228 research outputs found

    Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions

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    Breast cancer has reached the highest incidence rate worldwide among all malignancies since 2020. Breast imaging plays a significant role in early diagnosis and intervention to improve the outcome of breast cancer patients. In the past decade, deep learning has shown remarkable progress in breast cancer imaging analysis, holding great promise in interpreting the rich information and complex context of breast imaging modalities. Considering the rapid improvement in the deep learning technology and the increasing severity of breast cancer, it is critical to summarize past progress and identify future challenges to be addressed. In this paper, we provide an extensive survey of deep learning-based breast cancer imaging research, covering studies on mammogram, ultrasound, magnetic resonance imaging, and digital pathology images over the past decade. The major deep learning methods, publicly available datasets, and applications on imaging-based screening, diagnosis, treatment response prediction, and prognosis are described in detail. Drawn from the findings of this survey, we present a comprehensive discussion of the challenges and potential avenues for future research in deep learning-based breast cancer imaging.Comment: Survey, 41 page

    Complexity Reduction in Image-Based Breast Cancer Care

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    The diversity of malignancies of the breast requires personalized diagnostic and therapeutic decision making in a complex situation. This thesis contributes in three clinical areas: (1) For clinical diagnostic image evaluation, computer-aided detection and diagnosis of mass and non-mass lesions in breast MRI is developed. 4D texture features characterize mass lesions. For non-mass lesions, a combined detection/characterisation method utilizes the bilateral symmetry of the breast s contrast agent uptake. (2) To improve clinical workflows, a breast MRI reading paradigm is proposed, exemplified by a breast MRI reading workstation prototype. Instead of mouse and keyboard, it is operated using multi-touch gestures. The concept is extended to mammography screening, introducing efficient navigation aids. (3) Contributions to finite element modeling of breast tissue deformations tackle two clinical problems: surgery planning and the prediction of the breast deformation in a MRI biopsy device

    Fluorescence Guided Tumor Imaging: Foundations for Translational Applications

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    Optical imaging for medical applications is a growing field, and it has the potential to improve medical outcomes through its increased sensitivity and specificity, lower cost, and small instrumentation footprint as compared to other imaging modalities. The method holds great promise, ranging from direct clinical use as a diagnostic or therapeutic tool, to pre-clinical applications for increased understanding of pathology. Additionally, optical imaging uses non-ionizing radiation which is safe for patients, so it can be used for repeated imaging procedures to monitor therapy, guide treatment, and provide real-time feedback. The versatile features of fluorescence-based optical imaging make it suited for cancer related imaging applications to increase patient survival and improve clinical outcomes. This dissertation focuses on the development of image processing methods to obtain semi-quantitative fluorescence imaging data. These methods allow for the standardization of fluorescence imaging data for tumor characterization. When a fluorophore is located within tissue, changes in the fluorescence intensity can be used to isolate structures of interest. Typically, this is done through the accumulation of a dye in a target tissue either by the enhanced permeation and retention effect (EPR), or through targeted peptide sequences that bind receptors present in specific tissue types. When imaged, the contrast generated by a fluorescent probe can be used to indicate the presence or absence of a structure, bio-chemical compound, or receptor. Fluorescence intensity contrast can answer many biological and clinical questions effectively; however, we were interested in analyzing more than solely contrast when using planar fluorescence imaging. To better understand tumor properties, we developed a series of algorithms that harness additional pieces of information present in the fluorescence signal. We demonstrated that adding novel image processing algorithms enhanced the knowledge obtained from planar fluorescence images. Through this work, we gained an understanding of alternative approaches for processing planar fluorescence imaging data with the goal of improving future cancer diagnostics and therapeutics

    Multimodal image analysis of the human brain

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    Gedurende de laatste decennia heeft de snelle ontwikkeling van multi-modale en niet-invasieve hersenbeeldvorming technologieën een revolutie teweeg gebracht in de mogelijkheid om de structuur en functionaliteit van de hersens te bestuderen. Er is grote vooruitgang geboekt in het beoordelen van hersenschade door gebruik te maken van Magnetic Reconance Imaging (MRI), terwijl Elektroencefalografie (EEG) beschouwd wordt als de gouden standaard voor diagnose van neurologische afwijkingen. In deze thesis focussen we op de ontwikkeling van nieuwe technieken voor multi-modale beeldanalyse van het menselijke brein, waaronder MRI segmentatie en EEG bronlokalisatie. Hierdoor voegen we theorie en praktijk samen waarbij we focussen op twee medische applicaties: (1) automatische 3D MRI segmentatie van de volwassen hersens en (2) multi-modale EEG-MRI data analyse van de hersens van een pasgeborene met perinatale hersenschade. We besteden veel aandacht aan de verbetering en ontwikkeling van nieuwe methoden voor accurate en ruisrobuuste beeldsegmentatie, dewelke daarna succesvol gebruikt worden voor de segmentatie van hersens in MRI van zowel volwassen als pasgeborenen. Daarenboven ontwikkelden we een geïntegreerd multi-modaal methode voor de EEG bronlokalisatie in de hersenen van een pasgeborene. Deze lokalisatie wordt gebruikt voor de vergelijkende studie tussen een EEG aanval bij pasgeborenen en acute perinatale hersenletsels zichtbaar in MRI

