Deep Q-Network-Driven Catheter Segmentation in 3D US by Hybrid Constrained Semi-Supervised Learning and Dual-UNet

Catheter segmentation in 3D ultrasound is important for computer-assisted cardiac intervention. However, a large amount of labeled images are required to train a successful deep convolutional neural network (CNN) to segment the catheter, which is expensive and time-consuming. In this paper, we propose a novel catheter segmentation approach, which requests fewer annotations than the supervised learning method, but nevertheless achieves better performance. Our scheme considers a deep Q learning as the pre-localization step, which avoids voxel-level annotation and which can efficiently localize the target catheter. With the detected catheter, patch-based Dual-UNet is applied to segment the catheter in 3D volumetric data. To train the Dual-UNet with limited labeled images and leverage information of unlabeled images, we propose a novel semi-supervised scheme, which exploits unlabeled images based on hybrid constraints from predictions. Experiments show the proposed scheme achieves a higher performance than state-of-the-art semi-supervised methods, while it demonstrates that our method is able to learn from large-scale unlabeled images.Comment: Accepted by MICCAI 202

Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation

This paper presents a simple yet effective two-stage framework for semi-supervised medical image segmentation. Our key insight is to explore the feature representation learning with labeled and unlabeled (i.e., pseudo labeled) images to enhance the segmentation performance. In the first stage, we present an aleatoric uncertainty-aware method, namely AUA, to improve the segmentation performance for generating high-quality pseudo labels. Considering the inherent ambiguity of medical images, AUA adaptively regularizes the consistency on images with low ambiguity. To enhance the representation learning, we propose a stage-adaptive contrastive learning method, including a boundary-aware contrastive loss to regularize the labeled images in the first stage and a prototype-aware contrastive loss to optimize both labeled and pseudo labeled images in the second stage. The boundary-aware contrastive loss only optimizes pixels around the segmentation boundaries to reduce the computational cost. The prototype-aware contrastive loss fully leverages both labeled images and pseudo labeled images by building a centroid for each class to reduce computational cost for pair-wise comparison. Our method achieves the best results on two public medical image segmentation benchmarks. Notably, our method outperforms the prior state-of-the-art by 5.7% on Dice for colon tumor segmentation relying on just 5% labeled images.Comment: On submission to TM

Medical Image Segmentation with Deep Convolutional Neural Networks

Medical imaging is the technique and process of creating visual representations of the body of a patient for clinical analysis and medical intervention. Healthcare professionals rely heavily on medical images and image documentation for proper diagnosis and treatment. However, manual interpretation and analysis of medical images are time-consuming, and inaccurate when the interpreter is not well-trained. Fully automatic segmentation of the region of interest from medical images has been researched for years to enhance the efficiency and accuracy of understanding such images. With the advance of deep learning, various neural network models have gained great success in semantic segmentation and sparked research interests in medical image segmentation using deep learning. We propose three convolutional frameworks to segment tissues from different types of medical images. Comprehensive experiments and analyses are conducted on various segmentation neural networks to demonstrate the effectiveness of our methods. Furthermore, datasets built for training our networks and full implementations are published

Exploring probabilistic models for semi-supervised learning

Deep neural networks are increasingly harnessed for computer vision tasks, thanks to their robust performance. However, their training demands large-scale labeled datasets, which are labor-intensive to prepare. Semi-supervised learning (SSL) offers a solution by learning from a mix of labeled and unlabeled data. While most state-of-the-art SSL methods follow a deterministic approach, the exploration of their probabilistic counterparts remains limited. This research area is important because probabilistic models can provide uncertainty estimates critical for real-world applications. For instance, SSL-trained models may fall short of those trained with supervised learning due to potential pseudo-label errors in unlabeled data, and these models are more likely to make wrong predictions in practice. Especially in critical sectors like medical image analysis and autonomous driving, decision-makers must understand the model’s limitations and when incorrect predictions may occur, insights often provided by uncertainty estimates. Furthermore, uncertainty can also serve as a criterion for filtering out unreliable pseudo-labels when unlabeled samples are used for training, potentially improving deep model performance. This thesis furthers the exploration of probabilistic models for SSL. Drawing on the widely-used Bayesian approximation tool, Monte Carlo (MC) dropout, I propose a new probabilistic framework, the Generative Bayesian Deep Learning (GBDL) architecture, for semi-supervised medical image segmentation. This approach not only mitigates potential overfitting found in previous methods but also achieves superior results across four evaluation metrics. Unlike its empirically designed predecessors, GBDL is underpinned by a full Bayesian formulation, providing a theoretical probabilistic foundation. Acknowledging MC dropout’s limitations, I introduce NP-Match, a novel proba- bilistic approach for large-scale semi-supervised image classification. We evaluated NP-Match’s generalization capabilities through extensive experiments in different challenging settings such as standard, imbalanced, and multi-label semi-supervised image classification. According to the experimental results, NP-Match not only competes favorably with previous state-of-the-art methods but also estimates uncertainty more rapidly than MC-dropout-based models, thus enhancing both training and testing efficiency. Lastly, I propose NP-SemiSeg, a new probabilistic model for semi-supervised se- mantic segmentation. This flexible model can be integrated with various existing segmentation frameworks to make predictions and estimate uncertainty. Experiments indicate that NP-SemiSeg surpasses MC dropout in accuracy, uncertainty quantification, and speed