We investigated the feasibility of a new deep-learning (DL)-based lung analysis method for the evaluation of interstitial lung disease (ILD) by comparing it with evaluation using the traditional computer-aided diagnosis (CAD) system and patients’ clinical outcomes. We prospectively included 104 patients (84 with and 20 without ILD). An expert radiologist defined regions of interest in the typical areas of normal, ground-glass opacity, consolidation, consolidation with fibrosis (traction bronchiectasis), honeycombing, reticulation, traction bronchiectasis, and emphysema, and compared them with the CAD and DL-based analysis results. Next, we measured the extent of ILD lesions with the CAD and DL-based analysis and compared them. Finally, we compared the lesion extent on computed tomography (CT) images, as measured with the DL-based analysis, with pulmonary function tests results and patients’ overall survival. Pearson’s correlation analysis revealed a significant correlation between DL-based analysis and CAD results. Forced vital capacity was significantly correlated with DL-based analysis (r = 0.789, p < 0.001 for normal lung volume and r = −0.316, p = 0.001 for consolidation with fibrosis volume). Consolidation with fibrosis measured using DL-based analysis was independently associated with poor survival. The lesion extent measured using DL-based analysis showed a negative correlation with the pulmonary function test results and prognosis.
Previous work used phantoms to calibrate the nonlinear relationship between the gadolinium contrast concentration and the intensity of the magnetic resonance imaging signal. This work proposes a new nonlinear calibration procedure without phantoms and considers the variation of contrast agent mass minimum combined with the multiple input blood flow system. This also proposes a new single-input method with meaningful variables that is not influenced by reperfusion or noise generated by aliasing. The reperfusion in the lung is usually neglected and is not considered by the indicator dilution method. However, in cases of lung cancer, reperfusion cannot be neglected. A new multiple input method is formulated, and the contribution of the pulmonary artery and bronchial artery to lung perfusion can be considered and evaluated separately.
Methods:The calibration procedure applies the minimum variation of contrast agent mass in 3 different regions: (1) pulmonary artery, (2) left atrium, and (3) aorta. It was compared with four dimensional computerized tomography with iodine, which has a very high proportional relationship between contrast agent concentration and signal intensity.Results: Nonlinear calibration was performed without phantoms, and it is in the range of phantom calibration. It successfully separated the contributions of the pulmonary and bronchial arteries. The proposed multiple input method was verified in 6 subjects with lung cancer, and perfusion from the bronchial artery, rich in oxygen, was identified as very high in the cancer region.Conclusions: Nonlinear calibration of the contrast agent without phantoms is possible. Separate contributions of the pulmonary artery and aorta can be determined.
Industrial properties of aluminum alloys are usually increased by having additives in a liquid state which leads to crystal grain re nement. Conventional methods to con rm the effect of the miniaturization are binarization of electron microscope images and electron probe micro analyzer EPMA which require enormous man hours. The purpose of this research is to establish a method for classifying crystals in signi cantly shorter period. The proposed method is crystal classi cation per pixel on microscopic images by applying deep learning. In the experiment, the proposed method was performed on Al Si Mg alloys with an abundance ratio of 88:11:1, and the results were compared with the correct values created by the conventional EPMA and manual methods. The percentage of accuracy the intersection over union IoU came out lower than 30 in Mg, but approximately 90 in Si. Therefore, the study shows that this method can be applied with high accuracy if the element is presented in about 10 of the image.
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