Effect of image compression and scaling on automated scoring of immunohistochemical stainings and segmentation of tumor epithelium

<p>Abstract</p> <p>Background</p> <p>Digital whole-slide scanning of tissue specimens produces large images demanding increasing storing capacity. To reduce the need of extensive data storage systems image files can be compressed and scaled down. The aim of this article is to study the effect of different levels of image compression and scaling on automated image analysis of immunohistochemical (IHC) stainings and automated tumor segmentation.</p> <p>Methods</p> <p>Two tissue microarray (TMA) slides containing 800 samples of breast cancer tissue immunostained against Ki-67 protein and two TMA slides containing 144 samples of colorectal cancer immunostained against EGFR were digitized with a whole-slide scanner. The TMA images were JPEG2000 wavelet compressed with four compression ratios: lossless, and 1:12, 1:25 and 1:50 lossy compression. Each of the compressed breast cancer images was furthermore scaled down either to 1:1, 1:2, 1:4, 1:8, 1:16, 1:32, 1:64 or 1:128. Breast cancer images were analyzed using an algorithm that quantitates the extent of staining in Ki-67 immunostained images, and EGFR immunostained colorectal cancer images were analyzed with an automated tumor segmentation algorithm. The automated tools were validated by comparing the results from losslessly compressed and non-scaled images with results from conventional visual assessments. Percentage agreement and kappa statistics were calculated between results from compressed and scaled images and results from lossless and non-scaled images.</p> <p>Results</p> <p>Both of the studied image analysis methods showed good agreement between visual and automated results. In the automated IHC quantification, an agreement of over 98% and a kappa value of over 0.96 was observed between losslessly compressed and non-scaled images and combined compression ratios up to 1:50 and scaling down to 1:8. In automated tumor segmentation, an agreement of over 97% and a kappa value of over 0.93 was observed between losslessly compressed images and compression ratios up to 1:25.</p> <p>Conclusions</p> <p>The results of this study suggest that images stored for assessment of the extent of immunohistochemical staining can be compressed and scaled significantly, and images of tumors to be segmented can be compressed without compromising computer-assisted analysis results using studied methods.</p> <p>Virtual slides</p> <p>The virtual slide(s) for this article can be found here: <url>http://www.diagnosticpathology.diagnomx.eu/vs/2442925476534995</url></p

DEVELOPING NOVEL COMPUTER-AIDED DETECTION AND DIAGNOSIS SYSTEMS OF MEDICAL IMAGES

Reading medical images to detect and diagnose diseases is often difficult and has large inter-reader variability. To address this issue, developing computer-aided detection and diagnosis (CAD) schemes or systems of medical images has attracted broad research interest in the last several decades. Despite great effort and significant progress in previous studies, only limited CAD schemes have been used in clinical practice. Thus, developing new CAD schemes is still a hot research topic in medical imaging informatics field. In this dissertation, I investigate the feasibility of developing several new innovative CAD schemes for different application purposes. First, to predict breast tumor response to neoadjuvant chemotherapy and reduce unnecessary aggressive surgery, I developed two CAD schemes of breast magnetic resonance imaging (MRI) to generate quantitative image markers based on quantitative analysis of global kinetic features. Using the image marker computed from breast MRI acquired pre-chemotherapy, CAD scheme enables to predict radiographic complete response (CR) of breast tumors to neoadjuvant chemotherapy, while using the imaging marker based on the fusion of kinetic and texture features extracted from breast MRI performed after neoadjuvant chemotherapy, CAD scheme can better predict the pathologic complete response (pCR) of the patients. Second, to more accurately predict prognosis of stroke patients, quantifying brain hemorrhage and ventricular cerebrospinal fluid depicting on brain CT images can play an important role. For this purpose, I developed a new interactive CAD tool to segment hemorrhage regions and extract radiological imaging marker to quantitatively determine the severity of aneurysmal subarachnoid hemorrhage at presentation and correlate the estimation with various homeostatic/metabolic derangements and predict clinical outcome. Third, to improve the efficiency of primary antibody screening processes in new cancer drug development, I developed a CAD scheme to automatically identify the non-negative tissue slides, which indicate reactive antibodies in digital pathology images. Last, to improve operation efficiency and reliability of storing digital pathology image data, I developed a CAD scheme using optical character recognition algorithm to automatically extract metadata from tissue slide label images and reduce manual entry for slide tracking and archiving in the tissue pathology laboratories. In summary, in these studies, we developed and tested several innovative approaches to identify quantitative imaging markers with high discriminatory power. In all CAD schemes, the graphic user interface-based visual aid tools were also developed and implemented. Study results demonstrated feasibility of applying CAD technology to several new application fields, which has potential to assist radiologists, oncologists and pathologists improving accuracy and consistency in disease diagnosis and prognosis assessment of using medical image

