English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Calcaneus is the largest tarsal bone to withstand the daily stresses ofweight-bearing. The calcaneal fracture is the most common type in the tarsalbone fractures. After a fracture is suspected, plain radiographs should betaken first. Bohler's Angle (BA) and Critical Angle of Gissane (CAG), measuredby four anatomic landmarks in lateral foot radiograph, can guide fracturediagnosis and facilitate operative recovery of the fractured calcaneus. Thisstudy aims to develop an analysis system that can automatically locate fouranatomic landmarks, measure BA and CAG for fracture assessment, identifyfractured calcaneus, and segment fractured regions. For landmark detection, weproposed a coarse-to-fine Rotation-Invariant Regression-Voting (RIRV) landmarkdetection method based on regressive Multi-Layer Perceptron (MLP) and ScaleInvariant Feature Transform (SIFT) patch descriptor, which solves the problemof fickle rotation of calcaneus. By implementing a novel normalizationapproach, the RIRV method is explicitly rotation-invariance comparing withtraditional regressive methods. For fracture identification and segmentation, aconvolution neural network (CNN) based on U-Net with auxiliary classificationhead (U-Net-CH) is designed. The input ROIs of the CNN are normalized bydetected landmarks to uniform view, orientation, and scale. The advantage ofthis approach is the multi-task learning that combines classification andsegmentation. Our system can accurately measure BA and CAG with a mean angleerror of 3.8 and 6.2 respectively. For fracture identification and fractureregion segmentation, our system presents good performance with an F1-score of96.55%, recall of 94.99%, and segmentation IoU-score of 0.586.

Automatic Analysis System of Calcaneus Radiograph: Rotation-Invariant  Landmark Detection for Calcaneal Angle Measurement, Fracture Identification  and Fracture Region Segmentation