이중 합성곱 신경망을 이용한 백혈구 백분율 자동 분석 시스템에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 공과대학 협동과정 바이오엔지니어링전공, 2017. 8. 김희찬.Leukocyte or white blood cell differential count is an essential examination modality of hematology laboratory in diagnosis of various blood disorders. However, it requires highly experienced hematologists for correct diagnosis from samples with inter- and intra-sample variations. Due to tedious, time and cost consuming procedure of manual differential count, there has been high demands for development of automated system. In order for it to be applicable in clinical hematology laboratories, an automated system will have to detect and classify leukocytes of different maturation stages, especially in bone marrow aspirate smears. This has been a challenging problem in computer vision, image processing, and machine learning, because of complex nature of bone marrow aspirate smear. The leukocyte has multiple maturation stages, and these maturation stages have small inter-class differences, so it is difficult to differentiate even with expert knowledge. Moreover, a problem of color, shape, and size variations among samples exists and a problem of touching cell due to high leukocyte density of bone marrow aspirate smear exists. In this dissertation, an automated leukocyte differential count system for bone marrow aspirate smear was developed to overcome problems of manual differential count and to fulfill clinically unmet needs. The system should perform the differential count with high accuracy and objectivity, and high throughput and efficiency. Moreover, it should overcome challenges of bone marrow aspirate smear. To this end, a large dataset of bone marrow smear was collected for development of a detection and a classification algorithms. Watershed transformation and saliency map were utilized for single-leukocyte detection, and the dual-stage convolutional neural network that learns global and local features of complex leukocyte maturation stages was proposed for classification. Lastly, a probability guidance algorithm was proposed for integration of detection and classification algorithms. The performance of proposed system was assessed with ten leukocyte maturation stages of myeloid and erythroid series in bone marrow aspirate smears. Total of 200 large (1388 × 1040) digital images of bone marrow aspirate smears and 2,323 small (96 × 96) single leukocyte digital images were collected. The proposed system showed a state-of-the-art performance. It achieved an average detection accuracy of 96.09% and an average classification accuracy of 97.06%, and it was able to differential count 100 leukocytes in 4 to 5 seconds. This proposes a new paradigm in diagnosis of blood disorder and showed a potential of deep learning, especially the convolutional neural network, in medical image processing. The proposed system is expected to increase the total number of analyzed leukocytes in a sample, which will provide more statistically reliable information of a patient for diagnosis.Chapter 1 Introduction 1 1.1. Introduction to Hematology 2 1.2. Introduction to Convolutional Neural Network 12 1.3. Thesis Objectives 16 Chapter 2 Leukocyte Data Collection 19 2.1. Sample Preparation and Acquisition 20 2.2. Dataset Collection and Preparation 23 Chapter 3 Leukocyte Classification 27 3.1. Introduction 28 3.2. Methods 36 3.2.1. Data Collection and Preparation 36 3.2.2. Data Oversampling and Augmentation 38 3.2.3. Convolutional Neural Network Architecture and Dual-stage Convolutional Neural Network 40 3.2.4. Convolutional Neural Network Training 43 3.2.5. Implementation 46 3.2.6. Evaluation Metrics 46 3.3. Results and Discussion 48 3.4. Conclusion 66 Chapter 4 Implementation of Automated Leukocyte Differential Count System 67 4.1. System Overview 68 4.2. Leukocyte Detection 70 4.2.1. Introduction 70 4.2.2. Detection Algorithm 75 4.2.3. Experimental Setup and Evaluation 81 4.2.4. Results and Discussion 82 4.3. Automated Leukocyte Differential Count System 92 4.3.1. Implementation of Detection and Classification Algorithms 92 4.3.2. Graphical User Interface Design 93 4.3.3. Probability Guidance Algorithm 95 4.3.4. Experimental Setup and Evaluation 97 4.3.5. Results and Discussion 98 4.4. Conclusion 102 Chapter 5 Thesis Summary and Future Work 104 5.1 Thesis Summary and Contributions 105 5.2 Future Work 109 Bibliography 115 Abstract in Korean 122Docto

