• Journal of Biomedical Engineering Research
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• v.10 no.1
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• pp.1-10
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• 1989

An automatic lung nodule detection algorithm was applied for digital radiographic images using Bit Slice Processor. In this algorithm, signal enhancing filtering and signal suppressing filtering were performed on the given digital chest image, respectively. Then we grit the dirt- frrence image from these filtered images, and hi-level island images were obtained by applying various threshold values. From the island images, we decided the suspicious nodules using size and circularity test, and marked them to alert radiologists. The performance of the atgorithm was analyzed with respect to the size, contrast and position of digitally synthesized nodules. This method presented 45.8% of true positive ratio for the nodules of lOw in diameter with 12-16 pixel value differnces.

• Tuberculosis and Respiratory Diseases
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• v.41 no.4
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• pp.435-439
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• 1994

Lung cancer is the most common fatal malignant lesion in both sexes. Detection of the solitary pulmonary nodule is important because surgical series up to a third of solitary pulmonary nodules are bronchogenic carcinoma. Bronchioloalveolar cell carcinoma is a rare primary lung cancer and surgery is treatment of choice in brochioloalveolar cell carcinoma. We experinced a case of bronchioloalveolar cell carcinoma in solitary pulmonary nodule with cavitary lesion in chest CT scan, which is an uncommon finding in brochioloalveolar cell carcinoma.

• Journal of Chest Surgery
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• v.54 no.6
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• pp.494-499
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• 2021

Background: A recent increase in the incidental detection of ground glass nodules (GGNs) has created a need for improved diagnostic accuracy in screening for malignancies. However, surgical diagnosis remains challenging, especially via video-assisted thoracoscopic surgery (VATS). Herein, we present the efficacy of a novel electrical navigation system for perioperative percutaneous transthoracic nodule localization. Methods: Eighteen patients with GGNs who underwent electromagnetic navigated percutaneous transthoracic needle localization (ETTNL), followed by 1-stage diagnostic wedge resections via VATS between January and December 2020, were included in the analysis. Data on patient characteristics, nodules, procedures, and pathological diagnoses were collected and retrospectively reviewed. Results: Of the 18 nodules, 17 were successfully localized. Nine nodules were pure GGNs, and the remaining 9 were part-solid GGNs. The median nodule size was 9.0 mm (range, 4.0-20.0 mm); and the median depth from the visceral pleura was 5.2 mm (range, 0.0-14.4 mm). The median procedure time was 10 minutes (range, 7-20 minutes). The final pathologic results showed benign lesions in 3 cases and malignant lesions in 15 cases. Conclusion: Perioperative ETTNL appears to be an effective method for the localization of GGNs, providing guidance for a 1-stage VATS procedure.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.12 no.2
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• pp.137-142
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• 2012

The temporal subtraction technique as one of computer aided diagnosis has been introduced in medical fields to enhance the interval changes such as formation of new lesions and changes in existing abnormalities on deference image. With the temporal subtraction technique radiologists can easily detect lung nodules on visual screening. Until now, two-dimensional temporal subtraction imaging technique has been introduced for the clinical test. We have developed new temporal subtraction method to remove the subtraction artifacts which is caused by mis-registration on temporal subtraction images of lungs on MDCT images. In this paper, we propose a new computer aided diagnosis scheme for automatic enhancing the lung nodules from the temporal subtraction of thoracic MDCT images. At first, the candidates regions included nodules are detected by the multiple threshold technique in terms of the pixel value on the temporal subtraction images. Then, a rule-base method and artificial neural networks is utilized to remove the false positives of nodule candidates which is obtained temporal subtraction images. We have applied our detection of lung nodules to 30 thoracic MDCT image sets including lung nodules. With the detection method, satisfactory experimental results are obtained. Some experimental results are shown with discussion.

• Proceedings of the Korean Society of Medical Physics Conference
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• 2006.08a
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• pp.144-144
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• 2006

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.14 no.3
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• pp.1104-1120
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• 2020

The chest X-rays are a common way to diagnose lung cancer or pneumonia. In particular, the finding of a lung nodule is the most important problem in the early detection of lung cancer. Recently, a lot of automatic diagnosis algorithms have been studied to find the lung nodules missed by doctors. The algorithms are typically based on segmentation network like U-Net. However, the occurrence of false positives that similar to lung nodules present outside the lungs can severely degrade performance. In this study, we propose a multi-task learning method that simultaneously learns the lung region and nodule-labeled data based on the prior knowledge that lung nodules exist only in the lung. The proposed method significantly reduces false positives outside the lung and improves the recognition rate of lung nodules to 83.8 F1 score compared to 66.6 F1 score of single task learning with U-net model. The experimental results on the JSRT public dataset demonstrate the effectiveness of the proposed method compared with other baseline methods.

