MATERIALS AND METHODS: The HRCT and MR imaging in 46 cochlear implant patients in our department were reviewed.
RESULTS: Majority of our patients [34 patients (73.9%)] showed normal HRCT of the temporal bone; 5 (10.9%) patients had labyrinthitis ossificans, 2 (4.3%) had Mondini's abnormality and 2 (4.3%) had middle ear effusion. One patient each had high jugular bulb, hypoplasia of the internal auditory canal and single cochlear cavity, respectively.
CONCLUSION: The above findings contribute significantly to our surgical decisions regarding candidacy for surgery, side selection and surgical technique in cochlear implantation.
OBJECTIVES: To evaluate the sensitivity of QLF and OCT in detecting initial dental erosion in vitro.
METHODS: 12 human incisors were embedded in resin except for a window on the buccal surface. Bonding agent was applied to half of the window, creating an exposed and non-exposed area. Baseline measurements were taken with QLF, OCT and surface microhardness. Samples were immersed in orange juice for 60 min and measurements taken stepwise every 10 min. QLF was used to compare the loss of fluorescence between the two areas. The OCT system, OCS1300SS (Thorlabs Ltd.), was used to record the intensity of backscattered light of both areas. Multiple linear regression and paired t test were used to compare the change of the outcome measures.
RESULTS: All 3 instruments demonstrated significant dose responses with the erosive challenge interval (p < 0.05) and a detection threshold of 10 min from baseline. Thereafter, surface microhardness demonstrated significant changes after every 10 min of erosion, QLF at 4 erosive intervals (20, 40, 50 and 60 min) while OCT at only 2 (50 and 60 min).
CONCLUSION: It can be concluded that OCT and QLF were able to detect demineralization after 10 min of erosive challenge and could be used to monitor the progression of demineralization of initial enamel erosion in vitro.
METHODS: The dataset used in this study has been acquired by the Singapore Eye Research Institute (SERI), using CIRRUS TM (Carl Zeiss Meditec, Inc., Dublin, CA, USA) SD-OCT device. The dataset consists of 32 OCT volumes (16 DME and 16 normal cases). Each volume contains 128 B-scans with resolution of 1024 px × 512 px, resulting in more than 3800 images being processed. All SD-OCT volumes are read and assessed by trained graders and identified as normal or DME cases based on evaluation of retinal thickening, hard exudates, intraretinal cystoid space formation, and subretinal fluid. Within the DME sub-set, a large number of lesions has been selected to create a rather complete and diverse DME dataset. This paper presents an automatic classification framework for SD-OCT volumes in order to identify DME versus normal volumes. In this regard, a generic pipeline including pre-processing, feature detection, feature representation, and classification was investigated. More precisely, extraction of histogram of oriented gradients and local binary pattern (LBP) features within a multiresolution approach is used as well as principal component analysis (PCA) and bag of words (BoW) representations.
RESULTS AND CONCLUSION: Besides comparing individual and combined features, different representation approaches and different classifiers are evaluated. The best results are obtained for LBP[Formula: see text] vectors while represented and classified using PCA and a linear-support vector machine (SVM), leading to a sensitivity(SE) and specificity (SP) of 87.5 and 87.5%, respectively.
METHODS: We propose to use Residual Blocks with a 3 × 3 kernel size for local feature extraction and Non-Local Blocks to extract the global features. The Non-Local Block has the ability to extract global features without using a huge number of parameters. The key idea behind the Non-Local Block is to apply matrix multiplications between features on the same feature maps.
RESULTS: We trained and validated the proposed method on the LIDC-IDRI dataset which contains 1018 computed tomography scans. We followed a rigorous procedure for experimental setup, namely tenfold cross-validation, and ignored the nodules that had been annotated by