X線画像の胃領域抽出(情報処理工学科創設20周年特集号)

Recently, the screening system is popularized, a large number of diagnosis is the work of doctors. There are differences between individuals in the shape of their stomachs. The shape of stomach changes every second. In this paper, the extraction method of stomach region from x-ray image is described. First, the reasonable approximation can be made by general processing. Next, the contour of stomach region is extracted exactly using the knowledges about the stomach and its shape. This method is useful for x-ray image processing

Image-to-Graph Convolutional Network for 2D/3D Deformable Model Registration of Low-Contrast Organs

Organ shape reconstruction based on a single-projection image during treatment has wide clinical scope, e.g., in image-guided radiotherapy and surgical guidance. We propose an image-to-graph convolutional network that achieves deformable registration of a three-dimensional (3D) organ mesh for a low-contrast two-dimensional (2D) projection image. This framework enables simultaneous training of two types of transformation: from the 2D projection image to a displacement map, and from the sampled per-vertex feature to a 3D displacement that satisfies the geometrical constraint of the mesh structure. Assuming application to radiation therapy, the 2D/3D deformable registration performance is verified for multiple abdominal organs that have not been targeted to date, i.e., the liver, stomach, duodenum, and kidney, and for pancreatic cancer. The experimental results show shape prediction considering relationships among multiple organs can be used to predict respiratory motion and deformation from digitally reconstructed radiographs with clinically acceptable accuracy

A comparative evaluation for liver segmentation from spir images and a novel level set method using signed pressure force function

Thesis (Doctoral)--Izmir Institute of Technology, Electronics and Communication Engineering, Izmir, 2013Includes bibliographical references (leaves: 118-135)Text in English; Abstract: Turkish and Englishxv, 145 leavesDeveloping a robust method for liver segmentation from magnetic resonance images is a challenging task due to similar intensity values between adjacent organs, geometrically complex liver structure and injection of contrast media, which causes all tissues to have different gray level values. Several artifacts of pulsation and motion, and partial volume effects also increase difficulties for automatic liver segmentation from magnetic resonance images. In this thesis, we present an overview about liver segmentation methods in magnetic resonance images and show comparative results of seven different liver segmentation approaches chosen from deterministic (K-means based), probabilistic (Gaussian model based), supervised neural network (multilayer perceptron based) and deformable model based (level set) segmentation methods. The results of qualitative and quantitative analysis using sensitivity, specificity and accuracy metrics show that the multilayer perceptron based approach and a level set based approach which uses a distance regularization term and signed pressure force function are reasonable methods for liver segmentation from spectral pre-saturation inversion recovery images. However, the multilayer perceptron based segmentation method requires a higher computational cost. The distance regularization term based automatic level set method is very sensitive to chosen variance of Gaussian function. Our proposed level set based method that uses a novel signed pressure force function, which can control the direction and velocity of the evolving active contour, is faster and solves several problems of other applied methods such as sensitivity to initial contour or variance parameter of the Gaussian kernel in edge stopping functions without using any regularization term

Medical Image Segmentation by Deep Convolutional Neural Networks

Medical image segmentation is a fundamental and critical step for medical image analysis. Due to the complexity and diversity of medical images, the segmentation of medical images continues to be a challenging problem. Recently, deep learning techniques, especially Convolution Neural Networks (CNNs) have received extensive research and achieve great success in many vision tasks. Specifically, with the advent of Fully Convolutional Networks (FCNs), automatic medical image segmentation based on FCNs is a promising research field. This thesis focuses on two medical image segmentation tasks: lung segmentation in chest X-ray images and nuclei segmentation in histopathological images. For the lung segmentation task, we investigate several FCNs that have been successful in semantic and medical image segmentation. We evaluate the performance of these different FCNs on three publicly available chest X-ray image datasets. For the nuclei segmentation task, since the challenges of this task are difficulty in segmenting the small, overlapping and touching nuclei, and limited ability of generalization to nuclei in different organs and tissue types, we propose a novel nuclei segmentation approach based on a two-stage learning framework and Deep Layer Aggregation (DLA). We convert the original binary segmentation task into a two-step task by adding nuclei-boundary prediction (3-classes) as an intermediate step. To solve our two-step task, we design a two-stage learning framework by stacking two U-Nets. The first stage estimates nuclei and their coarse boundaries while the second stage outputs the final fine-grained segmentation map. Furthermore, we also extend the U-Nets with DLA by iteratively merging features across different levels. We evaluate our proposed method on two public diverse nuclei datasets. The experimental results show that our proposed approach outperforms many standard segmentation architectures and recently proposed nuclei segmentation methods, and can be easily generalized across different cell types in various organs

