Guidelines for cerebrovascular segmentation: Managing imperfect annotations in the context of semi-supervised...

Area of Science:

• Medical Imaging
• Computer Vision
• Machine Learning

Background:

• Medical image segmentation is crucial but requires extensive labeled data, which is costly and time-consuming to acquire.
• Cerebrovascular segmentation is particularly challenging due to inherent annotation ambiguities and expert subjectivity.
• Existing deep learning models struggle with imperfect and limited datasets.

Purpose of the Study:

• To investigate the impact of data quantity and quality on deep learning-based cerebrovascular segmentation.
• To compare the performance of various semi-supervised learning methods in scenarios with imperfect annotations.
• To provide practical guidelines for improving annotation and training strategies for cerebrovascular segmentation models.

Main Methods:

• Evaluation of state-of-the-art semi-supervised learning techniques utilizing unsupervised regularization.
• Comparative analysis of model performance across diverse data scenarios, varying in quantity and quality.
• Assessment of deep learning model dependency on data characteristics in medical image segmentation.

Main Results:

• Semi-supervised methods demonstrate effectiveness in cerebrovascular segmentation, even with limited or inconsistent annotations.
• Data quality significantly influences model performance, highlighting the need for consistent annotation guidelines.
• Specific semi-supervised approaches show varying degrees of robustness to data imperfections.

Conclusions:

• Guidelines are proposed to enhance the annotation process and dataset uniformity for cerebrovascular segmentation.
• Optimizing data annotation and leveraging semi-supervised learning are key to robust deep learning models in medical imaging.
• The study underscores the critical role of data dependency in the success of deep learning for complex segmentation tasks.