Multi-StyleGAN: Towards Image-Based Simulation of Time-Lapse Live-Cell Microscopy

Time-lapse fluorescent microscopy (TLFM) combined with predictive mathematical modelling is a powerful tool to study the inherently dynamic processes of life on the single-cell level. Such experiments are costly, complex and labour intensive. A complimentary approach and a step towards in silico experimentation, is to synthesise the imagery itself. Here, we propose Multi-StyleGAN as a descriptive approach to simulate time-lapse fluorescence microscopy imagery of living cells, based on a past experiment. This novel generative adversarial network synthesises a multi-domain sequence of consecutive timesteps. We showcase Multi-StyleGAN on imagery of multiple live yeast cells in microstructured environments and train on a dataset recorded in our laboratory. The simulation captures underlying biophysical factors and time dependencies, such as cell morphology, growth, physical interactions, as well as the intensity of a fluorescent reporter protein. An immediate application is to generate additional training and validation data for feature extraction algorithms or to aid and expedite development of advanced experimental techniques such as online monitoring or control of cells. Code and dataset is available at https://git.rwth-aachen.de/bcs/projects/tp/multi-stylegan.Comment: revised -- accepted to MICCAI 2021. (Tim Prangemeier and Christoph Reich --- both authors contributed equally

Deep learning a boon for biophotonics

This review covers original articles using deep learning in the biophotonic field published in the last years. In these years deep learning, which is a subset of machine learning mostly based on artificial neural network geometries, was applied to a number of biophotonic tasks and has achieved state-of-the-art performances. Therefore, deep learning in the biophotonic field is rapidly growing and it will be utilized in the next years to obtain real-time biophotonic decision-making systems and to analyze biophotonic data in general. In this contribution, we discuss the possibilities of deep learning in the biophotonic field including image classification, segmentation, registration, pseudostaining and resolution enhancement. Additionally, we discuss the potential use of deep learning for spectroscopic data including spectral data preprocessing and spectral classification. We conclude this review by addressing the potential applications and challenges of using deep learning for biophotonic data. © 2020 The Authors. Journal of Biophotonics published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinhei

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

Generative adversarial network: An overview of theory and applications

Abstract In recent times, image segmentation has been involving everywhere including disease diagnosis to autonomous vehicle driving. In computer vision, this image segmentation is one of the vital works and it is relatively complicated than other vision undertakings as it needs low-level spatial data. Especially, Deep Learning has impacted the field of segmentation incredibly and gave us today different successful models. The deep learning associated Generated Adversarial Networks (GAN) has presenting remarkable outcomes on image segmentation. In this study, the authors have presented a systematic review analysis on recent publications of GAN models and their applications. Three libraries such as Embase (Scopus), WoS, and PubMed have been considered for searching the relevant papers available in this area. Search outcomes have identified 2084 documents, after two-phase screening 52 potential records are included for final review. The following applications of GAN have been emerged: 3D object generation, medicine, pandemics, image processing, face detection, texture transfer, and traffic controlling. Before 2016, research in this field was limited and thereafter its practical usage came into existence worldwide. The present study also envisions the challenges associated with GAN and paves the path for future research in this realm

Deep Semantic Segmentation of Natural and Medical Images: A Review

The semantic image segmentation task consists of classifying each pixel of an image into an instance, where each instance corresponds to a class. This task is a part of the concept of scene understanding or better explaining the global context of an image. In the medical image analysis domain, image segmentation can be used for image-guided interventions, radiotherapy, or improved radiological diagnostics. In this review, we categorize the leading deep learning-based medical and non-medical image segmentation solutions into six main groups of deep architectural, data synthesis-based, loss function-based, sequenced models, weakly supervised, and multi-task methods and provide a comprehensive review of the contributions in each of these groups. Further, for each group, we analyze each variant of these groups and discuss the limitations of the current approaches and present potential future research directions for semantic image segmentation.Comment: 45 pages, 16 figures. Accepted for publication in Springer Artificial Intelligence Revie