Strength and dendritic organization of thalamocortical synapses onto excitatory layer 4 neurons

The thalamus is a potent driver of cortical activity, even though cortical synapses onto layer 4 (L4) neurons outnumber thalamic synapses ten to one. Previous in vitro studies have suggested that enhanced efficacy of thalamocortical (TC) relative to corticocortical (CC) synapses explains the effectiveness of the thalamus. We investigated possible key anatomical and physiological differences between these inputs onto excitatory L4 neurons in vivo. We developed a high-throughput light microscopy method, validated by electron microscopy, to completely map the locations of synapses across an entire dendritic tree. This demonstrated that TC synapses are slightly more proximal to the soma than CC synapses, but detailed compartmental modeling predicted that dendritic filtering does not appreciably favor one synaptic class over another. Measurements of synaptic strength in intact animals revealed that both TC and CC synapses are weak and approximately equivalent. We conclude that thalamic potency relies, not on enhanced TC strength, but on coincident activation of converging inputs

Nano-sized Optical Fluorescence Labels And Uses Thereof

A composition is disclosed which is capable of being used for detection, comprising an encapsulated noble metal nanocluster. Methods for preparing the encapsulated noble metal nanoclusters, and methods of using the encapsulated noble metal nanoclusters are also disclosed. The noble metal nanoclusters are preferably encapsulated by a dendrimer or a peptide. The encapsulated noble metal nanoclusters have a characteristic spectral emission, wherein said spectral emission is varied by controlling the nature of the encapsulating material, such as by controlling the size of the nanocluster and/or the generation of the dendrimer, and wherein said emission is used to provide information about a biological state.Georgia Tech Research Corporatio

Analysis of Signal Decomposition and Stain Separation methods for biomedical applications

Nowadays, the biomedical signal processing and classification and medical image interpretation play an essential role in the detection and diagnosis of several human diseases. The problem of high variability and heterogeneity of information, which is extracted from digital data, can be addressed with signal decomposition and stain separation techniques which can be useful approaches to highlight hidden patterns or rhythms in biological signals and specific cellular structures in histological color images, respectively. This thesis work can be divided into two macro-sections. In the first part (Part I), a novel cascaded RNN model based on long short-term memory (LSTM) blocks is presented with the aim to classify sleep stages automatically. A general workflow based on single-channel EEG signals is developed to enhance the low performance in staging N1 sleep without reducing the performances in the other sleep stages (i.e. Wake, N2, N3 and REM). In the same context, several signal decomposition techniques and time-frequency representations are deployed for the analysis of EEG signals. All extracted features are analyzed by using a novel correlation-based timestep feature selection and finally the selected features are fed to a bidirectional RNN model. In the second part (Part II), a fully automated method named SCAN (Stain Color Adaptive Normalization) is proposed for the separation and normalization of staining in digital pathology. This normalization system allows to standardize digitally, automatically and in a few seconds, the color intensity of a tissue slide with respect to that of a target image, in order to improve the pathologist’s diagnosis and increase the accuracy of computer-assisted diagnosis (CAD) systems. Multiscale evaluation and multi-tissue comparison are performed for assessing the robustness of the proposed method. In addition, a stain normalization based on a novel mathematical technique, named ICD (Inverse Color Deconvolution) is developed for immunohistochemical (IHC) staining in histopathological images. In conclusion, the proposed techniques achieve satisfactory results compared to state-of-the-art methods in the same research field. The workflow proposed in this thesis work and the developed algorithms can be employed for the analysis and interpretation of other biomedical signals and for digital medical image analysis

Underwater image restoration: super-resolution and deblurring via sparse representation and denoising by means of marine snow removal

Underwater imaging has been widely used as a tool in many fields, however, a major issue is the quality of the resulting images/videos. Due to the light's interaction with water and its constituents, the acquired underwater images/videos often suffer from a significant amount of scatter (blur, haze) and noise. In the light of these issues, this thesis considers problems of low-resolution, blurred and noisy underwater images and proposes several approaches to improve the quality of such images/video frames. Quantitative and qualitative experiments validate the success of proposed algorithms

