A Novel Application of Image-to-Image Translation: Chromosome Straightening Framework by Learning from a Single Image

In medical imaging, chromosome straightening plays a significant role in the pathological study of chromosomes and in the development of cytogenetic maps. Whereas different approaches exist for the straightening task, typically geometric algorithms are used whose outputs are characterized by jagged edges or fragments with discontinued banding patterns. To address the flaws in the geometric algorithms, we propose a novel framework based on image-to-image translation to learn a pertinent mapping dependence for synthesizing straightened chromosomes with uninterrupted banding patterns and preserved details. In addition, to avoid the pitfall of deficient input chromosomes, we construct an augmented dataset using only one single curved chromosome image for training models. Based on this framework, we apply two popular image-to-image translation architectures, U-shape networks and conditional generative adversarial networks, to assess its efficacy. Experiments on a dataset comprised of 642 real-world chromosomes demonstrate the superiority of our framework, as compared to the geometric method in straightening performance, by rendering realistic and continued chromosome details. Furthermore, our straightened results improve the chromosome classification by 0.98%-1.39% mean accuracy.Comment: This work has been accepted by CISP-BMEI202

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

Selected Papers from Experimental Stress Analysis 2020

This Special Issue consists of selected papers from the Experimental Stress Analysis 2020 conference. Experimental Stress Analysis 2020 was organized with the support of the Czech Society for Mechanics, Expert Group of Experimental Mechanics, and was, for this particular year, held online in 19–22 October 2020. The objectives of the conference included identification of current situation, sharing professional experience and knowledge, discussing new theoretical and practical findings, and the establishment and strengthening of relationships between universities, companies, and scientists from the field of experimental mechanics in mechanical and civil engineering. The topics of the conference were focused on experimental research on materials and structures subjected to mechanical, thermal–mechanical, and dynamic loading, including damage, fatigue, and fracture analyses. The selected papers deal with top-level contemporary phenomena, such as modern durable materials, numerical modeling and simulations, and innovative non-destructive materials’ testing

Human Metaphase Chromosome Analysis using Image Processing

Development of an effective human metaphase chromosome analysis algorithm can optimize expert time usage by increasing the efficiency of many clinical diagnosis processes. Although many methods exist in the literature, they are only applicable for limited morphological variations and are specific to the staining method used during cell preparation. They are also highly influenced by irregular chromosome boundaries as well as the presence of artifacts such as premature sister chromatid separation. Therefore an algorithm is proposed in this research which can operate with any morphological variation of the chromosome across images from multiple staining methods. The proposed algorithm is capable of calculating the segmentation outline, the centerline (which gives the chromosome length), partitioning of the telomere regions and the centromere location of a given chromosome. The algorithm also detects and corrects for the sister chromatid separation artifact in metaphase cell images. A metric termed the Candidate Based Centromere Confidence (CBCC) is proposed to accompany each centromere detection result of the proposed method, giving an indication of the confidence the algorithm has on a given localization. The proposed method was first tested for the ability of calculating an accurate width profile against a centerline based method [1] using 226 chromosomes. A statistical analysis of the centromere detection error values proved that the proposed method can accurately locate centromere locations with statistical significance. Furthermore, the proposed method performed more consistently across different staining methods in comparison to the centerline based approach. When tested with a larger data set of 1400 chromosomes collected from a set of DAPI (4\u27,6-diamidino-2-phenylindole) and Giemsa stained cell images, the proposed candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%

Towards automated detection of dicentric chromosomes in metaphase images

Thesis (MSc)--Stellenbosch University, 2021.ENGLISH ABSTRACT: The aim of the proposed research is to investigate methods to identify objects of interest and classify dicentric and normal chromosomes in metaphase images using suitable digital image processing techniques. Dicentric chromosomes are abnormal chromosomes with two centromeres (instead of one) created by a variety of processes, including irradiation. When a chromosome is exposed to radiation, two chromosome segments, each with a centromere may join together resulting in a dicentric chromosome. An acentric fragment, i.e. a partial chromosome with no centromere, is also formed. The first stage of the proposed system is geared towards the detection of objects of interest, i.e. isolated normal and isolated dicentric chromosomes, as well as acentric fragments and clusters of overlapping chromosomes. The last stage of the proposed system is geared towards the classification of isolated chromosomes as either normal or dicentric. The proposed system automatically detects objects of interest not associated with dirt. The classification of the aforementioned objects into isolated and clustered chromosomes, as well as acentric fragments, is conducted manually, while the automation of this stage is reserved for future work. The proposed system subsequently automatically categorises isolated chromosomes as either normal or dicentric. It is demonstrated that the system correctly detects and classifies a significant number of the aforementioned chromosomes within metaphase images provided by iThemba LABS.AFRIKAANSE OPSOMMING: Die doel van die voorgestelde navorsing is om metodes te ondersoek wat voorwerpe van belang in metafase-beelde identifiseer en disentriese en normale chromosome met behulp van geskikte beeldverwerkingstegnieke klassifiseer. Disentriese chromosome is abnormale chromosome met twee sentromere (in plaas van een) wat deur verskeie prosesse, insluitende bestraling, geskep word. Wanneer ’n chromosoom aan bestraling blootgestel word, kan twee chromosoomsegmente, elk met ’n sentromeer, saamgevoeg word wat ’n disentriese chromosoom tot gevolg het. ’n Asentriese fragment, dit wil sˆe ’n gedeeltelike chromosoom sonder ’n sentromeer, word ook gevorm. Die eerste fase van die voorgestelde stelsel is op die opsporing van voorwerpe van belang gerig, dit wil sˆe ge¨ısoleerde normale en ge¨ısoleerde disentriese chromosome, sowel as asentriese fragmente en groepe van oorvleulende chromosome. Die laaste fase van die voorgestelde stelsel is op die klassifikasie van ge¨ısoleerde chromosome as normaal of disentries gerig. Die voorgestelde stelsel bespeur outomaties voorwerpe van belang wat nie met vuilheid verband hou nie. Die klassifikasie van bogenoemde voorwerpe as ge¨ısoleerde en gegroepeerde chromosome, asook asentriese fragmente, word met die hand gedoen, terwyl die outomatisering van hierdie fase vir toekomstige werk gereserveer is. Die voorgestelde stelsel kategoriseer vervolgens ge¨ısoleerde chromosome outomaties as normaal of disentries. Daar word aangetoon dat die stelsel ’n beduidende aantal van bogenoemde chromosome korrek opspoor en klassifiseer binne die metafase-beelde wat deur iThemba LABS verskaf is.Master

