Automatic recognition of different types of acute leukaemia using peripheral blood cell images

[eng] Clinical pathologists have learned to identify morphological qualitative features to characterise the different normal cells, as well as the abnormal cell types whose presence in peripheral blood is the evidence of serious haematological diseases. A drawback of visual morphological analysis is that is time consuming, requires well-trained personnel and is prone to intra-observer variability, which is particularly true when dealing with blast cells. Indeed, subtle interclass morphological differences exist for leukaemia types, which turns into low specificity scores in the routine screening. They are well-known the difficulties that clinical pathologists have in the discrimination among different blasts and the subjectivity associated with their morphological recognition. The general objective of this thesis is the automatic recognition of different types of blast cells circulating in peripheral blood in acute leukaemia using digital image processing and machine learning techniques. In order to accomplish this objective, this thesis starts with a discrimination among normal mononuclear cells, reactive lymphocytes and three types of leukemic cells using traditional machine learning techniques and hand-crafted features obtained from cell segmentation. In the second part of the thesis, a new predictive system designed with two serially connected convolutional neural networks is developed for the diagnosis of acute leukaemia. This system was proved to distinguish neoplastic (leukaemia) and non-neoplastic (infections) diseases, as well as recognise the leukaemia lineage. Furthermore, it was evaluated for its integration in a real-clinical setting. This thesis also contributes in advancing the state of the art of the automatic recognition of acute leukaemia by providing a more realistic approach which reflects the real-life complexity of acute leukaemia diagnosis

Down Syndrome detection with Swin Transformer architecture

Objective: Down Syndrome, also known as Trisomy 21, is a severe genetic disease caused by an extra chromosome 21. For the detection of Trisomy 21, despite those statistical methods have been widely used for screening, karyotyping remains the gold standard and the first level of testing for diagnosis. Due to karyotyping being a time-consuming and labour-intensive procedure, Computer Vision methodologies have been explored to automate the karyotyping process for decades. However, few studies have focused on Down Syndrome detection with the Transformer technique. This study develops a Down-Syndrome-Detector (DSD) architecture based on the Transformer structure, which includes a segmentation module, an alignment module, a classification module, and a Down Syndrome indicator. Methods: The segmentation and classification modules are designed by homogeneous transfer learning at the model level. Transfer learning techniques enable a network to share weights learned from the source domain (e.g., millions of data in ImageNet) and optimize the weights with limited labeled data in the target domain (e.g., less than 6,000 images in BioImLab). The Align-Module is designed to process the segmentation output to fit the classification dataset, and the Down Syndrome Indicator identifies a Down Syndrome case from the classification output. Results: Experiments are first performed on two public datasets BioImLab (119 cases) and Advanced Digital Imaging Research (ADIR, 180 cases). Our performance metrics indicate the good ability of segmentation and classification modules of DSD. Then, the DS detection performance of DSD is evaluated on a private dataset consisting of 1084 cells (including 20 DS cells from 2 singleton cases): 90.0% and 86.1% for cell-level TPR and TNR; 100% and 96.08% for case-level TPR and TNR, respectively. Conclusion: This study develops a pipeline based on the modern Transformer architecture for the detection of Down Syndrome from original metaphase micrographs. Both segmentation and classification models developed in this study are assessed using public datasets with commonly used metrics, and both achieved good results. The DSDproposed in this study reported satisfactory singleton case-specific DS detection results. Significance: As verified by a medical specialist, the developed method may improve Down Syndrome detection efficiency by saving human labor and improving clinical practice

Advanced Representation Learning for Dense Prediction Tasks in Medical Image Analysis

