Computational Methods for Segmentation of Multi-Modal Multi-Dimensional Cardiac Images

Segmentation of the heart structures helps compute the cardiac contractile function quantified via the systolic and diastolic volumes, ejection fraction, and myocardial mass, representing a reliable diagnostic value. Similarly, quantification of the myocardial mechanics throughout the cardiac cycle, analysis of the activation patterns in the heart via electrocardiography (ECG) signals, serve as good cardiac diagnosis indicators. Furthermore, high quality anatomical models of the heart can be used in planning and guidance of minimally invasive interventions under the assistance of image guidance. The most crucial step for the above mentioned applications is to segment the ventricles and myocardium from the acquired cardiac image data. Although the manual delineation of the heart structures is deemed as the gold-standard approach, it requires significant time and effort, and is highly susceptible to inter- and intra-observer variability. These limitations suggest a need for fast, robust, and accurate semi- or fully-automatic segmentation algorithms. However, the complex motion and anatomy of the heart, indistinct borders due to blood flow, the presence of trabeculations, intensity inhomogeneity, and various other imaging artifacts, makes the segmentation task challenging. In this work, we present and evaluate segmentation algorithms for multi-modal, multi-dimensional cardiac image datasets. Firstly, we segment the left ventricle (LV) blood-pool from a tri-plane 2D+time trans-esophageal (TEE) ultrasound acquisition using local phase based filtering and graph-cut technique, propagate the segmentation throughout the cardiac cycle using non-rigid registration-based motion extraction, and reconstruct the 3D LV geometry. Secondly, we segment the LV blood-pool and myocardium from an open-source 4D cardiac cine Magnetic Resonance Imaging (MRI) dataset by incorporating average atlas based shape constraint into the graph-cut framework and iterative segmentation refinement. The developed fast and robust framework is further extended to perform right ventricle (RV) blood-pool segmentation from a different open-source 4D cardiac cine MRI dataset. Next, we employ convolutional neural network based multi-task learning framework to segment the myocardium and regress its area, simultaneously, and show that segmentation based computation of the myocardial area is significantly better than that regressed directly from the network, while also being more interpretable. Finally, we impose a weak shape constraint via multi-task learning framework in a fully convolutional network and show improved segmentation performance for LV, RV and myocardium across healthy and pathological cases, as well as, in the challenging apical and basal slices in two open-source 4D cardiac cine MRI datasets. We demonstrate the accuracy and robustness of the proposed segmentation methods by comparing the obtained results against the provided gold-standard manual segmentations, as well as with other competing segmentation methods

Medical Image Segmentation Review: The success of U-Net

Automatic medical image segmentation is a crucial topic in the medical domain and successively a critical counterpart in the computer-aided diagnosis paradigm. U-Net is the most widespread image segmentation architecture due to its flexibility, optimized modular design, and success in all medical image modalities. Over the years, the U-Net model achieved tremendous attention from academic and industrial researchers. Several extensions of this network have been proposed to address the scale and complexity created by medical tasks. Addressing the deficiency of the naive U-Net model is the foremost step for vendors to utilize the proper U-Net variant model for their business. Having a compendium of different variants in one place makes it easier for builders to identify the relevant research. Also, for ML researchers it will help them understand the challenges of the biological tasks that challenge the model. To address this, we discuss the practical aspects of the U-Net model and suggest a taxonomy to categorize each network variant. Moreover, to measure the performance of these strategies in a clinical application, we propose fair evaluations of some unique and famous designs on well-known datasets. We provide a comprehensive implementation library with trained models for future research. In addition, for ease of future studies, we created an online list of U-Net papers with their possible official implementation. All information is gathered in https://github.com/NITR098/Awesome-U-Net repository.Comment: Submitted to the IEEE Transactions on Pattern Analysis and Machine Intelligence Journa

