Generalizable deep learning based medical image segmentation

Deep learning is revolutionizing medical image analysis and interpretation. However, its real-world deployment is often hindered by the poor generalization to unseen domains (new imaging modalities and protocols). This lack of generalization ability is further exacerbated by the scarcity of labeled datasets for training: Data collection and annotation can be prohibitively expensive in terms of labor and costs because label quality heavily dependents on the expertise of radiologists. Additionally, unreliable predictions caused by poor model generalization pose safety risks to clinical downstream applications. To mitigate labeling requirements, we investigate and develop a series of techniques to strengthen the generalization ability and the data efficiency of deep medical image computing models. We further improve model accountability and identify unreliable predictions made on out-of-domain data, by designing probability calibration techniques. In the first and the second part of thesis, we discuss two types of problems for handling unexpected domains: unsupervised domain adaptation and single-source domain generalization. For domain adaptation we present a data-efficient technique that adapts a segmentation model trained on a labeled source domain (e.g., MRI) to an unlabeled target domain (e.g., CT), using a small number of unlabeled training images from the target domain. For domain generalization, we focus on both image reconstruction and segmentation. For image reconstruction, we design a simple and effective domain generalization technique for cross-domain MRI reconstruction, by reusing image representations learned from natural image datasets. For image segmentation, we perform causal analysis of the challenging cross-domain image segmentation problem. Guided by this causal analysis we propose an effective data-augmentation-based generalization technique for single-source domains. The proposed method outperforms existing approaches on a large variety of cross-domain image segmentation scenarios. In the third part of the thesis, we present a novel self-supervised method for learning generic image representations that can be used to analyze unexpected objects of interest. The proposed method is designed together with a novel few-shot image segmentation framework that can segment unseen objects of interest by taking only a few labeled examples as references. Superior flexibility over conventional fully-supervised models is demonstrated by our few-shot framework: it does not require any fine-tuning on novel objects of interest. We further build a publicly available comprehensive evaluation environment for few-shot medical image segmentation. In the fourth part of the thesis, we present a novel probability calibration model. To ensure safety in clinical settings, a deep model is expected to be able to alert human radiologists if it has low confidence, especially when confronted with out-of-domain data. To this end we present a plug-and-play model to calibrate prediction probabilities on out-of-domain data. It aligns the prediction probability in line with the actual accuracy on the test data. We evaluate our method on both artifact-corrupted images and images from an unforeseen MRI scanning protocol. Our method demonstrates improved calibration accuracy compared with the state-of-the-art method. Finally, we summarize the major contributions and limitations of our works. We also suggest future research directions that will benefit from the works in this thesis.Open Acces

Segmenting the left ventricle in 3D using a coupled ASM and a learned non-rigid spatial model

This paper presents a new approach to higher dimensional segmentation. We present an extended Active Shape Model (ASM) formulation for the segmentation of multi-contour anatomical structures. We employ coupling and weighting schemes to improve the robustness of ASM segmentation. 3D segmentation is achieved through propagation of a 2D ASM using a learned non-rigid spatial model. This approach does not suffer from the training and aligning difficulties faced by direct 3D model-based methods used today. Experimental results are encouraging at this early stage, and future directions of research are provided

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research

A novel model-based 3D+time left ventricular segmentation technique

A common approach to model-based segmentation is to assume a top-down modelling strategy. However, this is not feasible for complex 3D+time structures such as the cardiac left ventricle due to increased training requirements, aligning difficulties and local minima in resulting models. As our main contribution, we present an alternate bottom-up modelling approach. By combining the variation captured in multiple dimensionally-targeted models at segmentation-time we create a scalable segmentation framework that does not suffer from the ’curse of dimensionality’. Our second contribution involves a flexible contour coupling technique that allows our segmentation method to adapt to unseen contour configurations outside the training set. This is used to identify the endo- and epi-cardium contours of the left ventricle by coupling them at segmentationtime, instead of at model-time. We apply our approach to 33 3D+time MRI cardiac datasets and perform comprehensive evaluation against several state-of-the-art works. Quantitative evaluation illustrates that our method requires significantly less training than state-of-the-art model-based methods, while maintaining or improving segmentation accuracy

Domain Generalization for Medical Image Analysis: A Survey

Medical Image Analysis (MedIA) has become an essential tool in medicine and healthcare, aiding in disease diagnosis, prognosis, and treatment planning, and recent successes in deep learning (DL) have made significant contributions to its advances. However, DL models for MedIA remain challenging to deploy in real-world situations, failing for generalization under the distributional gap between training and testing samples, known as a distribution shift problem. Researchers have dedicated their efforts to developing various DL methods to adapt and perform robustly on unknown and out-of-distribution data distributions. This paper comprehensively reviews domain generalization studies specifically tailored for MedIA. We provide a holistic view of how domain generalization techniques interact within the broader MedIA system, going beyond methodologies to consider the operational implications on the entire MedIA workflow. Specifically, we categorize domain generalization methods into data-level, feature-level, model-level, and analysis-level methods. We show how those methods can be used in various stages of the MedIA workflow with DL equipped from data acquisition to model prediction and analysis. Furthermore, we include benchmark datasets and applications used to evaluate these approaches and analyze the strengths and weaknesses of various methods, unveiling future research opportunities

Segmentation of the right ventricle in MRI images using a dual active shape model

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/166226/1/ipr2bf01366.pd

Combining Shape and Learning for Medical Image Analysis

Automatic methods with the ability to make accurate, fast and robust assessments of medical images are highly requested in medical research and clinical care. Excellent automatic algorithms are characterized by speed, allowing for scalability, and an accuracy comparable to an expert radiologist. They should produce morphologically and physiologically plausible results while generalizing well to unseen and rare anatomies. Still, there are few, if any, applications where today\u27s automatic methods succeed to meet these requirements.\ua0The focus of this thesis is two tasks essential for enabling automatic medical image assessment, medical image segmentation and medical image registration. Medical image registration, i.e. aligning two separate medical images, is used as an important sub-routine in many image analysis tools as well as in image fusion, disease progress tracking and population statistics. Medical image segmentation, i.e. delineating anatomically or physiologically meaningful boundaries, is used for both diagnostic and visualization purposes in a wide range of applications, e.g. in computer-aided diagnosis and surgery.The thesis comprises five papers addressing medical image registration and/or segmentation for a diverse set of applications and modalities, i.e. pericardium segmentation in cardiac CTA, brain region parcellation in MRI, multi-organ segmentation in CT, heart ventricle segmentation in cardiac ultrasound and tau PET registration. The five papers propose competitive registration and segmentation methods enabled by machine learning techniques, e.g. random decision forests and convolutional neural networks, as well as by shape modelling, e.g. multi-atlas segmentation and conditional random fields