Machine learning is a rapidly growing field of artificial intelligence that allows computers to learn and make predictions using human labels. However, traditional machine learning methods have many drawbacks, such as being time-consuming, inefficient, task-specific biased, and requiring a large amount of domain knowledge. A subfield of machine learning, representation learning, focuses on learning meaningful and useful features or representations from input data. It aims to automatically learn relevant features from raw data, saving time, increasing efficiency and generalization, and reducing reliance on expert knowledge. Recently, deep learning has further accelerated the development of representation learning. It leverages deep architectures to extract complex and abstract representations, resulting in significant outperformance in many areas. In the field of computer vision, deep learning has made remarkable progress, particularly in high-level and real-world computer vision tasks. Since deep learning methods do not require handcrafted features and have the ability to understand complex visual information, they facilitate researchers to design automated systems that make accurate diagnoses and interpretations, especially in the field of medical image analysis. Deep learning has achieved state-of-the-art performance in many medical image analysis tasks, such as medical image regression/classification, generation and segmentation tasks. Compared to regression/classification tasks, medical image generation and segmentation tasks are more complex dense prediction tasks that understand semantic representations and generate pixel-level predictions. This thesis focuses on designing representation learning methods to improve the performance of dense prediction tasks in the field of medical image analysis. With advances in imaging technology, more complex medical images become available for use in this field. In contrast to traditional machine learning algorithms, current deep learning-based representation learning methods provide an end-to-end approach to automatically extract representations without the need for manual feature engineering from the complex data. In the field of medical image analysis, there are three unique challenges requiring the design of advanced representation learning architectures, \ie, limited labeled medical images, overfitting with limited data, and lack of interpretability. To address these challenges, we aim to design robust representation learning architectures for the two main directions of dense prediction tasks, namely medical image generation and segmentation. For medical image generation, the specific topic that we focus on is chromosome straightening. This task involves generating a straightened chromosome image from a curved chromosome input. In addition, the challenges of this task include insufficient training images and corresponding ground truth, as well as the non-rigid nature of chromosomes, leading to distorted details and shapes after straightening. We first propose a study for the chromosome straightening task. We introduce a novel framework using image-to-image translation and demonstrate its efficacy and robustness in generating straightened chromosomes. The framework addresses the challenges of limited training data and outperforms existing studies. We then present a subsequent study to address the limitations of our previous framework, resulting in new state-of-the-art performance and better interpretability and generalization capability. We propose a new robust chromosome straightening framework, named Vit-Patch GAN, which instead learns the motion representation of chromosomes for straightening while retaining more details of shape and banding patterns. For medical image segmentation, we focus on the fovea localization task, which is transferred from localization to small region segmentation. Accurate segmentation of the fovea region is crucial for monitoring and analyzing retinal diseases to prevent irreversible vision loss. This task also requires the incorporation of global features to effectively identify the fovea region and overcome hard cases associated with retinal diseases and non-standard fovea locations. We first propose a novel two-branch architecture, Bilateral-ViT, for fovea localization in retina image segmentation. This vision-transformer-based architecture incorporates global image context and blood vessel structure. It surpasses existing methods and achieves state-of-the-art results on two public datasets. We then propose a subsequent method to further improve the performance of fovea localization. We design a novel dual-stream deep learning architecture called Bilateral-Fuser. In contrast to our previous Bilateral-ViT, Bilateral-Fuser globally incorporates long-range connections from multiple cues, including fundus and vessel distribution. Moreover, with the newly designed Bilateral Token Incorporation module, Bilateral-Fuser learns anatomical-aware tokens, significantly reducing computational costs while achieving new state-of-the-art performance. Our comprehensive experiments also demonstrate that Bilateral-Fuser achieves better accuracy and robustness on both normal and diseased retina images, with excellent generalization capability

Molecular Genetic and DNA Methylation Profiling of Chronic Lymphocytic Leukaemia: a Focus on Divergent Prognostic Subgroups and Subsets

Advancements in prognostication have improved the subdivision of chronic lymphocytic leukaemia (CLL) into diverse prognostic subgroups. In CLL, IGHV unmutated and IGHV3-21 genes are associated with a poor-prognosis, conversely, IGHV mutated genes with a favourable outcome. The finding of multiple CLL subsets expressing ‘stereotyped’ B-cell receptors (BCRs) has suggested a role for antigen(s) in leukemogenesis. Patients belonging to certain stereotyped subsets share clinical and biological characteristics, yet limited knowledge exists regarding the genetic and epigenetic events that may influence their clinical behaviour. This thesis aimed to, further investigate Swedish IGHV3-21-utilising patients, screen for genetic and DNA methylation events in CLL subgroups/subsets and study DNA methylation over time and within different CLL compartments. In paper I, IGHV gene sequencing of 337 CLL patients from a Swedish population-based cohort revealed a lower (6.5%) IGHV3-21 frequency relative to previous Swedish hospital-based studies (10.1-12.7%). Interestingly, this frequency remained higher compared to other Western CLL (2.6-4.1%) hospital-based cohorts. Furthermore, we confirmed the poor-outcome for IGHV3-21 patients to be independent of mutational and stereotypy status. In paper II, genomic events in stereotyped IGHV3-21-subset #2, IGHV4-34- subset #4 and subset #16 and their non-stereotyped counterparts were investigated via SNP arrays (n=101). Subset #2 and non-subset #2 carried a higher frequency of V events compared to subset #4. A high frequency of del(11q) was evident in IGHV3- 21 patients particularly subset #2 cases, which may partially explain their poorprognosis. In contrast, the lower prevalence of aberrations and absence of poorprognostic alterations may reflect the inherent low-proliferative disease seen in subset #4 cases. In papers III and IV, differential methylation profiles in IGHV mutated and IGHV unmutated patients were identified using DNA-methylation microarrays. CLL prognostic genes (CLLU1, LPL), tumor-suppressor genes (TSGs) (ABI3, WISP3) and genes belonging to TGF-ß and NFkB/ TNFR1 pathways were differentially methylated between the subgroups. Additionally, the re-expression of methylated TSGs by use of methyl and deacetyl inhibitors was demonstrated. Interestingly, analysis of patient-paired diagnostic/follow-up samples and patient-matched lymph node (LN) and peripheral blood (PB) cases revealed global DNA methylation to be relatively stable over time and remarkably similar within the different compartments. Altogether, this thesis provides insight into the aberrant genomic and DNA methylation events in divergent CLL subgroups. Moreover this thesis helps distinguish the extent to which DNA methylation changes with respect to time and microenvironment in CLL

