• 대한의용생체공학회:의공학회지
• /
• 제38권5호
• /
• pp.237-241
• /
• 2017

Early detection of the pulmonary nodule is important for diagnosis and treatment of lung cancer. Recently, CT has been used as a screening tool for lung nodule detection. And, it has been reported that computer aided detection(CAD) systems can improve the accuracy of the radiologist in detection nodules on CT scan. The previous study has been proposed a method using Convolutional Neural Network(CNN) in Lung CAD system. But the proposed model has a limitation in accuracy due to its sparse layer structure. Therefore, we propose a Deep Convolutional Neural Network to overcome this limitation. The model proposed in this work is consist of 14 layers including 8 convolutional layers and 4 fully connected layers. The CNN model is trained and tested with 61,404 regions-of-interest (ROIs) patches of lung image including 39,760 nodules and 21,644 non-nodules extracted from the Lung Image Database Consortium(LIDC) dataset. We could obtain the classification accuracy of 91.79% with the CNN model presented in this work. To prevent overfitting, we trained the model with Augmented Dataset and regularization term in the cost function. With L1, L2 regularization at Training process, we obtained 92.39%, 92.52% of accuracy respectively. And we obtained 93.52% with data augmentation. In conclusion, we could obtain the accuracy of 93.75% with L2 Regularization and Data Augmentation.

• 대한영상의학회지
• /
• 제85권4호
• /
• pp.705-713
• /
• 2024

본 종설에서는 모니터코퍼레이션(주)의 폐결절 검출을 위한 인공지능 기반 컴퓨터 보조 병변 검출(artificial intelligence-based computer-aided detection; 이하 AI-CAD) 소프트웨어 LuCAS-plus의 임상적 활용에 관한 연구와 실제 사용 경험을 기술하였다. AI-CAD의 폐암 검진에 대한 임상시험에서는 민감도와 특이도 측면에서 의료진의 판단과 비슷한 수준의 성능을 보였으며, 악성 종양의 폐전이 진단에 적용한 실증연구에서도 높은 검출 성능을 보여주었다. 또한 폐전이 진단에서 AI-CAD와 결절 매칭 알고리즘을 함께 사용할 경우 위양성 결과를 유의하게 감소시킬 수 있었다. 실제 판독에서도 AI-CAD를 적용함으로써 흉부 CT 판독의 정확성을 향상시키고, 판독에 드는 시간과 노력을 절약할 수 있었다. 종합하면, 폐결절 검출 AI-CAD는 폐암 검진과 악성 종양 경과 관찰 흉부 CT 판독에 유의한 도움을 줄 것으로 기대된다.

• KSII Transactions on Internet and Information Systems (TIIS)
• /
• 제13권1호
• /
• pp.347-370
• /
• 2019

In the computer-aided detection (CAD) system of pulmonary nodules, a high false positive rate is common because the density and the computed tomography (CT) values of the vessel and the nodule in the CT images are similar, which affects the detection accuracy of pulmonary nodules. In this paper, a method of automatic detection of pulmonary nodules based on multi-scale enhancement filters and 3D shape features is proposed. The method uses an iterative threshold and a region growing algorithm to segment lung parenchyma. Two types of multi-scale enhancement filters are constructed to enhance the images of nodules and blood vessels in 3D lung images, and most of the blood vessel images in the nodular images are removed to obtain a suspected nodule image. An 18 neighborhood region growing algorithm is then used to extract the lung nodules. A new pulmonary nodules feature descriptor is proposed, and the features of the suspected nodules are extracted. A support vector machine (SVM) classifier is used to classify the pulmonary nodules. The experimental results show that our method can effectively detect pulmonary nodules and reduce false positive rates, and the feature descriptor proposed in this paper is valid which can be used to distinguish between nodules and blood vessels.

• Radiation Oncology Journal
• /
• 제32권3호
• /
• pp.116-124
• /
• 2014

Purpose: To investigate the frequency and clinical significance of detected incidental lung nodules found on computed tomography (CT) simulation images for hepatocellular carcinoma (HCC) using computer-aided diagnosis (CAD) and a physician review. Materials and Methods: Sixty-seven treatment-$na{\ddot{i}}ve$ HCC patients treated with transcatheter arterial chemoembolization and radiotherapy (RT) were included for the study. Portal phase of simulation CT images was used for CAD analysis and a physician review for lung nodule detection. For automated nodule detection, a commercially available CAD system was used. To assess the performance of lung nodule detection for lung metastasis, the sensitivity, negative predictive value (NPV), and positive predictive value (PPV) were calculated. Results: Forty-six patients had incidental nodules detected by CAD with a total of 109 nodules. Only 20 (18.3%) nodules were considered to be significant nodules by a physician review. The number of significant nodules detected by both of CAD or a physician review was 24 in 9 patients. Lung metastases developed in 11 of 46 patients who had any type of nodule. The sensitivities were 58.3% and 100% based on patient number and on the number of nodules, respectively. The NPVs were 91.4% and 100%, respectively. And the PPVs were 77.8% and 91.7%, respectively. Conclusion: Incidental detection of metastatic nodules was not an uncommon event. From our study, CAD could be applied to CT simulation images allowing for an increase in detection of metastatic nodules.

