For the last 20 years, a great amount of evidence has accumulated through epidemiological studies that most of the dry eye disease encountered in daily life, especially in video display terminal (VDT) workers, involves short tear film breakup time (TFBUT) type dry eye, a category characterized by severe symptoms but minimal clinical signs other than short TFBUT. An unstable tear film also affects the visual function, possibly due to the increase of higher order aberrations. Based on the change in the understanding of the types, symptoms, and signs of dry eye disease, the Asia Dry Eye Society agreed to the following definition of dry eye: "Dry eye is a multifactorial disease characterized by unstable tear film causing a variety of symptoms and/or visual impairment, potentially accompanied by ocular surface damage." The definition stresses instability of the tear film as well as the importance of visual impairment, highlighting an essential role for TFBUT assessment. This paper discusses the concept of Tear Film Oriented Therapy (TFOT), which evolved from the definition of dry eye, emphasizing the importance of a stable tear film.

Purpose In this study, we proposed an automated deep learning (DL) method for head and neck cancer (HNC) gross tumor volume (GTV) contouring on positron emission tomography-computed tomography (PET-CT) images. Materials and Methods PET-CT images were collected from 22 newly diagnosed HNC patients, of whom 17 (Database 1) and 5 (Database 2) were from two centers, respectively. An oncologist and a radiologist decided the gold standard of GTV manually by consensus. We developed a deep convolutional neural network (DCNN) and trained the network based on the two-dimensional PET-CT images and the gold standard of GTV in the training dataset. We did two experiments: Experiment 1, with Database 1 only, and Experiment 2, with both Databases 1 and 2. In both Experiment 1 and Experiment 2, we evaluated the proposed method using a leave-one-out cross-validation strategy. We compared the median results in Experiment 2 (GTVa) with the performance of other methods in the literature and with the gold standard (GTVm). Results A tumor segmentation task for a patient on coregistered PET-CT images took less than one minute. The dice similarity coefficient (DSC) of the proposed method in Experiment 1 and Experiment 2 was 0.481∼0.872 and 0.482∼0.868, respectively. The DSC of GTVa was better than that in previous studies. A high correlation was found between GTVa and GTVm (R = 0.99, P < 0.001). The median volume difference (%) between GTVm and GTVa was 10.9%. The median values of DSC, sensitivity, and precision of GTVa were 0.785, 0.764, and 0.789, respectively. Conclusion A fully automatic GTV contouring method for HNC based on DCNN and PET-CT from dual centers has been successfully proposed with high accuracy and efficiency. Our proposed method is of help to the clinicians in HNC management.

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BackgroundDeep vein thrombosis (DVT) is a common but elusive illness that can result in long-term disability or death. Accurate detection of thrombosis and assessment of its size and distribution are critical for treatment decision-making. In the present study, we sought to develop and evaluate a cardiovascular magnetic resonance (CMR) black-blood thrombus imaging (BTI) technique, based on delay alternating with nutation for tailored excitation black-blood preparation and variable flip angle turbo-spin-echo readout, for the diagnosis of non-acute DVT.MethodsThis prospective study was approved by institutional review board and informed consent obtained from all subjects. BTI was first conducted in 11 healthy subjects for parameter optimization and then conducted in 18 non-acute DVT patients to evaluate its diagnostic performance. Two clinically used CMR techniques, contrast-enhanced CMR venography (CE-MRV) and three dimensional magnetization prepared rapid acquisition gradient echo (MPRAGE), were also conducted in all patients for comparison. All images obtained from patients were analyzed on a per-segment basis. Using the consensus diagnosis of CE-MRV as the reference, the sensitivity (SE), specificity (SP), positive and negative predictive values (PPV and NPV), and accuracy (ACC) of BTI and MPRAGE as well as their diagnostic agreement with CE-MRV were calculated. Besides, diagnostic confidence and interreader diagnostic agreement were evaluated for all three techniques.ResultsBTI with optimized parameters effectively nulled the venous blood flow signal and allowed directly visualizing the thrombus within the black-blood lumen. Higher SE (90.4% vs 67.6%), SP (99.0% vs. 97.4%), PPV (95.4% vs. 85.6%), NPV (97.8% vs 92.9%) and ACC (97.4% vs. 91.8%) were obtained by BTI in comparison with MPRAGE. Good diagnostic confidence and excellent diagnostic and interreader agreements were achieved by BTI, which were superior to MPRAGE on detecting the chronic thrombus.ConclusionBTI allows direct visualization of non-acute DVT within the dark venous lumen and has the potential to be a reliable diagnostic tool without the use of contrast medium.