イ Xセン ジュウマン ゾウ ノ ディジタル ガゾウ カイセキ

京都大学0048新制・論文博士工学博士乙第5057号論工博第1610号新制||工||577(附属図書館)UT51-58-F220(主査)教授 桑原 道義, 教授 近藤 文治, 教授 長尾 眞学位規則第5条第2項該当Kyoto UniversityDFA

Visualization Techniques for Tongue Analysis in Traditional Chinese Medicine

Visual inspection of the tongue has been an important diagnostic method of Traditional Chinese Medicine (TCM). Clinic data have shown significant connections between various viscera cancers and abnormalities in the tongue and the tongue coating. Visual inspection of the tongue is simple and inexpensive, but the current practice in TCM is mainly experience-based and the quality of the visual inspection varies between individuals. The computerized inspection method provides quantitative models to evaluate color, texture and surface features on the tongue. In this paper, we investigate visualization techniques and processes to allow interactive data analysis with the aim to merge computerized measurements with human expert's diagnostic variables based on five-scale diagnostic conditions: Healthy (H), History Cancers (HC), History of Polyps (HP), Polyps (P) and Colon Cancer (C)

COMPARISON OF A PATIENT-SPECIFIC COMPUTED TOMOGRAPHY ORGAN DOSE SOFTWARE WITH COMMERCIAL PHANTOM-BASED TOOLS

Computed Tomography imaging is an important diagnostic tool but carries some risk due to radiation dose used to form the image. Currently, CT scanners report a measure of radiation dose for each scan that reflects the radiation emitted by the scanner, not the radiation dose absorbed by the patient. The radiation dose absorbed by organs, known as organ dose, is a more relevant metric that is important for risk assessment and CT protocol optimization. Tools for rapid organ-dose estimation are available but are limited to using general patient models. These publicly available tools are unable to model patient-specific anatomy and positioning within the scanner. To address these limitations, the Personalized Rapid Estimator of Dose in Computed Tomography (PREDICT) dosimetry tool was recently developed. This study validated the organ doses estimated by ‘PREDICT’ with ground truth values. The patient-specific PREDICT performance was also compared to two publicly available phantom-based methods: VirtualDose and NCICT. The PREDICT tool demonstrated lower organ dose errors compared to the phantom-based methods, demonstrating the benefit of patient-specific modeling. This study also developed a method to extract the walls of cavity organs, such as the bladder and the intestines, and quantified the effect of organ wall extraction on organ dose. The study found that the exogenous material within the cavity organ can affect organ dose estimate, therefore demonstrating the importance of boundary wall extraction in dosimetry tools such as PREDICT

ResNet18 Supported Inspection of Tuberculosis in Chest Radiographs With Integrated Deep, LBP, and DWT Features

The lung is a vital organ in human physiology and disease in lung causes various health issues. The acute disease in lung is a medical emergency and hence several methods are developed and implemented to detect the lung abnormality. Tuberculosis (TB) is one of the common lung disease and premature diagnosis and treatment is necessary to cure the disease with appropriate medication. Clinical level assessment of TB is commonly performed with chest radiographs (X-ray) and the recorded images are then examined to identify TB and its harshness. This research proposes a TB detection framework using integrated optimal deep and handcrafted features. The different stages of this work include (i) X-ray collection and processing, (ii) Pretrained Deep-Learning (PDL) scheme-based feature mining, (iii) Feature extraction with Local Binary Pattern (LBP) and Discrete Wavelet Transform (DWT), (iv) Feature optimization with Firefly-Algorithm, (v) Feature ranking and serial concatenation, and (vi) Classification by means of a 5-fold cross confirmation. The result of this study validates that, the ResNet18 scheme helps to achieve a better accuracy with SoftMax (95.2%) classifier and Decision Tree Classifier (99%) with deep and concatenated features, respectively. Further, overall performance of Decision Tree is better compared to other classifiers