• 한국정보전자통신기술학회논문지
• /
• 제6권1호
• /
• pp.37-44
• /
• 2013

본 논문에서는 voxel classification을 이용한 폐 결절 자동 검출 시스템을 제안한다. 제안하는 폐 영상 분석 방법은 크게 세 단계로 구성된다. 첫 번째 단계에서는 분석 대상 폐 영역을 분할한다. 그리고 두 번째 단계는 분할된 폐 영역 내에서 폐 구조물을 분할한다. 마지막으로 두 번째 과정에서 분할된 폐결절후보와 폐혈관 voxel을 대상으로 log-polar sampling을 이용한 특징 벡터를 만들고, 특징벡터를 입력 값으로 하여 support vector machine classifier를 이용하여 분석대상 voxel을 폐 결절 voxel과 비결절 voxel로 구분하여 폐 결절을 검출한다.

• 한국정보전자통신기술학회논문지
• /
• 제5권1호
• /
• pp.13-19
• /
• 2012

본 논문에서는 계층적 삼차원 블록 영상분석을 이용한 폐결절 자동 검출 방법을 제안한다. 제안하는 블록 기반 폐 영상 분석 방법은 크게 두 부분으로 나눌 수 있다. 첫 번째는 블록을 분할하고 분석하고자 하는 대상 블록을 선택하는 과정이며 두 번째는 선택된 분석 대상 블록을 분석하는 과정이다. 첫 번째 과정을 통하여 선택된 분석대상 블록들은 다음 단계인 분석과정을 통해 결절과 비결절로 분리될 수 있다. 분석대상 블록의 중심에 있는 object에서 분석을 위한 형태 특징을 추출 하고, 추출된 형태 특징 값을 Support Vector Machine을 이용하여 결절과 비 결절로 분리한다.

• 대한의용생체공학회:의공학회지
• /
• 제11권2호
• /
• pp.209-216
• /
• 1990

Pulmonary nodules in chest X-Ray images were detected using the symmetric property of human lung and its performance was evaluated. Thls algorithm reduced the effect of background components and enhanced the nodule signals relatively. The image was divided and processed separately, the half with matched filter only, and the other half with warping and matched filter. This algorithm increased the entire detection rate by reducing False-Positive error and improving True-Positive detectability. Result shows 10-25 % improvement in detection rate compared with the conventional alsorithm for nodules size of 10mm.

• Korean Journal of Radiology
• /
• 제23권1호
• /
• pp.139-149
• /
• 2022

Objective: To compare the effects of bone suppression imaging using deep learning (BSp-DL) based on a generative adversarial network (GAN) and bone subtraction imaging using a dual energy technique (BSt-DE) on radiologists' performance for pulmonary nodule detection on chest radiographs (CXRs). Materials and Methods: A total of 111 adults, including 49 patients with 83 pulmonary nodules, who underwent both CXR using the dual energy technique and chest CT, were enrolled. Using CT as a reference, two independent radiologists evaluated CXR images for the presence or absence of pulmonary nodules in three reading sessions (standard CXR, BSt-DE CXR, and BSp-DL CXR). Person-wise and nodule-wise performances were assessed using receiver-operating characteristic (ROC) and alternative free-response ROC (AFROC) curve analyses, respectively. Subgroup analyses based on nodule size, location, and the presence of overlapping bones were performed. Results: BSt-DE with an area under the AFROC curve (AUAFROC) of 0.996 and 0.976 for readers 1 and 2, respectively, and BSp-DL with AUAFROC of 0.981 and 0.958, respectively, showed better nodule-wise performance than standard CXR (AUAFROC of 0.907 and 0.808, respectively; p ≤ 0.005). In the person-wise analysis, BSp-DL with an area under the ROC curve (AUROC) of 0.984 and 0.931 for readers 1 and 2, respectively, showed better performance than standard CXR (AUROC of 0.915 and 0.798, respectively; p ≤ 0.011) and comparable performance to BSt-DE (AUROC of 0.988 and 0.974; p ≥ 0.064). BSt-DE and BSp-DL were superior to standard CXR for detecting nodules overlapping with bones (p < 0.017) or in the upper/middle lung zone (p < 0.017). BSt-DE was superior (p < 0.017) to BSp-DL in detecting peripheral and sub-centimeter nodules. Conclusion: BSp-DL (GAN-based bone suppression) showed comparable performance to BSt-DE and can improve radiologists' performance in detecting pulmonary nodules on CXRs. Nevertheless, for better delineation of small and peripheral nodules, further technical improvements are required.

• 자원환경지질
• /
• 제29권4호
• /
• pp.429-437
• /
• 1996

The purpose of this study is to correct the nodule abundance of FFG (Free Fall Grab) sampler on KODOS (Korea Deep Ocean Study) area in North-East Pacific Ocean. The image analysis of sea-floor photography was carried out for correcting the abundance of nodules, and the image enhancement techniques and edge detection method were used to discriminate between nodules and sediments. The trace of nodules on sediments was detected to reduce the fractionation effect in calculating the coverage of nodules. The three methods, using the coverage of nodules, using the volume density, and using corrected volume density, were utilized for the correction of the nodule abundance. The method using the coverage of nodules was more convenient and available for the correction of nodule abundance than the other two methods. The method using the corrected volume density had the highest confidence level compared with the other methods.

• 대한핵의학회지
• /
• 제35권6호
• /
• pp.398-400
• /
• 2001

The authors report a case of unsuspected myocardial ischemia detected during CoDe FDG PET (coincidence detection fluorodeoxyglucose positron emission tomogram) which was performed for the evaluation of a solitary pulmonary nodule. Camera-based FDG PET without attenuation correction often reveals false defect in the inferior wall of the left ventricle in normals due to excessive attenuation. However, this asymptomatic patient had increased uptake in the inferior wall suggesting ischemic myocardium. The scan finding was confirmed by Tl-201 myocardial SPECT and coronary angiogram. The patient then underwent successful PTCA of mild RCA and right ventricular branch followed by right upper lobectomy for small cell lung cancer.