Hypoxic Ischemic Encephalopathy Lesion Segmentation Challenge
Creators: name: Bao, Rina
name: Yangming Ou
name: P. Ellen Grant
Publisher: name: Zenodo
Year: 2023
Subjects: Neonatal Brain Injury
Hypoxic Ischemic Encephalopathy
Lesion Segmentation
Small Diffuse Lesion
MICCAI Challenges

description: Hypoxic ischemic encephalopathy (HIE) is a brain injury that occurs in 1 ~ 5/1000 term-born neonates. HIE affects around 200,000 term-born neonates every year worldwide, costing about $2 billion/year in the US alone, let alone family burdens. Although therapeutic hypothermia can reduce mortality and morbidity, yet around 60% of patients still die or develop neurocognitive deficits by 2 years of age. HIE lesion segmentation is a crucial step in clinical care of HIE. It will lead to a more accurate estimation of prognosis, a better understanding of neurological symptoms, and a timely prediction of response to therapy. HIE-related brain abnormalities in brain MRI are often diffuse (i.e., multi-focal), and small (over half the patients having lesions occupying &lt;1% of the brain volume). Segmentation for HIE MRI data is remarkably different from and arguably more challenging than other segmentation tasks such as brain tumors with big and focal lesions. Towards accurate early prognosis and medical diagnosis, there is an urgent but unmet need to improve the HIE lesion segmentation performance. Methods for HIE lesion segmentation need to be carefully designed due to its heavily unbalanced data and large variations in lesion size and location. Furthermore, accurate detection of HIE lesions can provide information and should benefit clinical targets, including predicting NICU outcomes or 2-year neurocognitive outcomes in our 2024 and 2025 challenges of HIE disease. The HIE lesion segmentation challenge will be the first challenge with public MRI data (N=131) for this important brain abnormality. This is the first public dataset for HIE with expert annotation of lesions. The data came from the<br> clinical workflow in the past 20 years and retrospective collection, curation, and annotation took 3-4 years. The release of the data is supported by an NIH R03 grant (2021-2023). As a result, the sample size is large for this disease, as annotations from experienced pediatric/neonatal neuroradiologists are otherwise rarely available. For example, many state-of-the-art HIE segmentation studies often use proprietary data with only lower dozens of samples. Public MRI datasets for brain tumors, Alzheimer’s Disease, and other diseases have fueled the advancement of AI algorithms and assisted medical diagnosis. We hope this 1st HIE lesion segmentation challenge with MRI will motivate a new research direction to learn challenging MRI segmentation with limited available data and relieve medical experts’ burdens for HIE diagnosis. The success of this first HIE lesion segmentation challenge will be a foundation for a series of planned challenges around HIE diagnosis and prognosis in the coming years at MICCAI. This includes, at least, multi-site/scanner HIE lesion segmentation in MRI, HIE outcome prediction in MRI, HIE outcome prediction combining MRI and clinical variables, and general challenge on small diffuse brain lesion segmentations.