Bending forces plastically deform growing bacterial cell walls

Cell walls define a cell shape in bacteria. They are rigid to resist large internal pressures, but remarkably plastic to adapt to a wide range of external forces and geometric constraints. Currently, it is unknown how bacteria maintain their shape. In this work, we develop experimental and theoretical approaches and show that mechanical stresses regulate bacterial cell-wall growth. By applying a precisely controllable hydrodynamic force to growing rod-shaped Escherichia coli and Bacillus subtilis cells, we demonstrate that the cells can exhibit two fundamentally different modes of deformation. The cells behave like elastic rods when subjected to transient forces, but deform plastically when significant cell wall synthesis occurs while the force is applied. The deformed cells always recover their shape. The experimental results are in quantitative agreement with the predictions of the theory of dislocation-mediated growth. In particular, we find that a single dimensionless parameter, which depends on a combination of independently measured physical properties of the cell, can describe the cell's responses under various experimental conditions. These findings provide insight into how living cells robustly maintain their shape under varying physical environments

The chirality of the mitotic spindle provides a mechanical response to forces and depends on microtubule motors and augmin

Forces produced by motor proteins and microtubule dynamics within the mitotic spindle are crucial for proper chromosome segregation. In addition to linear forces, rotational forces or torques are present in the spindle, which are reflected in the left-handed twisted shapes of microtubule bundles that make the spindle chiral. However, the biological role and molecular origins of spindle chirality are unknown. By developing methods for measuring the spindle twist, we show that spindles are most chiral near the metaphase-to-anaphase transition. To assess the role of chirality in spindle mechanics, we compressed the spindles along their axis. This resulted in a stronger left-handed twist, suggesting that the twisted shape allows for a mechanical response to forces. Inhibition or depletion of motor proteins that perform chiral stepping, Eg5/kinesin-5, Kif18A/kinesin-8, MKLP1/kinesin-6, and dynein, decreased the left-handed twist or led to right- handed twist, implying that these motors regulate the twist by rotating microtubules within their antiparallel overlaps or at the spindle pole. A right-handed twist was also observed after the depletion of the microtubule nucleator augmin, indicating its contribution to the twist through the nucleation of antiparallel bridging microtubules. The uncovered switch from left- handed to right-handed twist reveals the existence of competing mechanisms that promote twisting in opposite directions. As round spindles are more twisted than the elongated ones are, we infer that bending and twisting moments are generated by similar molecular mechanisms and propose a physiological role for spindle chirality in allowing the spindle to absorb mechanical load

Digital image analysis of mitotic chromosomes

Změny v počtu a ve struktuře chromozomů jsou příčinou řady vážných onemocnění. K odhalení chromozomálních změn slouží cytogenetická vyšetření, která nejčastěji vedou k sestavení karyotypu. Pro účely cytogenetických analýz se chromozomy vizualizují pomocí vhodných metod a nejčastěji se následně sestavují do karyotypu. Protože ruční stanovení karyotypu je časově i finančně náročné, vyvíjí se přístupy k automatickému karyotypování pomocí počítačového softwaru. Automatické karyotypovací systémy klasifikují chromozomy do tříd na základě identifikačních znaků, specifických pro každý chromozom. Automatickou klasifikaci však nejvíce limituje přítomnost překrývající se a silně ohnutých chromozomů, přítomných v téměř každé mitóze. Přesnost a spolehlivost karyotypovacích systémů stále závisí na zásahu uživatele. Cílem vývoje nových přístupů k automatickému karyotypování je tedy zejména překonání výše zmíněných problémů a dále vývoj takových klasifikačních metod, které umožňí klasifikaci chromozomů do párů bez lidské kontroly.Changes in chromosome number and structure may cause serious diseases. Cytogenetic tests leadin to set of karyotype are done for detecting these abnormalities. Chromosomes are visualised with proper methods and karyotype is made up most often. Manual karyotyping is time-consuming and expensive task. Because of this, researchers have been developing automated karyotyping systems. Karyotyping systems classify chromosomes into classes based on their characteristic features. Overlapping and bent chromosomes are limitations for automatic classification since they ocur at almost every mitosis. Accuracy and reliability of karyotyping systems still depend on the human intervention. Overcoming of these problems and development of fully automated system is the aim of modern approaches.