Molecular Genetic and DNA Methylation Profiling of Chronic Lymphocytic Leukaemia: a Focus on Divergent Prognostic Subgroups and Subsets

Advancements in prognostication have improved the subdivision of chronic lymphocytic leukaemia (CLL) into diverse prognostic subgroups. In CLL, IGHV unmutated and IGHV3-21 genes are associated with a poor-prognosis, conversely, IGHV mutated genes with a favourable outcome. The finding of multiple CLL subsets expressing ‘stereotyped’ B-cell receptors (BCRs) has suggested a role for antigen(s) in leukemogenesis. Patients belonging to certain stereotyped subsets share clinical and biological characteristics, yet limited knowledge exists regarding the genetic and epigenetic events that may influence their clinical behaviour. This thesis aimed to, further investigate Swedish IGHV3-21-utilising patients, screen for genetic and DNA methylation events in CLL subgroups/subsets and study DNA methylation over time and within different CLL compartments. In paper I, IGHV gene sequencing of 337 CLL patients from a Swedish population-based cohort revealed a lower (6.5%) IGHV3-21 frequency relative to previous Swedish hospital-based studies (10.1-12.7%). Interestingly, this frequency remained higher compared to other Western CLL (2.6-4.1%) hospital-based cohorts. Furthermore, we confirmed the poor-outcome for IGHV3-21 patients to be independent of mutational and stereotypy status. In paper II, genomic events in stereotyped IGHV3-21-subset #2, IGHV4-34- subset #4 and subset #16 and their non-stereotyped counterparts were investigated via SNP arrays (n=101). Subset #2 and non-subset #2 carried a higher frequency of V events compared to subset #4. A high frequency of del(11q) was evident in IGHV3- 21 patients particularly subset #2 cases, which may partially explain their poorprognosis. In contrast, the lower prevalence of aberrations and absence of poorprognostic alterations may reflect the inherent low-proliferative disease seen in subset #4 cases. In papers III and IV, differential methylation profiles in IGHV mutated and IGHV unmutated patients were identified using DNA-methylation microarrays. CLL prognostic genes (CLLU1, LPL), tumor-suppressor genes (TSGs) (ABI3, WISP3) and genes belonging to TGF-ß and NFkB/ TNFR1 pathways were differentially methylated between the subgroups. Additionally, the re-expression of methylated TSGs by use of methyl and deacetyl inhibitors was demonstrated. Interestingly, analysis of patient-paired diagnostic/follow-up samples and patient-matched lymph node (LN) and peripheral blood (PB) cases revealed global DNA methylation to be relatively stable over time and remarkably similar within the different compartments. Altogether, this thesis provides insight into the aberrant genomic and DNA methylation events in divergent CLL subgroups. Moreover this thesis helps distinguish the extent to which DNA methylation changes with respect to time and microenvironment in CLL

Advanced Computational Methods for Oncological Image Analysis

[Cancer is the second most common cause of death worldwide and encompasses highly variable clinical and biological scenarios. Some of the current clinical challenges are (i) early diagnosis of the disease and (ii) precision medicine, which allows for treatments targeted to specific clinical cases. The ultimate goal is to optimize the clinical workflow by combining accurate diagnosis with the most suitable therapies. Toward this, large-scale machine learning research can define associations among clinical, imaging, and multi-omics studies, making it possible to provide reliable diagnostic and prognostic biomarkers for precision oncology. Such reliable computer-assisted methods (i.e., artificial intelligence) together with clinicians’ unique knowledge can be used to properly handle typical issues in evaluation/quantification procedures (i.e., operator dependence and time-consuming tasks). These technical advances can significantly improve result repeatability in disease diagnosis and guide toward appropriate cancer care. Indeed, the need to apply machine learning and computational intelligence techniques has steadily increased to effectively perform image processing operations—such as segmentation, co-registration, classification, and dimensionality reduction—and multi-omics data integration.

