No abstract

Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

The experiment results demonstrate the potential clinical applications of this proposed method. This technique can offer physicians an objective second opinion, and reduce their heavy workload so as to avoid misdiagnosis causes because of excessive fatigue. In addition, it is easy and reproducible for a person without medical expertise to diagnose thyroid nodules.

Objective:To evaluate the degree of microvascular impairment in DR using multifractal and lacunarity analyses and to compare the diagnostic ability between traditional Euclidean measures (fovea avascular zone area and vessel density) and fractal geometric features. Methods:This retrospective cross-sectional study included a total of 143 eyes of 94 patients with different stages of DR. The retinal microvasculature was imaged by projection removed OCTA. We examined the degree of association between fractal metrics of the retinal microvasculature and DR severity. The area under the ROC curve was used to estimate the diagnostic performance. Results:With increasing DR severity, the multifractal spectrum shifted toward the left bottom and exhibited less left skewness and asymmetry. The vessel density, multifractal features, and lacunarity measured from the DCP were strongly associated with DR severity. The multifractal feature D 5 showed the highest diagnostic ability.The combination of multifractal features further improved the discriminating power. Conclusions: Multifractal and lacunarity analyses can be potentially valuable toolsfor assessment of microvascular impairments in DR. Multifractal geometric parameters exhibit a better discriminatory performance than Euclidean measures, particularly for detection of the early stages of DR. K E Y W O R D S diabetic retinopathy, lacunarity, microvascular network, multifractal, optical coherence tomography angiography S U PP O RTI N G I N FO R M ATI O N Additional supporting information may be found online in the Supporting Information section at the end of the article. How to cite this article: Zhu T, Ma J, Li J, et al. Multifractal and lacunarity analyses of microvascular morphology in eyes with diabetic retinopathy: A projection artifact resolved optical coherence tomography angiography study.

Purpose: Early detection of pulmonary nodules is an effective way to improve patients' chances of survival. In this work, we propose a novel and efficient way to build a computer-aided detection (CAD) system for pulmonary nodules based on computed tomography (CT) scans. Methods: The system can be roughly divided into two steps: nodule candidate detection and false positive reduction. Considering the three-dimensional (3D) nature of nodules, the CAD system adopts 3D convolutional neural networks (CNNs) in both stages. Specifically, in the first stage, a segmentation-based 3D CNN with a hybrid loss is designed to segment nodules. According to the probability maps produced by the segmentation network, a threshold method and connected component analysis are applied to generate nodule candidates. In the second stage, we employ three classification-based 3D CNNs with different types of inputs to reduce false positives. In addition to simple raw data input, we also introduce hybrid inputs to make better use of the output of the previous segmentation network.In experiments, we use data augmentation and batch normalization to avoid overfitting. Results: We evaluate the system on 888 CT scans from the publicly available LIDC-IDRI dataset, and our method achieves the best performance by comparing with the state-of-the-art methods, which has a high detection sensitivity of 97.5% with an average of only one false positive per scan. An additional evaluation on 115 CT scans from local hospitals is also performed. Conclusions: Experimental results demonstrate that our method is highly suited for the detection of pulmonary nodules.