet as the base network and integrates ambiguity-aware losses and a prior knowledge-based entropy minimization regularization term. Experiments and Results: Evaluates the proposed method on a multi-modal dataset of 2,960 scans from eight different sources for abdominal organ segmentation. Demonstrates the effectiveness and superior performance of the method compared to state-of-the-art alternative approaches, achieving an average Dice Similarity Coefficient (DSC) of 88.7%. Showcases the model's self-disambiguation capability and ability to handle both partially and sparsely labeled data, outperforming existing methods tailored for partially labeled data. Highlights the advantages of the proposed method in terms of efficiency, cost-saving features, and potential impact in the field.
Stats
The proposed method achieved an average Dice Similarity Coefficient (DSC) of 88.7% on the testing set, outperforming DoDNet (83.5%) and CLIP-driven (83.3%) models. The model trained with all 8 datasets achieved a DSC of 85.7% for individual anatomical structures, outperforming DoDNet and CLIP-driven by 5.7% and 5.0%, respectively. The model trained with only 20% of slices (evenly spaced) achieved an impressive average DSC ranging from 85.1% to 86.2%, outperforming baseline methods trained with all slices.
Quotes
"Our method exhibits impressive versatility and self-disambiguation capabilities, holding great promise for enhancing label efficiency and reducing the costs associated with model development, deployment, and maintenance." "Remarkably, our approach exhibits consistently superior performance across all datasets compared to state-of-the-art alternative methods."

Deeper Inquiries

How can the proposed method be extended to handle other types of weak supervision, such as image-level labels or bounding boxes, to further reduce annotation efforts

To extend the proposed method to handle other types of weak supervision, such as image-level labels or bounding boxes, the model architecture and loss functions can be adapted. For image-level labels, the model can be modified to incorporate global information during training. This can be achieved by adding a mechanism to aggregate information from the entire image to guide the segmentation process. For bounding boxes, the model can be adjusted to focus on the specific regions defined by the bounding boxes during training. By incorporating these modifications, the model can learn to segment based on different types of weak annotations, reducing the need for pixel-level annotations and further minimizing annotation efforts.

What are the potential limitations of the model self-disambiguation and prior knowledge incorporation strategies, and how can they be further improved

The model self-disambiguation and prior knowledge incorporation strategies may have limitations in scenarios where there is high variability or ambiguity in the data. In cases where there are overlapping structures or unclear boundaries, the model may struggle to accurately disambiguate between different classes. To improve these strategies, additional context-aware mechanisms can be introduced to provide more contextual information to the model. This can help the model make more informed decisions in complex scenarios. Furthermore, incorporating uncertainty estimation techniques can help the model assess its confidence in predictions and adjust accordingly. By enhancing the model with these capabilities, the limitations of self-disambiguation and prior knowledge incorporation can be mitigated.

Given the versatility of the proposed approach, how can it be adapted to address segmentation challenges in other medical imaging domains beyond abdominal structures

The proposed approach's versatility can be adapted to address segmentation challenges in other medical imaging domains by customizing the model architecture and training strategies to suit the specific characteristics of the new domain. For example, in neuroimaging, where structures may have intricate shapes and sizes, the model can be designed to handle fine details and variations in anatomy. Additionally, in cardiovascular imaging, where structures may exhibit complex spatial relationships, the model can be trained to capture these relationships effectively. By tailoring the approach to the unique requirements of different medical imaging domains, the model can be applied to a wide range of segmentation tasks beyond abdominal structures.
0