Solutions to Limited Annotation Problems of Deep Learning in Medical Image Segmentation

Image segmentation holds broad applications in medical image analysis, providing crucial support to doctors in both automatic diagnosis and computer-assisted interventions. The heterogeneity observed across various medical image datasets necessitates the training of task-specific segmentation models. However, effectively supervising the training of deep learning segmentation models typically demands dense label masks, a requirement that becomes challenging due to the constraints posed by privacy and cost issues in collecting large-scale medical datasets. These challenges collectively give rise to the limited annotations problems in medical image segmentation. In this dissertation, we address the challenges posed by annotation deficiencies through a comprehensive exploration of various strategies. Firstly, we employ self-supervised learning to extract information from unlabeled data, presenting a tailored self-supervised method designed specifically for convolutional neural networks and 3D Vision Transformers. Secondly, our attention shifts to domain adaptation problems, leveraging images with similar content but in different modalities. We introduce the use of contrastive loss as a shape constraint in our image translation framework, resulting in both improved performance and enhanced training robustness. Thirdly, we incorporate diffusion models for data augmentation, expanding datasets with generated image-label pairs. Lastly, we explore to extract segmentation masks from image-level annotations alone. We propose a multi-task training framework for ECG abnormal beats localization and a conditional diffusion-based algorithm for tumor detection.