    Frequency Domain Ultrasound Waveform Tomography Breast Imaging

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    Ultrasound tomography is an emerging modality for imaging breast tissue for the detection of disease. Using the principles of full waveform inversion, high-resolution quantitative sound speed and attenuation maps of the breast can be created. In this thesis, we introduce some basic principles of imaging breast disease and the formalism of sound wave propagation. We present numerical methods to model acoustic wave propagation as well methods to solve the corresponding inverse problem. Numerical simulations of sound speed and attenuation reconstructions are used to assess the efficacy of the algorithm. A careful review of the preprocessing techniques needed for the successful inversion of acoustic data is presented. Ex vivo and in vivo sound speed reconstructions highlight the significant improvements that are made upon commonly used travel time sound speed reconstruction methods. Note that we do not present ex vivo or in vivo attenuation reconstructions in this thesis. For the sound speed images, the higher resolution and contrast of the waveform method will hopefully allow a radiologist to make a more informed diagnosis of breast disease. A comparison of full waveform sound speed imaging to MRI shows a great deal of concordant findings. Lastly, we give examples of the use of full waveform inversion sound speed imaging in a clinical setting

    Advancements and Breakthroughs in Ultrasound Imaging

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    Ultrasonic imaging is a powerful diagnostic tool available to medical practitioners, engineers and researchers today. Due to the relative safety, and the non-invasive nature, ultrasonic imaging has become one of the most rapidly advancing technologies. These rapid advances are directly related to the parallel advancements in electronics, computing, and transducer technology together with sophisticated signal processing techniques. This book focuses on state of the art developments in ultrasonic imaging applications and underlying technologies presented by leading practitioners and researchers from many parts of the world

    Automatic Pancreas Segmentation and 3D Reconstruction for Morphological Feature Extraction in Medical Image Analysis

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    The development of highly accurate, quantitative automatic medical image segmentation techniques, in comparison to manual techniques, remains a constant challenge for medical image analysis. In particular, segmenting the pancreas from an abdominal scan presents additional difficulties: this particular organ has very high anatomical variability, and a full inspection is problematic due to the location of the pancreas behind the stomach. Therefore, accurate, automatic pancreas segmentation can consequently yield quantitative morphological measures such as volume and curvature, supporting biomedical research to establish the severity and progression of a condition, such as type 2 diabetes mellitus. Furthermore, it can also guide subject stratification after diagnosis or before clinical trials, and help shed additional light on detecting early signs of pancreatic cancer. This PhD thesis delivers a novel approach for automatic, accurate quantitative pancreas segmentation in mostly but not exclusively Magnetic Resonance Imaging (MRI), by harnessing the advantages of machine learning and classical image processing in computer vision. The proposed approach is evaluated on two MRI datasets containing 216 and 132 image volumes, achieving a mean Dice similarity coefficient (DSC) of 84:1 4:6% and 85:7 2:3% respectively. In order to demonstrate the universality of the approach, a dataset containing 82 Computer Tomography (CT) image volumes is also evaluated and achieves mean DSC of 83:1 5:3%. The proposed approach delivers a contribution to computer science (computer vision) in medical image analysis, reporting better quantitative pancreas segmentation results in comparison to other state-of-the-art techniques, and also captures detailed pancreas boundaries as verified by two independent experts in radiology and radiography. The contributions’ impact can support the usage of computational methods in biomedical research with a clinical translation; for example, the pancreas volume provides a prognostic biomarker about the severity of type 2 diabetes mellitus. Furthermore, a generalisation of the proposed segmentation approach successfully extends to other anatomical structures, including the kidneys, liver and iliopsoas muscles using different MRI sequences. Thus, the proposed approach can incorporate into the development of a computational tool to support radiological interpretations of MRI scans obtained using different sequences by providing a “second opinion”, help reduce possible misdiagnosis, and consequently, provide enhanced guidance towards targeted treatment planning
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