CAT-Net: A Cross-Slice Attention Transformer Model for Prostate Zonal Segmentation in MRI.

Prostate cancer is the second leading cause of cancer death among men in the United States. The diagnosis of prostate MRI often relies on accurate prostate zonal segmentation. However, state-of-the-art automatic segmentation methods often fail to produce well-contained volumetric segmentation of the prostate zones since certain slices of prostate MRI, such as base and apex slices, are harder to segment than other slices. This difficulty can be overcome by leveraging important multi-scale image-based information from adjacent slices, but current methods do not fully learn and exploit such cross-slice information. In this paper, we propose a novel cross-slice attention mechanism, which we use in a Transformer module to systematically learn cross-slice information at multiple scales. The module can be utilized in any existing deep-learning-based segmentation framework with skip connections. Experiments show that our cross-slice attention is able to capture cross-slice information significant for prostate zonal segmentation in order to improve the performance of current state-of-the-art methods. Cross-slice attention improves segmentation accuracy in the peripheral zones, such that segmentation results are consistent across all the prostate slices (apex, mid-gland, and base). The code for the proposed model is available at https://bit.ly/CAT-Net.

Artificial intelligence-enabled screening for diabetic retinopathy: a real-world, multicenter and prospective study.

INTRODUCTION: Early screening for diabetic retinopathy (DR) with an efficient and scalable method is highly needed to reduce blindness, due to the growing epidemic of diabetes. The aim of the study was to validate an artificial intelligence-enabled DR screening and to investigate the prevalence of DR in adult patients with diabetes in China. RESEARCH DESIGN AND METHODS: The study was prospectively conducted at 155 diabetes centers in China. A non-mydriatic, macula-centered fundus photograph per eye was collected and graded through a deep learning (DL)-based, five-stage DR classification. Images from a randomly selected one-third of participants were used for the DL algorithm validation. RESULTS: In total, 47 269 patients (mean (SD) age, 54.29 (11.60) years) were enrolled. 15 805 randomly selected participants were reviewed by a panel of specialists for DL algorithm validation. The DR grading algorithms had a 83.3% (95% CI: 81.9% to 84.6%) sensitivity and a 92.5% (95% CI: 92.1% to 92.9%) specificity to detect referable DR. The five-stage DR classification performance (concordance: 83.0%) is comparable to the interobserver variability of specialists (concordance: 84.3%). The estimated prevalence in patients with diabetes detected by DL algorithm for any DR, referable DR and vision-threatening DR were 28.8% (95% CI: 28.4% to 29.3%), 24.4% (95% CI: 24.0% to 24.8%) and 10.8% (95% CI: 10.5% to 11.1%), respectively. The prevalence was higher in female, elderly, longer diabetes duration and higher glycated hemoglobin groups. CONCLUSION: This study performed, a nationwide, multicenter, DL-based DR screening and the results indicated the importance and feasibility of DR screening in clinical practice with this system deployed at diabetes centers. TRIAL REGISTRATION NUMBER: NCT04240652.

United snakes.

Since their debut in 1987, snakes (active contour models) have become a standard image analysis technique with several variants now in common use. We present a framework called "United Snakes", which has two key features. First, it unifies the most popular snake variants, including finite difference, B-spline, and Hermite polynomial snakes in a consistent finite element formulation, thus expanding the range of object modeling capabilities within a uniform snake construction process. Second, it embodies the idea that the heretofore presumed competing technique known as "live wire" or "intelligent scissors" is in fact complementary to snakes and that the two techniques can advantageously be combined by introducing an effective hard constraint mechanism. The United Snakes framework amplifies the efficiency and reproducibility of the component techniques, and it offers more flexible interactive control while further minimizing user interactions. We apply United Snakes to several different medical image analysis tasks, including the segmentation of neuronal dendrites in EM images, dynamic chest image analysis, the quantification of growth plates in MR images and the isolation of the breast region in mammograms, demonstrating the generality, accuracy and robustness of the tool.