Retinal Vessel Segmentation in Fundoscopic Images with Generative Adversarial Networks
AbstractRetinal vessel segmentation is an indispensable step for automatic detection of retinal diseases with fundoscopic images. Though many approaches have been proposed, existing methods tend to miss fine vessels or allow false positives at terminal branches. Let alone under-segmentation, over-segmentation is also problematic when quantitative studies need to measure the precise width of vessels. In this paper, we present a method that generates the precise map of retinal vessels using generative adversarial training. Our methods achieve dice coefficient of 0.829 on DRIVE dataset and 0.834 on STARE dataset which is the state-of-the-art performance on both datasets.
American Journal of Roentgenology 2017
Computer-Assisted Program using Deep Learning Technique in Determination of Bone Age: Evaluation of the Accuracy and Efficiency
AbstractTo evaluate the accuracy of the new automatic software system for bone age assessment and validate its feasibility in clinical practice.
A Comparative Study of Automatic Hand Bone Age Assessment Systems
AbstractThe bone age assessment is a critical procedure in pediatric radiology for diagnosis of many disorders and assessment of response to treatment. It can be performed by either Greulich and Pyle(GP) or Tanner- Whitehouse(TW) method. Although GP method is convenient to use, it can lead to subjective results. To overcome these limitations, there have been a few trials for automated bone age assessment including commercialized system. To verify the validity of these attempts, we developed Korean based bone age assessment system based on convolutional neural network (CNN) and compared our system with the commercialized system(CS) and Harvard's system(HS).
Deep Learning-Based Content-Based Image Retrieval for Finding HRCT Images of Similar Patients with Interstitial Lung Disease: Validation with 100 Paired HRCTs and Automatic Quantification of Six Disease Patterns
AbstractInterstitial lung diseases (ILDs) represent a major cause of morbidity and mortality. High-resolution computed tomography (HRCT) has become critical to characterize the imaging patterns of ILD, but this approach remains vulnerable to inter- and intra-observer variation. To overcome human variation, automated techniques have been applied for differentiating a variety of obstructive lung diseases based on the features of a density histogram and texture analyses[1-8]. Quantitative assessment of lung parenchymal texture is important to analyze and differentiate regional diseased patterns of ILD, which would lead to content based image retrieval (CBIR). Using deep learning technique with Siamese convolutional neural net (CNN) on raw image itself and classified disease patterns, 3D CBIR at HRCT is potentially useful for diagnosis and decision by retrieval of similar HRCT to referring similar patient previously diagnosed with known treatment response and survival. To address these unmet clinical needs, we have developed DILD CBIR platform, a deep learning-based CBIR system and its evaluation tool with known 100 paired HRCTs of same patient; thus to provide an efficient and reliable quantification for the assessment of CBIR performance for ILD patients.