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Customized Expert-Adaptive Medical Image Segmentation with Limited Training Data


Core Concepts
A customized expert-adaptive medical image segmentation method leverages multi-expert annotation, multi-task deep neural network-based model training, and lightweight model fine-tuning to efficiently adapt the segmentation model to a new expert with limited annotated data.
Abstract
The paper presents a customized expert-adaptive medical image segmentation method to address the challenge of expert-specific annotation variations in medical image segmentation tasks. The key components of the method are: Multi-expert annotation: Multiple medical experts annotate each training image, resulting in a dataset with diverse expert-specific annotations. Multi-task deep neural network-based model training: A multi-task deep neural network model is trained on the multi-expert annotated dataset, where each expert corresponds to a specific branch in the model. Lightweight model fine-tuning: When a new expert wants to use the trained model, the shared layers of the model are kept, and the expert-specific layers are fine-tuned using a small number of annotated samples from the new expert. The authors evaluate the customized expert-adaptive method on brain MRI segmentation tasks with limited training data. The experiments demonstrate the effectiveness of the method in adapting the segmentation model to a new expert, especially when the amount of annotated data from the new expert is limited. The results also show the impact of the number of experts involved in the multi-expert annotation on the model's adaptivity.
Stats
The segmentation performance of a model trained on data annotated by one expert and tested on a new expert can degrade by 0.77%-6.08% in Dice score, 0.08-0.59 in ASSD, and 0.3-2.01 in 95HD compared to the model trained and tested on the same expert. When fine-tuning the multi-expert trained model with 20 annotated samples from the new expert, the performance is comparable to the best baseline performance obtained using the whole dataset. Involving the annotations of more than 3 experts in the training stage yields consistently better adaptation performance than the single-expert counterpart.
Quotes
"Annotations in MIS are highly expert-specific. On one hand, medical images contain complex features, e.g., blurry regions, transition zones, and partial volume effects, which pose a great challenge to creating annotations. On the other hand, medical experts may have varying expertise and skills for reading and annotating a medical image and may also target distinct post-segmentation tasks." "An intuitive solution is to re-train the model using medical images with annotations created by the new expert. However, obtaining sufficient annotated images from a new expert to effectively re-train the model to cater for this expert is seldom feasible in practice."

Key Insights Distilled From

by Binyan Hu,A.... at arxiv.org 05-02-2024

https://arxiv.org/pdf/2402.07330.pdf
Expert-Adaptive Medical Image Segmentation

Deeper Inquiries

How can the expert-adaptive method be further improved to achieve satisfactory performance with even fewer annotated samples from the new expert?

To enhance the expert-adaptive method's performance with fewer annotated samples from the new expert, several strategies can be considered: Data Augmentation Techniques: Implementing advanced data augmentation methods can help in artificially increasing the size of the annotated dataset. Techniques like geometric transformations, intensity variations, and elastic deformations can generate additional training samples, reducing the reliance on a large number of annotated images. Transfer Learning: Leveraging pre-trained models on related tasks or datasets can aid in initializing the model with knowledge learned from a broader context. Fine-tuning the pre-trained model with the limited annotated samples from the new expert can lead to improved performance with fewer annotations. Active Learning: Implementing active learning strategies can intelligently select the most informative samples for annotation, optimizing the model's performance with minimal annotations. Techniques like uncertainty sampling or query-by-committee can help in selecting the most valuable samples for annotation. Semi-Supervised Learning: Incorporating semi-supervised learning techniques can utilize both annotated and unannotated data to train the model. By effectively leveraging the unlabeled data, the model can improve its performance with a limited number of annotated samples. Regularization Techniques: Applying regularization methods like dropout, weight decay, or batch normalization can prevent overfitting and enhance the model's generalization ability, especially in scenarios with limited annotated data. By integrating these strategies into the expert-adaptive method, the model can adapt more effectively to new experts with even fewer annotated samples, ultimately improving its performance and generalizability.

How can the insights from this work on expert-specific annotation variations be applied to other medical image analysis tasks beyond segmentation?

The insights gained from the study on expert-specific annotation variations in medical image segmentation can be extrapolated to other medical image analysis tasks to enhance their performance and adaptability. Some ways to apply these insights include: Classification Tasks: In tasks like disease classification or organ identification, understanding the variations in annotations by different experts can help in developing models that are robust to diverse interpretations. By incorporating multi-expert annotations and expert-adaptive techniques, classification models can be trained to accommodate different expert perspectives, improving their accuracy and reliability. Object Detection: For tasks involving object detection in medical images, such as identifying tumors or abnormalities, considering expert-specific variations in annotations can lead to more accurate and consistent detection. By training object detection models with multi-expert annotations and fine-tuning them with annotations from new experts, the models can adapt better to different annotation styles and improve their detection capabilities. Image Registration: In tasks like image registration where aligning multiple images is crucial, understanding how different experts annotate corresponding features can enhance the registration accuracy. By incorporating insights from expert-specific variations, registration models can be designed to handle diverse annotations and improve the alignment of medical images for better analysis and diagnosis. Quality Assurance: Insights from expert-specific annotation variations can also be applied to quality assurance tasks in medical image analysis. By identifying discrepancies in annotations by different experts, quality assurance models can be developed to flag potential errors or inconsistencies in annotations, ensuring the reliability and accuracy of the analysis results. By applying the lessons learned from expert-specific annotation variations in medical image segmentation to other medical image analysis tasks, researchers and practitioners can enhance the robustness, accuracy, and adaptability of models across a wide range of applications in the medical imaging domain.
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