• IEMEK Journal of Embedded Systems and Applications
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• v.17 no.5
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• pp.265-272
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• 2022

As the risk of lung cancer has increased, early-stage detection and treatment of cancers have received a lot of attention. Among various medical imaging approaches, computer tomography (CT) has been widely utilized to examine the size and growth rate of lung nodules. However, the process of manual examination is a time-consuming task, and it causes physical and mental fatigue for medical professionals. Recently, many computer-aided diagnostic methods have been proposed to reduce the workload of medical professionals. In recent studies, encoder-decoder architectures have shown reliable performances in medical image segmentation, and it is adopted to predict lesion candidates. However, localizing nodules in lung CT images is a challenging problem due to the extremely small sizes and unstructured shapes of nodules. To solve these problems, we utilize atrous spatial pyramid pooling (ASPP) to minimize the loss of information for a general U-Net baseline model to extract rich representations from various receptive fields. Moreover, we propose mixed-up attention mechanism of reverse, boundary and convolutional block attention module (CBAM) to improve the accuracy of segmentation small scale of various shapes. The performance of the proposed model is compared with several previous attention mechanisms on the LIDC-IDRI dataset, and experimental results demonstrate that reverse, boundary, and CBAM (RB-CBAM) are effective in the segmentation of small nodules.

• The Korean Journal of Nuclear Medicine
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• v.25 no.2
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• pp.280-285
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• 1991

We analyzed Radioisotope scan findings of 46 patients of thyroiditis which were proven pathologically at K.C.H. The results were as follows 1) 45 patients were female, one was male and average age of patients was 37 years old. 2) The lesion site was predominant in both lobe (67%) 3) Hashimoto's thyroiditis showed enlarged thyroid (85%) with cold nodule (20%), diffuse decreased activity (10%), while subacute thyroiditis was presented absent activity (53%), poor visualization (20%) or cold nodule (7%) 4) Radioisotope scan was valuable in evaluations function of thyroid gland and detection of lesion but there was a limit of pathological nature.

• Proceedings of the KSMRM Conference
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• 2003.10a
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• pp.22-22
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• 2003

The purpose of this study was to compare the detection rate of brain metastasis according to size of nodule between 1.5T and 3.0T MRI 대상 및 방법： We reviewed 44 patients with primary tumors and clinical symptoms suggesting brain metastasis. After administration of double dose gadolinium-DTPA, MR imaging was performed with 3D SPGR sequence by 3.0T MRI and then with T1 SE sequence by 1.5T MRI. Consequently, comparison was done in 1.5T T1 SE sequence and 3.0T 3D SPGR sequence. With use of the signal intensity (SI) measurements in the metastatic nodules and adjacent tissue, metastatic nodule-to-adjacent tissue SI ratio were calculated. In each patient, the number of metastatic lesions detected in 1.5T and 3.0T, and their size were assessed qualitatively by three blinded readers.

• Korean Journal of Radiology
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• v.24 no.2
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• pp.155-165
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• 2023

Objective: Little is known about the effects of using different expert-determined reference standards when evaluating the performance of deep learning-based automatic detection (DLAD) models and their added value to radiologists. We assessed the concordance of expert-determined standards with a clinical gold standard (herein, pathological confirmation) and the effects of different expert-determined reference standards on the estimates of radiologists' diagnostic performance to detect malignant pulmonary nodules on chest radiographs with and without the assistance of a DLAD model. Materials and Methods: This study included chest radiographs from 50 patients with pathologically proven lung cancer and 50 controls. Five expert-determined standards were constructed using the interpretations of 10 experts: individual judgment by the most experienced expert, majority vote, consensus judgments of two and three experts, and a latent class analysis (LCA) model. In separate reader tests, additional 10 radiologists independently interpreted the radiographs and then assisted with the DLAD model. Their diagnostic performance was estimated using the clinical gold standard and various expert-determined standards as the reference standard, and the results were compared using the t test with Bonferroni correction. Results: The LCA model (sensitivity, 72.6%; specificity, 100%) was most similar to the clinical gold standard. When expert-determined standards were used, the sensitivities of radiologists and DLAD model alone were overestimated, and their specificities were underestimated (all p-values < 0.05). DLAD assistance diminished the overestimation of sensitivity but exaggerated the underestimation of specificity (all p-values < 0.001). The DLAD model improved sensitivity and specificity to a greater extent when using the clinical gold standard than when using the expert-determined standards (all p-values < 0.001), except for sensitivity with the LCA model (p = 0.094). Conclusion: The LCA model was most similar to the clinical gold standard for malignant pulmonary nodule detection on chest radiographs. Expert-determined standards caused bias in measuring the diagnostic performance of the artificial intelligence model.