Urological Cancer 2020

This Urological Cancer 2020 collection contains a set of multidisciplinary contributions to the extraordinary heterogeneity of tumor mechanisms, diagnostic approaches, and therapies of the renal, urinary tract, and prostate cancers, with the intention of offering to interested readers a representative snapshot of the status of urological research

From nanometers to centimeters: Imaging across spatial scales with smart computer-aided microscopy

Microscopes have been an invaluable tool throughout the history of the life sciences, as they allow researchers to observe the miniscule details of living systems in space and time. However, modern biology studies complex and non-obvious phenotypes and their distributions in populations and thus requires that microscopes evolve from visual aids for anecdotal observation into instruments for objective and quantitative measurements. To this end, many cutting-edge developments in microscopy are fuelled by innovations in the computational processing of the generated images. Computational tools can be applied in the early stages of an experiment, where they allow for reconstruction of images with higher resolution and contrast or more colors compared to raw data. In the final analysis stage, state-of-the-art image analysis pipelines seek to extract interpretable and humanly tractable information from the high-dimensional space of images. In the work presented in this thesis, I performed super-resolution microscopy and wrote image analysis pipelines to derive quantitative information about multiple biological processes. I contributed to studies on the regulation of DNMT1 by implementing machine learning-based segmentation of replication sites in images and performed quantitative statistical analysis of the recruitment of multiple DNMT1 mutants. To study the spatiotemporal distribution of DNA damage response I performed STED microscopy and could provide a lower bound on the size of the elementary spatial units of DNA repair. In this project, I also wrote image analysis pipelines and performed statistical analysis to show a decoupling of DNA density and heterochromatin marks during repair. More on the experimental side, I helped in the establishment of a protocol for many-fold color multiplexing by iterative labelling of diverse structures via DNA hybridization. Turning from small scale details to the distribution of phenotypes in a population, I wrote a reusable pipeline for fitting models of cell cycle stage distribution and inhibition curves to high-throughput measurements to quickly quantify the effects of innovative antiproliferative antibody-drug-conjugates. The main focus of the thesis is BigStitcher, a tool for the management and alignment of terabyte-sized image datasets. Such enormous datasets are nowadays generated routinely with light-sheet microscopy and sample preparation techniques such as clearing or expansion. Their sheer size, high dimensionality and unique optical properties poses a serious bottleneck for researchers and requires specialized processing tools, as the images often do not fit into the main memory of most computers. BigStitcher primarily allows for fast registration of such many-dimensional datasets on conventional hardware using optimized multi-resolution alignment algorithms. The software can also correct a variety of aberrations such as fixed-pattern noise, chromatic shifts and even complex sample-induced distortions. A defining feature of BigStitcher, as well as the various image analysis scripts developed in this work is their interactivity. A central goal was to leverage the user's expertise at key moments and bring innovations from the big data world to the lab with its smaller and much more diverse datasets without replacing scientists with automated black-box pipelines. To this end, BigStitcher was implemented as a user-friendly plug-in for the open source image processing platform Fiji and provides the users with a nearly instantaneous preview of the aligned images and opportunities for manual control of all processing steps. With its powerful features and ease-of-use, BigStitcher paves the way to the routine application of light-sheet microscopy and other methods producing equally large datasets

Construction and characterisation of models for X-linked severe combined immunodeficiency for targeted gene correction by zinc finger nucleases.

X-linked Severe Combined Immunodeficiency (SCID-X1) is an immunopathy caused by a mutation of the common gamma chain (γc) gene, IL2RG, which results in a lack of T cells, NK cells and with dysfunctional B cells. Current gene therapy methods involve the addition of a correct γc gene via integrating viral vectors. However, these current non-targeting gene addition strategies can result in transformation of the cell. A novel solution to this problem is met by targeted gene correction via homologous recombination stimulated by a site specific cleavage event caused by zinc finger nucleases (ZFN) within the disease gene. A γc deficient mouse has been created by replacing the murine il2rg locus with a mutated human IL2RG containing a point mutation frequently seen in SCID-X1 patients. The mutant human IL2RG is transcribed and initial analysis of this new SCID-X1 model has revealed a phenotype mirroring γc gene knockout mice. Lineage negative bone marrow cells from these mice, transduced with integrating lentiviral vector encoding functional IL2RG can reconstitute the immune cells in the Rag2-/-γc-/- double knockout SCID mouse model. Therefore the humanised mouse model of SCID-X1 can be corrected and is an appropriate platform to assess the efficiency of various gene targeting and correction strategies for the human mutation including ZFN induced homologous recombination. We have successfully achieved targeted homologous recombination in both a human T cell SCID-X1 cell line model and the humanised mouse embryonic stem cells with IL2RG specific ZFN

Meiosis

Meiosis, the process of forming gametes in preparation for sexual reproduction, has long been a focus of intense study. Meiosis has been studied at the cytological, genetic, molecular and cellular levels. Studies in model systems have revealed common underlying mechanisms while in parallel, studies in diverse organisms have revealed the incredible variation in meiotic mechanisms. This book brings together many of the diverse strands of investigation into this fascinating and challenging field of biology