A Compact Representation of Histopathology Images using Digital Stain Separation & Frequency-Based Encoded Local Projections

In recent years, histopathology images have been increasingly used as a diagnostic tool in the medical field. The process of accurately diagnosing a biopsy sample requires significant expertise in the field, and as such can be time-consuming and is prone to uncertainty and error. With the advent of digital pathology, using image recognition systems to highlight problem areas or locate similar images can aid pathologists in making quick and accurate diagnoses. In this paper, we specifically consider the encoded local projections (ELP) algorithm, which has previously shown some success as a tool for classification and recognition of histopathology images. We build on the success of the ELP algorithm as a means for image classification and recognition by proposing a modified algorithm which captures the local frequency information of the image. The proposed algorithm estimates local frequencies by quantifying the changes in multiple projections in local windows of greyscale images. By doing so we remove the need to store the full projections, thus significantly reducing the histogram size, and decreasing computation time for image retrieval and classification tasks. Furthermore, we investigate the effectiveness of applying our method to histopathology images which have been digitally separated into their hematoxylin and eosin stain components. The proposed algorithm is tested on the publicly available invasive ductal carcinoma (IDC) data set. The histograms are used to train an SVM to classify the data. The experiments showed that the proposed method outperforms the original ELP algorithm in image retrieval tasks. On classification tasks, the results are found to be comparable to state-of-the-art deep learning methods and better than many handcrafted features from the literature.Comment: Accepted for publication in the International Conference on Image Analysis and Recognition (ICIAR 2019

A Critical Examination of Two Specific Approaches Used to Characterize Medical Images: i) Projection-based Descriptors for Image Retrieval and ii) Estimating Fractal Dimensions of Discrete Sets

In this thesis we provide a critical examination of two methods which are used to characterize medical images. Accordingly, this thesis is split into two main parts. First, we take a look at the problem of designing efficient, compact image descriptors for content-based image retrieval of digital histopathology slides. Our approach here is twofold, in that we propose a frequency-based approach to encoding projection gradients and we study the effect of separating histology slides into two colour components based on a typical staining protocol. Our frequency-based approach is shown to be more effective in terms of search performance and efficiency than the standard MinMax method of binary encoding often employed in the literature. Furthermore, we find that by separating histopathology images into their stain components, we see a significant improvement in search accuracy over the use of greyscale images, and comparable, and often superior performance to the use of three channel RGB colour images as inputs. The results in this part of the thesis not only stand on their own as a solution for image search, they can also be applied to improve the efficiency and performance of future research in this field. In the second part of this thesis, we consider the use of fractal dimensions as a method to characterize vascular networks, and other branching structures such as streams, and trees. We discuss the self-similarity (or lack thereof) of branching structures, and provide a clear argument against the use of the typical methods, such as the box-counting and sandbox methods, to estimate fractal dimensions from finite images of branching networks. Additionally, local slopes are used as a tool to illustrate the issues with these approaches when they are applied to branching structures, such as computer-generated fractal trees and retinal vascular networks. Some alternative approaches are suggested which could be used for the characterization of complex branching structures, including vascular networks

Discriminative Representations for Heterogeneous Images and Multimodal Data

Histology images of tumor tissue are an important diagnostic and prognostic tool for pathologists. Recently developed molecular methods group tumors into subtypes to further guide treatment decisions, but they are not routinely performed on all patients. A lower cost and repeatable method to predict tumor subtypes from histology could bring benefits to more cancer patients. Further, combining imaging and genomic data types provides a more complete view of the tumor and may improve prognostication and treatment decisions. While molecular and genomic methods capture the state of a small sample of tumor, histological image analysis provides a spatial view and can identify multiple subtypes in a single tumor. This intra-tumor heterogeneity has yet to be fully understood and its quantification may lead to future insights into tumor progression. In this work, I develop methods to learn appropriate features directly from images using dictionary learning or deep learning. I use multiple instance learning to account for intra-tumor variations in subtype during training, improving subtype predictions and providing insights into tumor heterogeneity. I also integrate image and genomic features to learn a projection to a shared space that is also discriminative. This method can be used for cross-modal classification or to improve predictions from images by also learning from genomic data during training, even if only image data is available at test time.Doctor of Philosoph

Roadmap on digital holography [Invited]

This Roadmap article on digital holography provides an overview of a vast array of research activities in the field of digital holography. The paper consists of a series of 25 sections from the prominent experts in digital holography presenting various aspects of the field on sensing, 3D imaging and displays, virtual and augmented reality, microscopy, cell identification, tomography, label-free live cell imaging, and other applications. Each section represents the vision of its author to describe the significant progress, potential impact, important developments, and challenging issues in the field of digital holography

Applications of diffusion MRI: Tensor-valued encoding, time-dependent diffusion, and histological validation

Diffusion MRI (dMRI) sensitizes the MR signal to the diffusion of water molecules at the microscopic level and thereby non-invasively probes tissue microstructure. This is relevant when determining biological properties of tissues, for example, cancer type and its malignancy. The problem is, however, that dMRI lacks sensitivity and specificity to distinct microstructural features because an image voxel contains vast number of different features that are mapped onto relatively few dMRI observables. To tackle this issue, we aimed at solving two gaps in current knowledge—the first was related to what microstructural aspects are of most importance and the second to how adding new observables to the dMRI measurement could improve brain tumor imaging.In this work, we first investigate the biological underpinnings of dMRI observables—focusing on the degree to which larger-scale microstructural arrangements are of relevance. In Paper I, we investigated the effects of non-straight propagation of axons and found that they are indistinguishable from those originating from the diameter of a straight axon, at least for typical measurements with a clinical scanner. We propose that the use of short diffusion times could help separate them. In Paper II, in a comparison between histology and microimaging of meningioma brain tumors, we quantified to what degree the common biological interpretation of one of the most used dMRI observable holds—mean diffusivity (MD) as reflecting cell density and fractional anisotropy reflecting tissue anisotropy. We found that the local variability in MD was explained in minority of the samples whereas FA in majority by the common interpretations. We suggested additional relevant features such as tumor vascularization, psammoma bodies, microcysts or tissue cohesivity for explaining MD variability.Second, we examined whether a framework that introduces a new measurement observable brings value in intracranial tumor imaging. This new variable is termed the b-tensor shape and is derived from the tensor-valued dMRI paradigm. In Paper IV, we adjusted and shortened by 40 % (from 5 to 3 minutes) a tensor-valued dMRI protocol for clinical imaging of intracranial tumors and applied it to characterize to a wide range of different intracranial tumors. The protocol was also used in clinical studies of patients with intracranial tumors—gliomas and meningiomas—in Paper III and Paper V, respectively. In Paper III, we found that using so-called spherical b-tensor encoding leads to enhanced conspicuity of glioma hyperintensities to white matter in all patients and on average the signal-intensity-ratio increased by 28 %. In Paper V we found that it may also inform on meningiomas preoperatively. The standard deviation of isotropic kurtosis was associated with tumor grade and with and the 10th percentiles of the mean and anisotropic kurtoses with firm tumor consistency. Preoperative knowledge of the consistency is important for the neurosurgeons when choosing the optimal surgical procedure