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

Accurate localization of cephalometric landmarks holds great importance inthe fields of orthodontics and orthognathics due to its potential forautomating key point labeling. In the context of landmark detection,particularly in cephalometrics, it has been observed that existing methodsoften lack standardized pipelines and well-designed bias reduction processes,which significantly impact their performance. In this paper, we revisit arelated task, human pose estimation (HPE), which shares numerous similaritieswith cephalometric landmark detection (CLD), and emphasize the potential fortransferring techniques from the former field to benefit the latter. Motivatedby this insight, we have developed a robust and adaptable benchmark based onthe well-established HPE codebase known as MMPose. This benchmark can serve asa dependable baseline for achieving exceptional CLD performance. Furthermore,we introduce an upscaling design within the framework to further enhanceperformance. This enhancement involves the incorporation of a lightweight andefficient super-resolution module, which generates heatmap predictions onhigh-resolution features and leads to further performance refinement,benefiting from its ability to reduce quantization bias. In the MICCAICLDetection2023 challenge, our method achieves 1st place ranking on threemetrics and 3rd place on the remaining one. The code for our method isavailable at https://github.com/5k5000/CLdetection2023.

Revisiting Cephalometric Landmark Detection from the view of Human Pose Estimation with Lightweight Super-Resolution Head