Expert Diagnosis of Plus Disease in Retinopathy of Prematurity From Computer-Based Image Analysis.

IMPORTANCE: Published definitions of plus disease in retinopathy of prematurity (ROP) reference arterial tortuosity and venous dilation within the posterior pole based on a standard published photograph. One possible explanation for limited interexpert reliability for a diagnosis of plus disease is that experts deviate from the published definitions. OBJECTIVE: To identify vascular features used by experts for diagnosis of plus disease through quantitative image analysis. DESIGN, SETTING, AND PARTICIPANTS: A computer-based image analysis system (Imaging and Informatics in ROP [i-ROP]) was developed using a set of 77 digital fundus images, and the system was designed to classify images compared with a reference standard diagnosis (RSD). System performance was analyzed as a function of the field of view (circular crops with a radius of 1-6 disc diameters) and vessel subtype (arteries only, veins only, or all vessels). Routine ROP screening was conducted from June 29, 2011, to October 14, 2014, in neonatal intensive care units at 8 academic institutions, with a subset of 73 images independently classified by 11 ROP experts for validation. The RSD was compared with the majority diagnosis of experts. MAIN OUTCOMES AND MEASURES: The primary outcome measure was the percentage of accuracy of the i-ROP system classification of plus disease, with the RSD as a function of the field of view and vessel type. Secondary outcome measures included the accuracy of the 11 experts compared with the RSD. RESULTS: Accuracy of plus disease diagnosis by the i-ROP computer-based system was highest (95%; 95% CI, 94%-95%) when it incorporated vascular tortuosity from both arteries and veins and with the widest field of view (6-disc diameter radius). Accuracy was 90% or less when using only arterial tortuosity and 85% or less using a 2- to 3-disc diameter view similar to the standard published photograph. Diagnostic accuracy of the i-ROP system (95%) was comparable to that of 11 expert physicians (mean 87%, range 79%-99%). CONCLUSIONS AND RELEVANCE: Experts in ROP appear to consider findings from beyond the posterior retina when diagnosing plus disease and consider tortuosity of both arteries and veins, in contrast with published definitions. It is feasible for a computer-based image analysis system to perform comparably with ROP experts, using manually segmented images.

Toward a severity index for ROP: An unsupervised approach.

Retinopathy of prematurity (ROP) is a disease affecting low birth-weight infants and is the major cause of childhood blindness. Although accurate diagnosis is important, there is a high variability among expert decisions mostly due to subjective thresholds. Existing work focused on automated diagnosis of ROP. In this study, we construct a continuous severity index as an alternative to discrete classification. We follow an unsupervised approach by performing nonlinear dimensionality reduction. Instead of extracting several statistics of image features, each image is represented by the probability distribution of its features. The distance between distributions are then used in manifold learning methods as the distance between samples. The experiments are carried out on a dataset of 104 wide-angle retinal images. The results are promising and they reflect the challenges of the discrete classification.

Computer-Based Image Analysis for Plus Disease Diagnosis in Retinopathy of Prematurity: Performance of the "i-ROP" System and Image Features Associated With Expert Diagnosis.

PURPOSE: We developed and evaluated the performance of a novel computer-based image analysis system for grading plus disease in retinopathy of prematurity (ROP), and identified the image features, shapes, and sizes that best correlate with expert diagnosis. METHODS: A dataset of 77 wide-angle retinal images from infants screened for ROP was collected. A reference standard diagnosis was determined for each image by combining image grading from 3 experts with the clinical diagnosis from ophthalmoscopic examination. Manually segmented images were cropped into a range of shapes and sizes, and a computer algorithm was developed to extract tortuosity and dilation features from arteries and veins. Each feature was fed into our system to identify the set of characteristics that yielded the highest-performing system compared to the reference standard, which we refer to as the "i-ROP" system. RESULTS: Among the tested crop shapes, sizes, and measured features, point-based measurements of arterial and venous tortuosity (combined), and a large circular cropped image (with radius 6 times the disc diameter), provided the highest diagnostic accuracy. The i-ROP system achieved 95% accuracy for classifying preplus and plus disease compared to the reference standard. This was comparable to the performance of the 3 individual experts (96%, 94%, 92%), and significantly higher than the mean performance of 31 nonexperts (81%). CONCLUSIONS: This comprehensive analysis of computer-based plus disease suggests that it may be feasible to develop a fully-automated system based on wide-angle retinal images that performs comparably to expert graders at three-level plus disease discrimination. TRANSLATIONAL RELEVANCE: Computer-based image analysis, using objective and quantitative retinal vascular features, has potential to complement clinical ROP diagnosis by ophthalmologists.