The Effectiveness of Transfer Learning Systems on Medical Images

Deep neural networks have revolutionized the performances of many machine learning tasks such as medical image classification and segmentation. Current deep learning (DL) algorithms, specifically convolutional neural networks are increasingly becoming the methodological choice for most medical image analysis. However, training these deep neural networks requires high computational resources and very large amounts of labeled data which is often expensive and laborious. Meanwhile, recent studies have shown the transfer learning (TL) paradigm as an attractive choice in providing promising solutions to challenges of shortage in the availability of labeled medical images. Accordingly, TL enables us to leverage the knowledge learned from related data to solve a new problem. The objective of this dissertation is to examine the effectiveness of TL systems on medical images. First, a comprehensive systematic literature review was performed to provide an up-to-date status of TL systems on medical images. Specifically, we proposed a novel conceptual framework to organize the review. Second, a novel DL network was pretrained on natural images and utilized to evaluate the effectiveness of TL on a very large medical image dataset, specifically Chest X-rays images. Lastly, domain adaptation using an autoencoder was evaluated on the medical image dataset and the results confirmed the effectiveness of TL through fine-tuning strategies. We make several contributions to TL systems on medical image analysis: Firstly, we present a novel survey of TL on medical images and propose a new conceptual framework to organize the findings. Secondly, we propose a novel DL architecture to improve learned representations of medical images while mitigating the problem of vanishing gradients. Additionally, we identified the optimal cut-off layer (OCL) that provided the best model performance. We found that the higher layers in the proposed deep model give a better feature representation of our medical image task. Finally, we analyzed the effect of domain adaptation by fine-tuning an autoencoder on our medical images and provide theoretical contributions on the application of the transductive TL approach. The contributions herein reveal several research gaps to motivate future research and contribute to the body of literature in this active research area of TL systems on medical image analysis

Data augmentation using generative adversarial network for gastrointestinal parasite microscopy image classification

Las enfermedades parasitarias gastrointestinales representan un problema latente en los países en desarrollo; es necesario crear herramientas de apoyo para el diagnóstico médico de estas enfermedades, se requiere automatizar tareas como la clasificación de muestras de los parásitos causantes obtenidas a través del microscopio utilizando métodos como el aprendizaje profundo. Sin embargo, estos métodos requieren grandes cantidades de datos. Actualmente, la recolección de estas imágenes representa un procedimiento complejo, importante consumo de recursos y largos períodos. Por tanto, es necesario proponer una solución computacional a este problema. En este trabajo se presenta un enfoque para generar conjuntos de imágenes sintéticas de 8 especies de parásitos, utilizando Redes Generativas Adversarias Convolucionales Profundas (DCGAN). Además, buscando mejores resultados, se aplicaron técnicas de mejora de imagen. Estos conjuntos de datos sintéticos (SD) fueron evaluados en una serie de combinaciones con los conjuntos de datos reales (RD) utilizando la tarea de clasificación, donde la mayor exactitud se obtuvo con el modelo Resnet50 pre-entrenado (99,2%), mostrando que el aumento de la RD con SD obtenido de DCGAN ayuda a lograr una mayor exactitud

Modeling and Analysis of Subcellular Protein Localization in Hyper-Dimensional Fluorescent Microscopy Images Using Deep Learning Methods

Hyper-dimensional images are informative and become increasingly common in biomedical research. However, the machine learning methods of studying and processing the hyper-dimensional images are underdeveloped. Most of the methods only model the mapping functions between input and output by focusing on the spatial relationship, whereas neglect the temporal and causal relationships. In many cases, the spatial, temporal, and causal relationships are correlated and become a relationship complex. Therefore, only modeling the spatial relationship may result in inaccurate mapping function modeling and lead to undesired output. Despite the importance, there are multiple challenges on modeling the relationship complex, including the model complexity and the data availability. The objective of this dissertation is to comprehensively study the mapping function modeling of the spatial-temporal and the spatial-temporal-causal relationship in hyper-dimensional data with deep learning approaches. The modeling methods are expected to accurately capture the complex relationships in class-level and object-level so that new image processing tools can be developed based on the methods to study the relationships between targets in hyper-dimensional data. In this dissertation, four different cases of relationship complex are studied, including the class-level spatial-temporal-causal relationship and spatial-temporal relationship modeling, and the object-level spatial-temporal-causal relationship and spatial-temporal relationship modeling. The modelings are achieved by deep learning networks that implicitly model the mapping functions with network weight matrix. For spatial-temporal relationship, because the cause factor information is unavailable, discriminative modeling that only relies on available information is studied. For class-level and object-level spatial-temporal-causal relationship, generative modeling is studied with a new deep learning network and three new tools proposed. For spatial-temporal relationship modeling, a state-of-the-art segmentation network has been found to be the best performer over 18 networks. Based on accurate segmentation, we study the object-level temporal dynamics and interactions through dynamics tracking. The multi-object portion tracking (MOPT) method allows object tracking in subcellular level and identifies object events, including object born, dead, split, and fusion. The tracking results is 2.96% higher on consistent tracking accuracy and 35.48% higher on event identification accuracy, compared with the existing state-of-the-art tracking methods. For spatial-temporal-causal relationship modeling, the proposed four-dimensional reslicing generative adversarial network (4DR-GAN) captures the complex relationships between the input and the target proteins. The experimental results on four groups of proteins demonstrate the efficacy of 4DR-GAN compared with the widely used Pix2Pix network. On protein localization prediction (PLP), the predicted localization from 4DR-GAN is more accurate in subcellular localization, temporal consistency, and dynamics. Based on efficient PLP, the digital activation (DA) and digital inactivation (DI) tools allow precise spatial and temporal control on global and local localization manipulation. They allow researchers to study the protein functions and causal relationships by observing the digital manipulation and PLP output response