Deep Domain Adaptation Learning Framework for Associating Image Features to Tumour Gene Profile

While medical imaging and general pathology are routine in cancer diagnosis, genetic sequencing is not always assessable due to the strong phenotypic and genetic heterogeneity of human cancers. Image-genomics integrates medical imaging and genetics to provide a complementary approach to optimise cancer diagnosis by associating tumour imaging traits with clinical data and has demonstrated its potential in identifying imaging surrogates for tumour biomarkers. However, existing image-genomics research has focused on quantifying tumour visual traits according to human understanding, which may not be optimal across different cancer types. The challenge hence lies in the extraction of optimised imaging representations in an objective data-driven manner. Such an approach requires large volumes of annotated image data that are difficult to acquire. We propose a deep domain adaptation learning framework for associating image features to tumour genetic information, exploiting the ability of domain adaptation technique to learn relevant image features from close knowledge domains. Our proposed framework leverages the current state-of-the-art in image object recognition to provide image features to encode subtle variations of tumour phenotypic characteristics with domain adaptation techniques. The proposed framework was evaluated with current state-of-the-art in: (i) tumour histopathology image classification and; (ii) image-genomics associations. The proposed framework demonstrated improved accuracy of tumour classification, as well as providing additional data-derived representations of tumour phenotypic characteristics that exhibit strong image-genomics association. This thesis advances and indicates the potential of image-genomics research to reveal additional imaging surrogates to genetic biomarkers, which has the potential to facilitate cancer diagnosis

Nucleus segmentation : towards automated solutions

Single nucleus segmentation is a frequent challenge of microscopy image processing, since it is the first step of many quantitative data analysis pipelines. The quality of tracking single cells, extracting features or classifying cellular phenotypes strongly depends on segmentation accuracy. Worldwide competitions have been held, aiming to improve segmentation, and recent years have definitely brought significant improvements: large annotated datasets are now freely available, several 2D segmentation strategies have been extended to 3D, and deep learning approaches have increased accuracy. However, even today, no generally accepted solution and benchmarking platform exist. We review the most recent single-cell segmentation tools, and provide an interactive method browser to select the most appropriate solution.Peer reviewe

Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

Deep learning for image-based liver analysis — A comprehensive review focusing on malignant lesions

Deep learning-based methods, in particular, convolutional neural networks and fully convolutional networks are now widely used in the medical image analysis domain. The scope of this review focuses on the analysis using deep learning of focal liver lesions, with a special interest in hepatocellular carcinoma and metastatic cancer; and structures like the parenchyma or the vascular system. Here, we address several neural network architectures used for analyzing the anatomical structures and lesions in the liver from various imaging modalities such as computed tomography, magnetic resonance imaging and ultrasound. Image analysis tasks like segmentation, object detection and classification for the liver, liver vessels and liver lesions are discussed. Based on the qualitative search, 91 papers were filtered out for the survey, including journal publications and conference proceedings. The papers reviewed in this work are grouped into eight categories based on the methodologies used. By comparing the evaluation metrics, hybrid models performed better for both the liver and the lesion segmentation tasks, ensemble classifiers performed better for the vessel segmentation tasks and combined approach performed better for both the lesion classification and detection tasks. The performance was measured based on the Dice score for the segmentation, and accuracy for the classification and detection tasks, which are the most commonly used metrics.publishedVersio

Deep Learning for Detection and Segmentation in High-Content Microscopy Images

High-content microscopy led to many advances in biology and medicine. This fast emerging technology is transforming cell biology into a big data driven science. Computer vision methods are used to automate the analysis of microscopy image data. In recent years, deep learning became popular and had major success in computer vision. Most of the available methods are developed to process natural images. Compared to natural images, microscopy images pose domain specific challenges such as small training datasets, clustered objects, and class imbalance. In this thesis, new deep learning methods for object detection and cell segmentation in microscopy images are introduced. For particle detection in fluorescence microscopy images, a deep learning method based on a domain-adapted Deconvolution Network is presented. In addition, a method for mitotic cell detection in heterogeneous histopathology images is proposed, which combines a deep residual network with Hough voting. The method is used for grading of whole-slide histology images of breast carcinoma. Moreover, a method for both particle detection and cell detection based on object centroids is introduced, which is trainable end-to-end. It comprises a novel Centroid Proposal Network, a layer for ensembling detection hypotheses over image scales and anchors, an anchor regularization scheme which favours prior anchors over regressed locations, and an improved algorithm for Non-Maximum Suppression. Furthermore, a novel loss function based on Normalized Mutual Information is proposed which can cope with strong class imbalance and is derived within a Bayesian framework. For cell segmentation, a deep neural network with increased receptive field to capture rich semantic information is introduced. Moreover, a deep neural network which combines both paradigms of multi-scale feature aggregation of Convolutional Neural Networks and iterative refinement of Recurrent Neural Networks is proposed. To increase the robustness of the training and improve segmentation, a novel focal loss function is presented. In addition, a framework for black-box hyperparameter optimization for biomedical image analysis pipelines is proposed. The framework has a modular architecture that separates hyperparameter sampling and hyperparameter optimization. A visualization of the loss function based on infimum projections is suggested to obtain further insights into the optimization problem. Also, a transfer learning approach is presented, which uses only one color channel for pre-training and performs fine-tuning on more color channels. Furthermore, an approach for unsupervised domain adaptation for histopathological slides is presented. Finally, Galaxy Image Analysis is presented, a platform for web-based microscopy image analysis. Galaxy Image Analysis workflows for cell segmentation in cell cultures, particle detection in mice brain tissue, and MALDI/H&E image registration have been developed. The proposed methods were applied to challenging synthetic as well as real microscopy image data from various microscopy modalities. It turned out that the proposed methods yield state-of-the-art or improved results. The methods were benchmarked in international image analysis challenges and used in various cooperation projects with biomedical researchers

A Comprehensive Overview of Computational Nuclei Segmentation Methods in Digital Pathology

In the cancer diagnosis pipeline, digital pathology plays an instrumental role in the identification, staging, and grading of malignant areas on biopsy tissue specimens. High resolution histology images are subject to high variance in appearance, sourcing either from the acquisition devices or the H\&E staining process. Nuclei segmentation is an important task, as it detects the nuclei cells over background tissue and gives rise to the topology, size, and count of nuclei which are determinant factors for cancer detection. Yet, it is a fairly time consuming task for pathologists, with reportedly high subjectivity. Computer Aided Diagnosis (CAD) tools empowered by modern Artificial Intelligence (AI) models enable the automation of nuclei segmentation. This can reduce the subjectivity in analysis and reading time. This paper provides an extensive review, beginning from earlier works use traditional image processing techniques and reaching up to modern approaches following the Deep Learning (DL) paradigm. Our review also focuses on the weak supervision aspect of the problem, motivated by the fact that annotated data is scarce. At the end, the advantages of different models and types of supervision are thoroughly discussed. Furthermore, we try to extrapolate and envision how future research lines will potentially be, so as to minimize the need for labeled data while maintaining high performance. Future methods should emphasize efficient and explainable models with a transparent underlying process so that physicians can trust their output.Comment: 47 pages, 27 figures, 9 table