개 흉부 방사선 자료의 딥러닝 적용을 통한 심장 면적 자동 분석 방법 개발

학위논문 (석사) -- 서울대학교 대학원 : 보건대학원 보건학과, 2021. 2. 성주헌 .Introduction : Measurement of canine heart size in thoracic lateral radiograph is crucial in detecting heart enlargement caused by diverse cardiovascular diseases. The purpose of this study was 1) to develop deep learning (DL) model that segments heart and 4th thoracic vertebrae (T4) body, 2) develop new radiographic measurement using calculated 2 dimensional heart area and length of T4 body, and 3) calculate performance of our new measurement to detect heart enlargement using echocardiographic measurement as gold standard. Methods : Total 1,000 thoracic radiographic images of dog were collected from Seoul National University Veterinary Medicine Teaching Hospital from 2018. 01. 01 to 2020. 08. 31. Given ground truth mask, two Attention U-Nets for segmentation of heart and T4 body were trained using different hyperparameters. Among 1,000 images, model was trained with 800 images, validated with 100 images and tested with 100 images. Performance of DL model was assessed with dice score coefficient, precision and recall. New calculation method was developed to calculate heart volume and adjust by T4 body length, which was named vertebra-adjusted heart volume (VaHV). Correlation of VaHV of 100 test images and reported VHS (vertebral heart score) was assessed. With 188 images with concurrent echocardiographic examination, diagnostic performance of VaHV for detecting cardiomegaly was assessed by students t-test, receiver operating characteristic (ROC) curve and area under the curve (AUC). Results : The two trained DL model showed very good similarity with ground truth (dice score coefficient 0.956 for heart segmentation, 0.844 for T4 body segmentation). VaHV of 100 test images showed statistically significant correlation with VHS. VaHV showed better diagnostic performance in detecting left atrial enlargement and left ventricular enlargement than VHS. Conclusions : DL model can be used to segment heart and vertebrae in veterinary radiographic images. Our new radiographic measurement obtained from DL model can potentially be used to assess and monitor cardiomegaly in dogs.개의 심장질환 중 가장 높은 유병률을 나타내는 이첨판 폐쇄부전증을 포함하여 다양한 심장질환이 점진적인 심비대를 특징으로 하기에, 개의 흉부 방사선 영상에서 심장 크기를 측정하여 심비대를 진단하는 것은 심장질환을 조기에 발견하고 적절한 치료시기를 계획하는 데 있어 매우 중요한 부분을 차지한다. 현장에서 바로 잴 수 있는 지표로서 기존에는 vertebral heart score (VHS)가 널리 사용되고 있으나, 이는 1차원 길이의 합으로 이루어진 지표이기에 심비대를 진단하는 데 한계가 있을 수 있다. 본 연구의 목적은 개의 흉부 방사선 영상에서 심장 면적과 척추체 길이를 자동으로 산출하는 딥러닝 모델을 구축하고, 이를 이용하여 심장 용적을 추정할 수 있는 지표를 개발하는 것이었다. 본 연구는 서울대학교 수의과대학 동물병원 검진자료로부터 수집된 총 1,188 건의 자료를 바탕으로 수행되었다. 1,000건의 영상은 심장과 척추체의 면적을 자동으로 분할 (semantic segmentation) 해주는 딥러닝 모델을 훈련시키고 평가하기 위해 사용되었으며, 이를 이용하여 새로운 심장 용적 지표인 vertebra-adjusted heart volume (VaHV) 를 산출했다. 추가로 1달 미만 간격의 방사선 촬영 기록과 심장초음파 검진 기록을 가진 188건의 영상을 수집하여 훈련된 딥러닝 모델을 이용해 계산한 VaHV와 심장초음파 기록 (LA/Ao, LVIDDN) 을 비교하여 VaHV의 심비대 진단능을 평가하였다. 심장과 척추체의 면적 불균형을 보완하기 위해 서로 다른 hyperparameter를 가진 Improved Attention U-Net이 사용되었으며, 두 개의 신경망 모두 시험용 데이터셋에서 정답 면적과 높은 일치율 (dice score coefficient 0.956, 0.844) 를 보였으며, 신경망의 예측결과에서 계산된 VaHV는 기존에 기록된 VHS와 통계적으로 유의한 상관계수를 (r = 0.69, P 1.6, LVIDDN > 1.7) 에 대해 높은 예측력을 가짐을 확인하였으며 (AUC 0.818), 기존에 사용되던 VHS의 예측력 (AUC 0.805) 보다 우수한 성능을 보임을 확인하였다. 본 연구는 수의방사선에서 최초로 딥러닝을 이용한 의미론적 면적 분할 (semantic segmentation) 을 적용하여 수의 영상에서 기존보다 더 다양한 신경망 알고리즘이 활용될 수 있는 가능성을 보여주었다. 또한 심장의 2차원 면적이 심비대를 진단함에 있어 기존의 길이 기반 심장 크기 측정 지표를 보완할 수 있다는 가능성을 보여주었다.1. Introduction 6 2. Materials and Methods 8 2.1 Data Collection 8 2.2 Development of DL model 10 2.2.1 Introduction to Semantic Segmentation 10 2.2.2 Attention U-Net with Focal Tversky Loss, Surface Loss 11 2.2.2.1 Attention U-Net 11 2.2.2.2 Improved Attention U-Net with Focal Tversky Loss 12 2.2.2.3 Surface Loss 14 2.2.3 Image Preprocessing 15 2.2.4 Establishing Ground Truth 16 2.2.5 Training DL Model 16 2.2.5.1 DL Model for Heart Segmentation 18 2.2.5.2 DL Model for T4 Body Segmentation 19 2.3 Volumetric Measurement of Heart 20 2.3.1 Analysis of Binary Mask 20 2.3.2 Vertebra-adjusted Heart Volume (VaHV) 21 2.3.3 Calculation of VaHV from DL Model Prediction 22 2.4 Statistical Methods 23 2.4.1 Segmentation DL Model Performance 23 2.4.2 Correlation between VaHV and VHS 23 2.4.3 Evaluation of Cardiomegaly using Echocardiographic Measurement 23 3. Results 24 3.1 DL Model 24 3.1.1 Heart Segmentation 24 3.1.2 T4 Body Segmentation 26 3.2 Descriptive Statistics of VaHV 28 3.3 Correlation between VaHV and VHS 29 3.4 Diagnostic Performance of VaHV for Detecting Cardiomegaly 30 4. Discussion 33 5. Conclusion 34 6. References 35 초록 38Maste