Reasoning with Uncertainty in Deep Learning for Safer Medical Image Computing

Deep learning is now ubiquitous in the research field of medical image computing. As such technologies progress towards clinical translation, the question of safety becomes critical. Once deployed, machine learning systems unavoidably face situations where the correct decision or prediction is ambiguous. However, the current methods disproportionately rely on deterministic algorithms, lacking a mechanism to represent and manipulate uncertainty. In safety-critical applications such as medical imaging, reasoning under uncertainty is crucial for developing a reliable decision making system. Probabilistic machine learning provides a natural framework to quantify the degree of uncertainty over different variables of interest, be it the prediction, the model parameters and structures, or the underlying data (images and labels). Probability distributions are used to represent all the uncertain unobserved quantities in a model and how they relate to the data, and probability theory is used as a language to compute and manipulate these distributions. In this thesis, we explore probabilistic modelling as a framework to integrate uncertainty information into deep learning models, and demonstrate its utility in various high-dimensional medical imaging applications. In the process, we make several fundamental enhancements to current methods. We categorise our contributions into three groups according to the types of uncertainties being modelled: (i) predictive; (ii) structural and (iii) human uncertainty. Firstly, we discuss the importance of quantifying predictive uncertainty and understanding its sources for developing a risk-averse and transparent medical image enhancement application. We demonstrate how a measure of predictive uncertainty can be used as a proxy for the predictive accuracy in the absence of ground-truths. Furthermore, assuming the structure of the model is flexible enough for the task, we introduce a way to decompose the predictive uncertainty into its orthogonal sources i.e. aleatoric and parameter uncertainty. We show the potential utility of such decoupling in providing a quantitative “explanations” into the model performance. Secondly, we introduce our recent attempts at learning model structures directly from data. One work proposes a method based on variational inference to learn a posterior distribution over connectivity structures within a neural network architecture for multi-task learning, and share some preliminary results in the MR-only radiotherapy planning application. Another work explores how the training algorithm of decision trees could be extended to grow the architecture of a neural network to adapt to the given availability of data and the complexity of the task. Lastly, we develop methods to model the “measurement noise” (e.g., biases and skill levels) of human annotators, and integrate this information into the learning process of the neural network classifier. In particular, we show that explicitly modelling the uncertainty involved in the annotation process not only leads to an improvement in robustness to label noise, but also yields useful insights into the patterns of errors that characterise individual experts

Blurry Boundary Delineation and Adversarial Confidence Learning for Medical Image Analysis

Low tissue contrast and fuzzy boundaries are major challenges in medical image segmentation which is a key step for various medical image analysis tasks. In particular, blurry boundary delineation is one of the most challenging problems due to low-contrast and even vanishing boundaries. Currently, encoder-decoder networks are widely adopted for medical image segmentation. With the lateral skip connection, the models can obtain and fuse both semantic and resolution information in deep layers to achieve more accurate segmentation performance. However, in many applications (e.g., images with blurry boundaries), these models often cannot precisely locate complex boundaries and segment tiny isolated parts. To solve this challenging problem, we empirically analyze why simple lateral connections in encoder-decoder architectures are not able to accurately locate indistinct boundaries. Based on the analysis, we argue learning high-resolution semantic information in the lateral connection can better delineate the blurry boundaries. Two methods have been proposed to achieve such a goal. a) A high-resolution pathway composed of dilated residual blocks has been adopted to replace the simple lateral connection for learning the high-resolution semantic features. b) A semantic-guided encoder feature learning strategy is further proposed to learn high-resolution semantic encoder features so that we can more accurately and efficiently locate the blurry boundaries. Besides, we also explore a contour constraint mechanism to model blurry boundary detection. Experimental results on real clinical datasets (infant brain MRI and pelvic organ datasets) show that our proposed methods can achieve state-of-the-art segmentation accuracy, especially for the blurry regions. Further analysis also indicates that our proposed network components indeed contribute to the performance gain. Experiments on an extra dataset also validate the generalization ability of our proposed methods. Generative adversarial networks (GANs) are widely used in medical image analysis tasks, such as medical image segmentation and synthesis. In these works, adversarial learning is usually directly applied to the original supervised segmentation (synthesis) networks. The use of adversarial learning is effective in improving visual perception performance since adversarial learning works as realistic regularization for supervised generators. However, the quantitative performance often cannot be improved as much as the qualitative performance, and it can even become worse in some cases. In this dissertation, I explore how adversarial learning could be more useful in supervised segmentation (synthesis) models, i.e., how to synchronously improve visual and quantitative performance. I first analyze the roles of discriminator in the classic GANs and compare them with those in supervised adversarial systems. Based on this analysis, an adversarial confidence learning framework is proposed for taking better advantage of adversarial learning; that is, besides the adversarial learning for emphasizing visual perception, the confidence information provided by the adversarial network is utilized to enhance the design of the supervised segmentation (synthesis) network. In particular, I propose using a fully convolutional adversarial network for confidence learning to provide voxel-wise and region-wise confidence information for the segmentation (synthesis) network. Furthermore, various loss functions of GANs are investigated and the binary cross entropy loss is finally chosen to train the proposed adversarial confidence learning system so that the modeling capacity of the discriminator is retained for confidence learning. With these settings, two machine learning algorithms are proposed to solve some specific medical image analysis problems. a) A difficulty-aware attention mechanism is proposed to properly handle hard samples or regions by taking structural information into consideration so that the irregular distribution of medical data could be appropriately dealt with. Experimental results on clinical and challenge datasets show that the proposed algorithm can achieve state-of-the-art segmentation (synthesis) accuracy. Further analysis also indicates that adversarial confidence learning can synchronously improve the visual perception and quantitative performance. b) A semisupervised segmentation model is proposed to alleviate the everlasting challenge for medical image segmentation - lack of annotated data. The proposed method can automatically recognize well-segmented regions (instead of the entire sample) and dynamically include them to increase the label set during training. Specifically, based on the confidence map, a region-attention based semi-supervised learning strategy is designed to further train the segmentation network. Experimental results on real clinical datasets show that the proposed approach can achieve better segmentation performance with extra unannotated data.Doctor of Philosoph