Bipartite Distance for Shape-Aware Landmark Detection in Spinal X-Ray Images

05/28/2020
by   Abdullah-Al-Zubaer Imran, et al.
9

Scoliosis is a congenital disease that causes lateral curvature in the spine. Its assessment relies on the identification and localization of vertebrae in spinal X-ray images, conventionally via tedious and time-consuming manual radiographic procedures that are prone to subjectivity and observational variability. Reliability can be improved through the automatic detection and localization of spinal landmarks. To guide a CNN in the learning of spinal shape while detecting landmarks in X-ray images, we propose a novel loss based on a bipartite distance (BPD) measure, and show that it consistently improves landmark detection performance.

READ FULL TEXT
research
01/09/2020

Vertebra-Focused Landmark Detection for Scoliosis Assessment

Adolescent idiopathic scoliosis (AIS) is a lifetime disease that arises ...
research
07/28/2017

A weighting strategy for Active Shape Models

Active Shape Models (ASM) are an iterative segmentation technique to fin...
research
12/09/2022

CEPHA29: Automatic Cephalometric Landmark Detection Challenge 2023

Quantitative cephalometric analysis is the most widely used clinical and...
research
06/05/2022

Accurate Scoliosis Vertebral Landmark Localization on X-ray Images via Shape-constrained Multi-stage Cascaded CNNs

Vertebral landmark localization is a crucial step for variant spine-rela...
research
07/24/2019

Multi-task Localization and Segmentation for X-ray Guided Planning in Knee Surgery

X-ray based measurement and guidance are commonly used tools in orthopae...
research
12/28/2014

Metacarpal Bones Localization in X-ray Imagery Using Particle Filter Segmentation

Statistical methods such as sequential Monte Carlo Methods were proposed...
research
08/10/2020

Locating Cephalometric X-Ray Landmarks with Foveated Pyramid Attention

CNNs, initially inspired by human vision, differ in a key way: they samp...

Please sign up or login with your details

Forgot password? Click here to reset