Colorectal cancer residual disease at maximal response to EGFR blockade displays a druggable Paneth cell–like phenotype

Blockade of epidermal growth factor receptor (EGFR) causes tumor regression in some patients with metastatic colorectal cancer (mCRC). However, residual disease reservoirs typically remain even after maximal response to therapy, leading to relapse. Using patient-derived xenografts (PDXs), we observed that mCRC cells surviving EGFR inhibition exhibited gene expression patterns similar to those of a quiescent subpopulation of normal intestinal secretory precursors with Paneth cell characteristics. Compared with untreated tumors, these pseudodifferentiated tumor remnants had reduced expression of genes encoding EGFR-activating ligands, enhanced activity of human epidermal growth factor receptor 2 (HER2) and HER3, and persistent signaling along the phosphatidylinositol 3-kinase (PI3K) pathway. Clinically, properties of residual disease cells from the PDX models were detected in lingering tumors of responsive patients and in tumors of individuals who had experienced early recurrence. Mechanistically, residual tumor reprogramming after EGFR neutralization was mediated by inactivation of Yes-associated protein (YAP), a master regulator of intestinal epithelium recovery from injury. In preclinical trials, Pan-HER antibodies minimized residual disease, blunted PI3K signaling, and induced long-term tumor control after treatment discontinuation. We found that tolerance to EGFR inhibition is characterized by inactivation of an intrinsic lineage program that drives both regenerative signaling during intestinal repair and EGFR-dependent tumorigenesis. Thus, our results shed light on CRC lineage plasticity as an adaptive escape mechanism from EGFR-targeted therapy and suggest opportunities to preemptively target residual disease

Advancing fluorescent contrast agent recovery methods for surgical guidance applications

Fluorescence-guided surgery (FGS) utilizes fluorescent contrast agents and specialized optical instruments to assist surgeons in intraoperatively identifying tissue-specific characteristics, such as perfusion, malignancy, and molecular function. In doing so, FGS represents a powerful surgical navigation tool for solving clinical challenges not easily addressed by other conventional imaging methods. With growing translational efforts, major hurdles within the FGS field include: insufficient tools for understanding contrast agent uptake behaviors, the inability to image tissue beyond a couple millimeters, and lastly, performance limitations of currently-approved contrast agents in accurately and rapidly labeling disease. The developments presented within this thesis aim to address such shortcomings. Current preclinical fluorescence imaging tools often sacrifice either 3D scale or spatial resolution. To address this gap in high-resolution, whole-body preclinical imaging tools available, the crux of this work lays on the development of a hyperspectral cryo-imaging system and image-processing techniques to accurately recapitulate high-resolution, 3D biodistributions in whole-animal experiments. Specifically, the goal is to correct each cryo-imaging dataset such that it becomes a useful reporter for whole-body biodistributions in relevant disease models. To investigate potential benefits of seeing deeper during FGS, we investigated short-wave infrared imaging (SWIR) for recovering fluorescence beyond the conventional top few millimeters. Through phantom, preclinical, and clinical SWIR imaging, we were able to 1) validate the capability of SWIR imaging with conventional NIR-I fluorophores, 2) demonstrate the translational benefits of SWIR-ICG angiography in a large animal model, and 3) detect micro-dose levels of an EGFR-targeted NIR-I probe during a Phase 0 clinical trial. Lastly, we evaluated contrast agent performances for FGS glioma resection and breast cancer margin assessment. To evaluate glioma-labeling performance of untargeted contrast agents, 3D agent biodistributions were compared voxel-by-voxel to gold-standard Gd-MRI and pathology slides. Finally, building on expertise in dual-probe ratiometric imaging at Dartmouth, a 10-pt clinical pilot study was carried out to assess the technique’s efficacy for rapid margin assessment. In summary, this thesis serves to advance FGS by introducing novel fluorescence imaging devices, techniques, and agents which overcome challenges in understanding whole-body agent biodistributions, recovering agent distributions at greater depths, and verifying agents’ performance for specific FGS applications

The Development and Validation of a Molecular Imaging Probe Targeted to Cathepsin D for the In-vivo Detection of Alzheimer Disease

