Baseline Microperimetry and OCT in the RUSH2A Study: Structure-Function Association and Correlation With Disease Severity.

PURPOSE: To investigate baseline mesopic microperimetry (MP) and spectral domain optical coherence tomography (OCT) in the Rate of Progression in USH2A-related Retinal Degeneration (RUSH2A) study. DESIGN: Natural history study METHODS: Setting: 16 clinical sites in Europe and North AmericaStudy Population: Participants with Usher syndrome type 2 (USH2) (N = 80) or autosomal recessive nonsyndromic RP (ARRP) (N = 47) associated with biallelic disease-causing sequence variants in USH2AObservation Procedures: General linear models were used to assess characteristics including disease duration, MP mean sensitivity and OCT intact ellipsoid zone (EZ) area. The associations between mean sensitivity and EZ area with other measures, including best corrected visual acuity (BCVA) and central subfield thickness (CST) within the central 1 mm, were assessed using Spearman correlation coefficients. MAIN OUTCOME MEASURES: Mean sensitivity on MP; EZ area and CST on OCT. RESULTS: All participants (N = 127) had OCT, while MP was obtained at selected sites (N = 93). Participants with Usher syndrome type 2 (USH2, N = 80) and nonsyndromic autosomal recessive Retinitis Pigmentosa (ARRP, N = 47) had the following similar measurements: EZ area (median (interquartile range [IQR]): 1.4 (0.4, 3.1) mm2 vs 2.3 (0.7, 5.7) mm2) and CST (median (IQR): 247 (223, 280) µm vs 261 (246, 288), and mean sensitivity (median (IQR): 3.5 (2.1, 8.4) dB vs 5.1 (2.9, 9.0) dB). Longer disease duration was associated with smaller EZ area (P < 0.001) and lower mean sensitivity (P = 0.01). Better BCVA, larger EZ area, and larger CST were correlated with greater mean sensitivity (r > 0.3 and P < 0.01). Better BCVA and larger CST were associated with larger EZ area (r > 0.6 and P < 0.001). CONCLUSIONS: Longer disease duration correlated with more severe retinal structure and function abnormalities, and there were associations between MP and OCT metrics. Monitoring changes in retinal structure-function relationships during disease progression will provide important insights into disease mechanism in USH2A-related retinal degeneration.

Segmentation of Pancreatic Subregions in Computed Tomography Images.

The accurate segmentation of pancreatic subregions (head, body, and tail) in CT images provides an opportunity to examine the local morphological and textural changes in the pancreas. Quantifying such changes aids in understanding the spatial heterogeneity of the pancreas and assists in the diagnosis and treatment planning of pancreatic cancer. Manual outlining of pancreatic subregions is tedious, time-consuming, and prone to subjective inconsistency. This paper presents a multistage anatomy-guided framework for accurate and automatic 3D segmentation of pancreatic subregions in CT images. Using the delineated pancreas, two soft-label maps were estimated for subregional segmentation-one by training a fully supervised naïve Bayes model that considers the length and volumetric proportions of each subregional structure based on their anatomical arrangement, and the other by using the conventional deep learning U-Net architecture for 3D segmentation. The U-Net model then estimates the joint probability of the two maps and performs optimal segmentation of subregions. Model performance was assessed using three datasets of contrast-enhanced abdominal CT scans: one public NIH dataset of the healthy pancreas, and two datasets D1 and D2 (one for each of pre-cancerous and cancerous pancreas). The model demonstrated excellent performance during the multifold cross-validation using the NIH dataset, and external validation using D1 and D2. To the best of our knowledge, this is the first automated model for the segmentation of pancreatic subregions in CT images. A dataset consisting of reference anatomical labels for subregions in all images of the NIH dataset is also established.