Red blood cell segmentation and classification method using MATLAB

Red blood cells (RBCs) are the most important kind of blood cell. Its diagnosis is very important process for early detection of related disease such as malaria and anemia before suitable follow up treatment can be proceed. Some of the human disease can be showed by counting the number of red blood cells. Red blood cell count gives the vital information that help diagnosis many of the patient’s sickness. Conventional method under blood smears RBC diagnosis is applying light microscope conducted by pathologist. This method is time-consuming and laborious. In this project an automated RBC counting is proposed to speed up the time consumption and to reduce the potential of the wrongly identified RBC. Initially the RBC goes for image pre-processing which involved global thresholding. Then it continues with RBCs counting by using two different algorithms which are the watershed segmentation based on distance transform, and the second one is the artificial neural network (ANN) classification with fitting application depend on regression method. Before applying ANN classification there are step needed to get feature extraction data that are the data extraction using moment invariant. There are still weaknesses and constraints due to the image itself such as color similarity, weak edge boundary, overlapping condition, and image quality. Thus, more study must be done to handle those matters to produce strong analysis approach for medical diagnosis purpose. This project build a better solution and help to improve the current methods so that it can be more capable, robust, and effective whenever any sample of blood cell is analyzed. At the end of this project it conducted comparison between 20 images of blood samples taken from the medical electronic laboratory in Universiti Tun Hussein Onn Malaysia (UTHM). The proposed method has been tested on blood cell images and the effectiveness and reliability of each of the counting method has been demonstrated

Effects of Noninhibitory Serpin Maspin on the Actin Cytoskeleton: A Quantitative Image Modeling Approach

Recent developments in quantitative image analysis allow us to interrogate confocal microscopy images to answer biological questions. Clumped and layered cell nuclei and cytoplasm in confocal images challenges the ability to identify subcellular compartments. To date, there is no perfect image analysis method to identify cytoskeletal changes in confocal images. Here, we present a multidisciplinary study where an image analysis model was developed to allow quantitative measurements of changes in the cytoskeleton of cells with different maspin exposure. Maspin, a noninhibitory serpin influences cell migration, adhesion, invasion, proliferation, and apoptosis in ways that are consistent with its identification as a tumor metastasis suppressor. Using different cell types, we tested the hypothesis that reduction in cell migration by maspin would be reflected in the architecture of the actin cytoskeleton. A hybrid marker-controlled watershed segmentation technique was used to segment the nuclei, cytoplasm, and ruffling regions before measuring cytoskeletal changes. This was informed by immunohistochemical staining of cells transfected stably or transiently with maspin proteins, or with added bioactive peptides or protein. Image analysis results showed that the effects of maspin were mirrored by effects on cell architecture, in a way that could be described quantitatively

Segmentation of haematopoeitic cells in bone marrow using circle detection and splitting techniques

pre-printBone marrow evaluation is indicated when peripheral blood abnormalities are not explained by clinical, physical, or laboratory findings. In this paper, we propose a novel method for segmentation of haematopoietic cells in the bone marrow from scanned slide images. Segmentation of clumped cells is a challenging problem for this application. We first use color information and morphology to eliminate red blood cells and the background. Clumped haematopoietic cells are then segmented using circle detection and a splitting algorithm based on the detected circle centers. The Hough Transform is used for circle detection and to find the number and positions of circle centers in each region. The splitting algorithm is based on detecting the maximum curvature points, and partitioning them based on information obtained from the centers of the circles in each region. The performance of the segmentation algorithm for haematopoietic cells is evaluated by comparing our proposed method with a hematologist's visual segmentation in a set of 3748 cells

Mechanism of active targeting in solid tumors with transferrin-containing gold nanoparticles

PEGylated gold nanoparticles are decorated with various amounts of human transferrin (Tf) to give a series of Tf-targeted particles with near-constant size and electrokinetic potential. The effects of Tf content on nanoparticle tumor targeting were investigated in mice bearing s.c. Neuro2A tumors. Quantitative biodistributions of the nanoparticles 24 h after i.v. tail-vein injections show that the nanoparticle accumulations in the tumors and other organs are independent of Tf. However, the nanoparticle localizations within a particular organ are influenced by the Tf content. In tumor tissue, the content of targeting ligands significantly influences the number of nanoparticles localized within the cancer cells. In liver tissue, high Tf content leads to small amounts of the nanoparticles residing in hepatocytes, whereas most nanoparticles remain in nonparenchymal cells. These results suggest that targeted nanoparticles can provide greater intracellular delivery of therapeutic agents to the cancer cells within solid tumors than their nontargeted analogs

A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images

Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment of peripheral blood and bone marrow samples to help experts diagnose blood disorders such as acute leukemia. These systems generally initiate a segmentation stage where white blood cells are separated from the background and other nonsalient objects. As the success of such imaging systems mainly depends on the accuracy of this stage, studies attach great importance for developing accurate segmentation algorithms. Although previous studies give promising results for segmentation of sparsely distributed normal white blood cells, only a few of them focus on segmenting touching and overlapping cell clusters, which is usually the case when leukemic cells are present. In this article, we present a new algorithm for segmentation of both normal and leukemic cells in peripheral blood and bone marrow images. In this algorithm, we propose to model color and shape characteristics of white blood cells by defining two transformations and introduce an efficient use of these transformations in a marker-controlled watershed algorithm. Particularly, these domain specific characteristics are used to identify markers and define the marking function of the watershed algorithm as well as to eliminate false white blood cells in a postprocessing step. Working on 650 white blood cells in peripheral blood and bone marrow images, our experiments reveal that the proposed algorithm improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr

Imaging White Blood Cells using a Snapshot Hyper-Spectral Imaging System

Automated white blood cell (WBC) counting systems process an extracted whole blood sample and provide a cell count. A step that would not be ideal for onsite screening of individuals in triage or at a security gate. Snapshot Hyper-Spectral imaging systems are capable of capturing several spectral bands simultaneously, offering co-registered images of a target. With appropriate optics, these systems are potentially able to image blood cells in vivo as they flow through a vessel, eliminating the need for a blood draw and sample staining. Our group has evaluated the capability of a commercial Snapshot Hyper-Spectral imaging system, specifically the Arrow system from Rebellion Photonics, in differentiating between white and red blood cells on unstained and sealed blood smear slides. We evaluated the imaging capabilities of this hyperspectral camera as a platform to build an automated blood cell counting system. Hyperspectral data consisting of 25, 443x313 hyperspectral bands with ~3nm spacing were captured over the range of 419 to 494nm. Open-source hyperspectral datacube analysis tools, used primarily in Geographic Information Systems (GIS) applications, indicate that white blood cells\u27 features are most prominent in the 428-442nm band for blood samples viewed under 20x and 50x magnification over a varying range of illumination intensities. The system has shown to successfully segment blood cells based on their spectral-spatial information. These images could potentially be used in subsequent automated white blood cell segmentation and counting algorithms for performing in vivo white blood cell counting

Medical gas plasma promotes blood coagulation via platelet activation

Major blood loss still is a risk factor during surgery. Electrocauterization often is used for necrotizing the tissue and thereby halts bleeding (hemostasis). However, the carbonized tissue is prone to falling off, putting patients at risk of severe side effects, such as dangerous internal bleeding many hours after surgery. We have developed a medical gas plasma jet technology as an alternative to electrocauterization and investigated its hemostatic (blood clotting) effects and mechanisms of action using whole human blood. The gas plasma efficiently coagulated anticoagulated donor blood, which resulted from the local lysis of red blood cells (hemolysis). Image cytometry further showed enhanced platelet aggregation. Gas plasmas release reactive oxygen species (ROS), but neither scavenging of long-lived ROS nor addition of chemically-generated ROS were able to abrogate or recapitulate the gas plasma effect, respectively. However, platelet activation was markedly impaired in platelet-rich plasma when compared to gas plasma-treated whole blood that moreover contained significant amounts of hemoglobin indicative of red blood cell lysis (hemolysis). Finally, incubation of whole blood with concentration-matched hemolysates phenocopied the gas plasmas-mediated platelet activation. These results will spur the translation of plasma systems for hemolysis into clinical practice

Image processing and analysis methods in quantitative endothelial cell biology

This thesis details the development of computerised image processing and analysis pipelines for quantitative evaluation of microscope image data acquired in endothelial vascular biology experimentation. The overarching objective of this work was to advance our understanding of the cell biology of cardiovascular processes; principally involving haemostasis, thrombosis, and inflammation. Bioinformatics techniques are increasingly necessary to extract and evaluate information from biological experimentation. In cell biology advances in microscopy and the increased acquisition of large scale digital image data sets have created a need for automated image processing and data analysis. The development, testing, and evaluation of three computerised workflows for analysis of microscopy images investigating cardiovascular cell biology are described here. The first image analysis pipeline extracts morphometric features from high-throughput experiments imaging endothelial cells and organelles. Segmentation of endothelial cells and their organelles followed by extraction of morphometric features provides a rich quantitative data set to investigate haemostatic mechanisms. A second image processing workflow was applied to platelet images obtained from super-resolution microscopy, and used in a proof-of-principle study of a new platelet dense-granule deficiency diagnostic method. The method was able to efficiently differentiate between healthy volunteers and three patients with Hermansky-Pudlak syndrome. This was achieved by segmenting and counting the number of CD63-positive structures per platelet, allowing for the differentiation of patients from control volunteers with 99\% confidence. The final workflow described is a video analysis method that quantifies interactions of leukocytes with an endothelial monolayer. Phase contrast microscopy videos were analysed with a Haar-like features object detection and custom tracking method to quantify the dynamic interaction of rolling leukocytes. This technique provides much more information than a manual evaluation and was found to give a tracking accuracy of 92\%. These three methodologies provide a toolkit to further biological understanding of multiple facets of cardiovascular behaviour