Background: Currently there is no widely accepted test to diagnose AD. The involvement of the lysosomal system in Alzheimer’s disease (AD) progression provides an opportunity to develop associated biomarkers. The lysosomal enzyme Cathepsin D (CatD) has been shown to be over-expressed in the AD brain before clinical onset. We have developed a dual modality contrast agent (CA) to detect CatD activity which consists of an HIV-1 Tat Cell Penetrating Peptide (CPP) conjugated to a CatD cleavage sequence and two imaging moieties consisting of a fluorescently- tagged probe and a DOTA cage for chelating Gallium-68. The purpose of this work was to validate CatD as an AD biomarker across multiple AD disease models and to test our novel CA in-vivo by means of optical near infra-red (NIR) fluorescence imaging and positron emission tomography (PET). Methods: Three transgenic (Tg) mouse AD model strains were tested for CatD expression by Western blot and immunohistochemistry analysis. The chosen mouse line (5XFAD) and controls were imaged at 5 and 12 months of age using an eXplore Optix scanner (GE Healthcare, Milwaukee, WI, USA). Next, mice at 2, 6 and 9 months of age were tested using an Inveon microPET system (Siemens Medical Solutions, Knoxville TN, USA) using a 68Ga-labeled CatD targeted CA. Results: All 3 AD mice demonstrated an elevation of CatD expression in parallel with AD pathology. The 5XFAD had the highest levels of CatD, making it the best mouse model to study CatD upregulation. The rate of the NIR CatD Targeted CA washout was significantly slower in the 5XFAD mice (

Digital image processing for prognostic and diagnostic clinical pathology

When digital imaging and image processing methods are applied to clinical diagnostic and prognostic needs, the methods can be seen to increase human understanding and provide objective measurements. Most current clinical applications are limited to providing subjective information to healthcare professionals rather than providing objective measures. This Thesis provides detail of methods and systems that have been developed both for objective and subjective microscopy applications. A system framework is presented that provides a base for the development of microscopy imaging systems. This practical framework is based on currently available hardware and developed with standard software development tools. Image processing methods are applied to counter optical limitations of the bright field microscope, automating the system and allowing for unsupervised image capture and analysis. Current literature provides evidence that 3D visualisation has provided increased insight and application in many clinical areas. There have been recent advancements in the use of 3D visualisation for the study of soft tissue structures, but its clinical application within histology remains limited. Methods and applications have been researched and further developed which allow for the 3D reconstruction and visualisation of soft tissue structures using microtomed serial histological sections specimens. A system has been developed suitable for this need is presented giving considerations to image capture, data registration and 3D visualisation, requirements. The developed system has been used to explore and increase 3D insight on clinical samples. The area of automated objective image quantification of microscope slides presents the allure of providing objective methods replacing existing objective and subjective methods, increasing accuracy and rsducinq manual burden. One such existing objective test is DNA Image Ploidy which seeks to characterise cancer by the measurement of DNA content within individual cell nuclei, an accepted but manually burdensome method. The main novelty of the work completed lies in the development of an automated system for DNA Image Ploidy measurement, combining methods for automatic specimen focus, segmentation, parametric extraction and the implementation of an automated cell type classification system. A consideration for any clinical image processing system is the correct sampling of the tissue under study. VVhile the image capture requirements for both objective systems and subjective systems are similar there is also an important link between the 3D structures of the tissue. 3D understanding can aid in decisions regarding the sampling criteria of objective tests for as although many tests are completed in the 2D realm the clinical samples are 3D objects. Cancers such as Prostate and Breast cancer are known to be multi-focal, with areas of seeming physically, independent areas of disease within a single site. It is not possible to understand the true 3D nature of the samples using 2D micro-tomed sections in isolation from each other. The 3D systems described in this report provide a platform of the exploration of the true multi focal nature of disease soft tissue structures allowing for the sampling criteria of objective tests such as DNA Image Ploidy to be correctly set. For the Automated DNA Image Ploidy and the 3D reconstruction and visualisation systems, clinical review has been completed to test the increased insights provided. Datasets which have been reconstructed from microtomed serial sections and visualised with the developed 3D system area presented. For the automated DNA Image Ploidy system, the developed system is compared with the existing manual method to qualify the quality of data capture, operational speed and correctness of nuclei classification. Conclusions are presented for the work that has been completed and discussion given as to future areas of research that could be undertaken, extending the areas of study, increasing both clinical insight and practical application