Manifold Learning by Preserving Distance Orders.

Nonlinear dimensionality reduction is essential for the analysis and the interpretation of high dimensional data sets. In this manuscript, we propose a distance order preserving manifold learning algorithm that extends the basic mean-squared error cost function used mainly in multidimensional scaling (MDS)-based methods. We develop a constrained optimization problem by assuming explicit constraints on the order of distances in the low-dimensional space. In this optimization problem, as a generalization of MDS, instead of forcing a linear relationship between the distances in the high-dimensional original and low-dimensional projection space, we learn a non-decreasing relation approximated by radial basis functions. We compare the proposed method with existing manifold learning algorithms using synthetic datasets based on the commonly used residual variance and proposed percentage of violated distance orders metrics. We also perform experiments on a retinal image dataset used in Retinopathy of Prematurity (ROP) diagnosis.

Plus disease diagnosis in retinopathy of prematurity: vascular tortuosity as a function of distance from optic disk.

PURPOSE: To examine vascular tortuosity as a function of distance from the optic disk in infants with retinopathy of prematurity. METHODS: Thirty-four wide-angle retinal images from infants with retinopathy of prematurity were reviewed by 22 experts. A reference standard for each image was defined as the diagnosis (plus vs. not plus) given by the majority of experts. Tortuosity, defined as vessel length divided by straight line distance between vessel end points, was calculated as a function of distance from the disk margin for arteries and veins using computer-based methods developed by the authors. RESULTS: Mean cumulative tortuosity increased with distance from the disk margin, both in 13 images with plus disease (P = 0.007 for arterial tortuosity [n = 62 arteries], P < 0.001 for venous tortuosity [n = 58 veins] based on slope of best fit line by regression), and in 21 images without plus disease (P < 0.001 for arterial tortuosity [n = 94 arteries], P <0 .001 for venous tortuosity [n = 85 veins]). Images with plus disease had significantly higher vascular tortuosity than images without plus disease (P < 0.05), up to 7.0 disk diameters from the optic disk margin. CONCLUSION: Vascular tortuosity was higher peripherally than centrally, both in images with and without plus disease, suggesting that peripheral retinal features may be relevant for retinopathy of prematurity diagnosis.

Level Sets for Retinal Vasculature Segmentation Using Seeds from Ridges and Edges from Phase Maps.

In this paper, we present a novel modification to level set based automatic retinal vasculature segmentation approaches. The method introduces ridge sample extraction for sampling the vasculature centerline and phase map based edge detection for accurate region boundary detection. Segmenting the vasculature in fundus images has been generally challenging for level set methods employing classical edge-detection methodologies. Furthermore, initialization with seed points determined by sampling vessel centerlines using ridge identification makes the method completely automated. The resulting algorithm is able to segment vasculature in fundus imagery accurately and automatically. Quantitative results supplemented with visual ones support this observation. The methodology could be applied to the broader class of vessel segmentation problems encountered in medical image analytics.

Motion flow analysis in cell videos using a multi-level clustering method.

Analyzing motion flow of cells is an important task for many biomedical applications. It is a challenging problem due to noise in images and uncontrolled motion of cells. In this study, a method to find regions of organized motion and direction of flow is proposed. Since dense optical flow methods might fail due to homogeneous regions and irregular motion patterns, the technique involves analyzing trajectories of strong corner features. Trajectories are clustered to find dominant flow patterns for different regions of the frame, where a multilevel clustering scheme is followed. Experiments show that the technique gives accurate results for detecting region and direction of flow.

Towards respiration management in radiation treatment of lung tumors: transferring regions of interest from planning CT to kilovoltage X-ray images.

Tracking of lung tumors is imperative for improved radiotherapy treatment. However, the motion of the thoracic organs makes it a complicated task. 4D CT images acquired prior to treatment provide valuable information regarding the motion of organs and tumor, since it is manually annotated. In order to track tumors using treatment-day X-ray images (kV images), we need to find the correspondence with CT images so that projection of tumor region of interest will provide a good estimate about the position of the tumor on the X-ray image. In this study, we propose a method to estimate the alignment and respiration phase corresponding to X-ray images using 4D CT data. Our approach generates Digitally Reconstructed Radiographs (DRRs) using bilateral filter smoothing and computes rigid registration with kV images since the position and orientation of patient might differ between CT and treatment-day image acquisition processes. Instead of using landmark points, our registration method makes use of Kernel Density Estimation over the edges that are not affected much by respiration. To estimate the phase of X-ray, we apply template matching techniques between the lung regions of X-ray and registered DRRs. Our approach gives accurate results for rigid registration and provides a starting point to track tumors using the X-ray images during the treatment.