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Fawzia Amri

amrif1

  •  United States of America
  •  University of Dayton
  •  Electrical and Computer Engineering
  •  Website
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  • Member for 2 years, 4 months

Activity Overview

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VESSEL12
Challenge User

The VESSEL12 challenge compares methods for automatic (and semi-automatic) segmentation of blood vessels in the lungs from CT images.

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QUBIQ2021
Challenge User

Quantification of Uncertainties in Biomedical Image Segmentation Challenge 2021

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AIROGS
Challenge User

Artificial Intelligence for RObust Glaucoma Screening Challenge

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Parse2022
Challenge User

It is of significant clinical interest to study pulmonary artery structures in the field of medical image analysis. One prerequisite step is to segment pulmonary artery structures from CT with high accuracy and low time-consuming. The segmentation of pulmonary artery structures benefits the quantification of its morphological changes for diagnosis of pulmonary hypertension and thoracic surgery. However, due to the complexity of pulmonary artery topology, automated segmentation of pulmonary artery topology is a challenging task. Besides, the open accessible large-scale CT data with well labeled pulmonary artery are scarce (The large variations of the topological structures from different patients make the annotation an extremely challenging process). The lack of well labeled pulmonary artery hinders the development of automatic pulmonary artery segmentation algorithm. Hence, we try to host the first Pulmonary ARtery SEgmentation challenge in MICCAI 2022 (Named Parse2022) to start a new research topic.