Iterative annotation to ease neural network training: Specialized machine learning in medical image analysis

Neural networks promise to bring robust, quantitative analysis to medical fields, but adoption is limited by the technicalities of training these networks. To address this translation gap between medical researchers and neural networks in the field of pathology, we have created an intuitive interface which utilizes the commonly used whole slide image (WSI) viewer, Aperio ImageScope (Leica Biosystems Imaging, Inc.), for the annotation and display of neural network predictions on WSIs. Leveraging this, we propose the use of a human-in-the-loop strategy to reduce the burden of WSI annotation. We track network performance improvements as a function of iteration and quantify the use of this pipeline for the segmentation of renal histologic findings on WSIs. More specifically, we present network performance when applied to segmentation of renal micro compartments, and demonstrate multi-class segmentation in human and mouse renal tissue slides. Finally, to show the adaptability of this technique to other medical imaging fields, we demonstrate its ability to iteratively segment human prostate glands from radiology imaging data.Comment: 15 pages, 7 figures, 2 supplemental figures (on the last page

Identification of Topological Features in Renal Tumor Microenvironment Associated with Patient Survival

Motivation As a highly heterogeneous disease, the progression of tumor is not only achieved by unlimited growth of the tumor cells, but also supported, stimulated, and nurtured by the microenvironment around it. However, traditional qualitative and/or semi-quantitative parameters obtained by pathologist’s visual examination have very limited capability to capture this interaction between tumor and its microenvironment. With the advent of digital pathology, computerized image analysis may provide a better tumor characterization and give new insights into this problem. Results We propose a novel bioimage informatics pipeline for automatically characterizing the topological organization of different cell patterns in the tumor microenvironment. We apply this pipeline to the only publicly available large histopathology image dataset for a cohort of 190 patients with papillary renal cell carcinoma obtained from The Cancer Genome Atlas project. Experimental results show that the proposed topological features can successfully stratify early- and middle-stage patients with distinct survival, and show superior performance to traditional clinical features and cellular morphological and intensity features. The proposed features not only provide new insights into the topological organizations of cancers, but also can be integrated with genomic data in future studies to develop new integrative biomarkers

Improving cancer subtype diagnosis and grading using clinical decision support system based on computer-aided tissue image analysis

This research focuses towards the development of a clinical decision support system (CDSS) based on cellular and tissue image analysis and classification system that improves consistency and facilitates the clinical decision making process. In a typical cancer examination, pathologists make diagnosis by manually reading morphological features in patient biopsy images, in which cancer biomarkers are highlighted by using different staining techniques. This process is subjected to pathologist's training and experience, especially when the same cancer has several subtypes (i.e. benign tumor subtype vs. malignant subtype) and the same cancer tissue biopsy contains heterogeneous morphologies in different locations. The variability in pathologist's manual reading may result in varying cancer diagnosis and treatment. This Ph.D. research aims to reduce the subjectivity and variation existing in traditional histo-pathological reading of patient tissue biopsy slides through Computer-Aided Diagnosis (CAD). Using the CAD, quantitative molecular profiling of cancer biomarkers of stained biopsy images are obtained by extracting and analyzing texture and cellular structure features. In addition, cancer sub-type classification and a semi-automatic grade scoring (i.e. clinical decision making) for improved consistency over a large number of cancer subtype images can be performed. The CAD tools do have their own limitations and in certain cases the clinicians, however, prefer systems which are flexible and take into account their individuality when necessary by providing some control rather than fully automated system. Therefore, to be able to introduce CDSS in health care, we need to understand users' perspectives and preferences on the new information technology. This forms as the basis for this research where we target to present the quantitative information acquired through the image analysis, annotate the images and provide suitable visualization which can facilitate the process of decision making in a clinical setting.PhDCommittee Chair: Dr. May D. Wang; Committee Member: Dr. Andrew N. Young; Committee Member: Dr. Anthony J. Yezzi; Committee Member: Dr. Edward J. Coyle; Committee Member: Dr. Paul Benkese

Transcription factor NFE2L1 decreases in glomerulonephropathies after podocyte damage

Funding: This work was supported by NHS Lothian. This project received funding from the European Union’s Horizon 2020 research and innovation programme under Grant Agreement No. 101017453 as part of the KATY project, and from NuCana plc.Podocyte cellular injury and detachment from glomerular capillaries constitute a critical factor contributing to kidney disease. Notably, transcription factors are instrumental in maintaining podocyte differentiation and homeostasis. This study explores the hitherto uninvestigated expression of Nuclear Factor Erythroid 2-related Factor 1 (NFE2L1) in podocytes. We evaluated the podocyte expression of NFE2L1, Nuclear Factor Erythroid 2-related Factor 2 (NFE2L2), and NAD(P)H:quinone Oxidoreductase (NQO1) in 127 human glomerular disease biopsies using multiplexed immunofluorescence and image analysis. We found that both NFE2L1 and NQO1 expressions were significantly diminished across all observed renal diseases. Furthermore, we exposed human immortalized podocytes and ex vivo kidney slices to Puromycin Aminonucleoside (PAN) and characterized the NFE2L1 protein isoform expression. PAN treatment led to a reduction in the nuclear expression of NFE2L1 in ex vivo kidney slices and podocytes.Publisher PDFPeer reviewe

Large-scale 3-dimensional quantitative imaging of tissues: state-of-the-art and translational implications

Recent developments in automated optical sectioning microscope systems have enabled researchers to conduct high resolution, three-dimensional (3D) microscopy at the scale of millimeters in various types of tissues. This powerful technology allows the exploration of tissues at an unprecedented level of detail, while preserving the spatial context. By doing so, such technology will also enable researchers to explore cellular and molecular signatures within tissue and correlate with disease course. This will allow an improved understanding of pathophysiology and facilitate a precision medicine approach to assess the response to treatment. The ability to perform large-scale imaging in 3D cannot be realized without the widespread availability of accessible quantitative analysis. In this review, we will outline recent advances in large-scale 3D imaging and discuss the available methodologies to perform meaningful analysis and potential applications in translational research

Karpinski Score under Digital Investigation: A Fully Automated Segmentation Algorithm to Identify Vascular and Stromal Injury of Donors’ Kidneys

In kidney transplantations, the evaluation of the vascular structures and stromal areas is crucial for determining kidney acceptance, which is currently based on the pathologist’s visual evaluation. In this context, an accurate assessment of the vascular and stromal injury is fundamental to assessing the nephron status. In the present paper, the authors present a fully automated algorithm, called RENFAST (Rapid EvaluatioN of Fibrosis And vesselS Thickness), for the segmentation of kidney blood vessels and fibrosis in histopathological images. The proposed method employs a novel strategy based on deep learning to accurately segment blood vessels, while interstitial fibrosis is assessed using an adaptive stain separation method. The RENFAST algorithm is developed and tested on 350 periodic acid–Schiff (PAS) images for blood vessel segmentation and on 300 Massone’s trichrome (TRIC) stained images for the detection of renal fibrosis. In the TEST set, the algorithm exhibits excellent segmentation performance in both blood vessels (accuracy: 0.8936) and fibrosis (accuracy: 0.9227) and outperforms all the compared methods. To the best of our knowledge, the RENFAST algorithm is the first fully automated method capable of detecting both blood vessels and fibrosis in digital histological images. Being very fast (average computational time 2.91 s), this algorithm paves the way for automated, quantitative, and real-time kidney graft assessments