Multivessel Coronary Artery Segmentation and Stenosis Localisation using
  Ensemble Learning
Muhammad Bilal Dinis Martinho Reiner Sim Adnan Qayyum Hunaid Vohra Massimo Caputo Taofeek Akinosho Sofiat Abioye Zaheer Khan Waleed Niaz Junaid Qadir
Coronary angiography analysis is a common clinical task performed by
cardiologists to diagnose coronary artery disease (CAD) through an assessment
of atherosclerotic plaque's accumulation. This study introduces an end-to-end
machine learning solution developed as part of our solution for the MICCAI 2023
Automatic Region-based Coronary Artery Disease diagnostics using x-ray
angiography imagEs (ARCADE) challenge, which aims to benchmark solutions for
multivessel coronary artery segmentation and potential stenotic lesion
localisation from X-ray coronary angiograms. We adopted a robust baseline model
training strategy to progressively improve performance, comprising five
successive stages of binary class pretraining, multivessel segmentation,
fine-tuning using class frequency weighted dataloaders, fine-tuning using
F1-based curriculum learning strategy (F1-CLS), and finally multi-target
angiogram view classifier-based collective adaptation. Unlike many other
medical imaging procedures, this task exhibits a notable degree of
interobserver variability. %, making it particularly amenable to automated
analysis. Our ensemble model combines the outputs from six baseline models
using the weighted ensembling approach, which our analysis shows is found to
double the predictive accuracy of the proposed solution. The final prediction
was further refined, targeting the correction of misclassified blobs. Our
solution achieved a mean F1 score of $37.69\%$ for coronary artery
segmentation, and $39.41\%$ for stenosis localisation, positioning our team in
the 5th position on both leaderboards. This work demonstrates the potential of
automated tools to aid CAD diagnosis, guide interventions, and improve the
accuracy of stent injections in clinical settings.