Purpose To develop an automated classification system using a machine learning classifier to distinguish clinically unaffected eyes in patients with keratoconus from a normal control population based on a combination of Scheimpflug camera images and ultra-high-resolution optical coherence tomography (UHR-OCT) imaging data. Methods A total of 121 eyes from 121 participants were classified by 2 cornea experts into 3 groups: normal (50 eyes), with keratoconus (38 eyes) or with subclinical keratoconus (33 eyes). All eyes were imaged with a Scheimpflug camera and UHR-OCT. Corneal morphological features were extracted from the imaging data. A neural network was used to train a model based on these features to distinguish the eyes with subclinical keratoconus from normal eyes. Fisher’s score was used to rank the differentiable power of each feature. The receiver operating characteristic (ROC) curves were calculated to obtain the area under the ROC curves (AUCs). Results The developed classification model used to combine all features from the Scheimpflug camera and UHR-OCT dramatically improved the differentiable power to discriminate between normal eyes and eyes with subclinical keratoconus (AUC = 0.93). The variation in the thickness profile within each individual in the corneal epithelium extracted from UHR-OCT imaging ranked the highest in differentiating eyes with subclinical keratoconus from normal eyes. Conclusion The automated classification system using machine learning based on the combination of Scheimpflug camera data and UHR-OCT imaging data showed excellent performance in discriminating eyes with subclinical keratoconus from normal eyes. The epithelial features extracted from the OCT images were the most valuable in the discrimination process. This classification system has the potential to improve the differentiable power of subclinical keratoconus and the efficiency of keratoconus screening.

In this study, we proposed an automated method based on convolutional neural network (CNN) for nasopharyngeal carcinoma (NPC) segmentation on dual-sequence magnetic resonance imaging (MRI). T1-weighted (T1W) and T2-weighted (T2W) MRI images were collected from 44 NPC patients. We developed a dense connectivity embedding U-net (DEU) and trained the network based on the two-dimensional dual-sequence MRI images in the training dataset and applied post-processing to remove the false positive results. In order to justify the effectiveness of dual-sequence MRI images, we performed an experiment with different inputs in eight randomly selected patients. We evaluated DEU's performance by using a 10-fold cross-validation strategy and compared the results with the previous studies. The Dice similarity coefficient (DSC) of the method using only T1W, only T2W and dual-sequence of 10-fold cross-validation as different inputs were 0.620 ± 0.0642, 0.642 ± 0.118 and 0.721 ± 0.036, respectively. The median DSC in 10-fold cross-validation experiment with DEU was 0.735. The average DSC of seven external subjects was 0.87. To summarize, we successfully proposed and verified a fully automatic NPC segmentation method based on DEU and dual-sequence MRI images with accurate and stable performance. If further verified, our proposed method would be of use in clinical practice of NPC.

Purpose-To test the feasibility of measuring the entire thickness profiles of the epithelium and contact lens in vivo, using high speed and high resolution spectral domain optical coherence tomography (SD-OCT).Methods-A custom-built, long scan depth SD-OCT was developed based on a CMOS camera and the axial resolution was about 5.1 µm in tissue. Five eyes of 5 subjects were imaged twice across the horizontal meridian before and while wearing one contact lens (CL). Semi-automatic measurement was done to yield the entire thickness profiles of the epithelium, total cornea, and contact lens after correcting for optical distortion.Results-The full width and depth of the epithelium, ocular surface and contact lens were clearly visualized. The epithelial thickness (ET) at the center was 51.9 ± 3.5 µm, it remained at this thickness across the central 7 mm diameter and then increased at both temporal and nasal peripheries. The contact lens profile showed the thinnest point at the center with thickness of 100.3 ± 4.9 µm. The thickness increased towards the mid-periphery and then decreased at the edge.Conclusions-This pilot study demonstrated the feasibility of using high speed CMOS-based OCT to evaluate the entire thickness profiles of the epithelium and contact lens in vivo. Further development will be needed to extend the scanning from 2D to 3D with a robust automatic image processing ability.