Chest radiographs and machine learning - Past, present and future.

Despite its simple acquisition technique, the chest X-ray remains the most common first-line imaging tool for chest assessment globally. Recent evidence for image analysis using modern machine learning points to possible improvements in both the efficiency and the accuracy of chest X-ray interpretation. While promising, these machine learning algorithms have not provided comprehensive assessment of findings in an image and do not account for clinical history or other relevant clinical information. However, the rapid evolution in technology and evidence base for its use suggests that the next generation of comprehensive, well-tested machine learning algorithms will be a revolution akin to early advances in X-ray technology. Current use cases, strengths, limitations and applications of chest X-ray machine learning systems are discussed

Implementation and evaluation of a bony structure suppression software tool for chest X-ray imaging

Includes abstract.Includes bibliographical references.This project proposed to implement a bony structure suppression tool and analyse its effects on a texture-based classification algorithm in order to assist in the analysis of chest X-ray images. The diagnosis of pulmonary tuberculosis (TB) often includes the evaluation of chest X-ray images, and the reliability of image interpretation depends upon the experience of the radiologist. Computer-aided diagnosis (CAD) may be used to increase the accuracy of diagnosis. Overlapping structures in chest X-ray images hinder the ability of lung texture analysis for CAD to detect abnormalities. This dissertation examines whether the performance of texturebased CAD tools may be improved by the suppression of bony structures, particularly of the ribs, in the chest region

COVID-CBR: a deep learning architecture featuring case-based reasoning for classification of COVID-19 from chest x-ray images

Background and Objectives: This study aims to assist rapid accurate diagnosis of COVID-19 based on chest x-ray (CXR) images to provide supplementary information, leading to screening program for early detection of COVID-19 based on CXR images by developing an interpretable, robust and performant AI system. Methods: A case-based reasoning approach built upon autoencoder deep learning architecture is applied to classify COVID-19 from other non-COVID-19 as well as normal subjects from chest x-ray images. The system integrates the interpretation and decision-making together by producing a set of profiles that in appearance resemble the training samples and hence explain the outcome of classifications. Three classes are studied, which are COVID-19 (n=250), other non-COVID-19 diseases (NCD) (n=384), including TB and ARDS, and normal (n=327). Results: This COVID-CBR system sustains the average sensitivity and specificity of 93.1±3.58% and 96.1±4.10% respectively for classification of these three classes. In comparison with the current state of the art, including COVID-Net, VGG-16 and other explainable AI systems, the developed COVID-CBR system appears to perform similar or better when classifying multi-class categories. Conclusion: This paper presents a case-based reasoning deep learning system for detection of COVID-19 from chest x-ray images. Comparison with several state of the art systems is conducted. Although the improvement tends to be marginal, especially for VGG-16, the novelty of this work manifests its interpretable feature building upon case-based reasoning, leading to revealing this viral insight and hence ascertaining more effective treatment and drugs while maintaining being transparent. Furthermore, different from several other current explainable networks that highlight key regions or the points of an input that activate the network, i.e. heat maps, this work is constructed upon whole training images, i.e. case-based, whereby each training image belongs to one of the case clusters

Computed-Tomography (CT) Scan

A computed tomography (CT) scan uses X-rays and a computer to create detailed images of the inside of the body. CT scanners measure, versus different angles, X-ray attenuations when passing through different tissues inside the body through rotation of both X-ray tube and a row of X-ray detectors placed in the gantry. These measurements are then processed using computer algorithms to reconstruct tomographic (cross-sectional) images. CT can produce detailed images of many structures inside the body, including the internal organs, blood vessels, and bones. This book presents a comprehensive overview of CT scanning. Chapters address such topics as instrumental basics, CT imaging in coronavirus, radiation and risk assessment in chest imaging, positron emission tomography (PET), and feature extraction