2nd BONBID-HIE Challenge for Lesion Segmentation and Outcome Prediction
Creators: name: Bao, Rino
name: Grant, Ellen
name: Ou, Yangming
Publisher: name: Zenodo
Year: 2024
Subjects: Neonatal Brain Injury
Hypoxic Ischemic Encephalopathy
Lesion Segmentation
Small Diffuse Lesion
Outcome Prediction
MICCAI 2024 challenges

description: Hypoxic-Ischemic Encephalopathy (HIE) is a severe neonatal brain injury that affects around 0.5% of newborns globally. Despite advancements Therapeutic Hypothermia (TH), introduced in 2005, the prognosis for many infants remains challenging, with a considerable percentage (35%-50%) suffering adverse outcomes. A key issue is the limited ability to identify high-risk infants early and accurately assess therapeutic outcomes before age two [6]. This highlights an urgent need for precise and reliable biomarkers for early and accurate prognosis.Last year (2023), we held the first HIE brain MRI challenge. The task was to segment the HIE-related lesions in infant brain MRI. Totally 120 research groups downloaded our data and 14 algorithms were submitted. Built on last year's success, we propose a second-year challenge in MICCAI 2024, with two aims: (1) lesion segmentation in infant brain MRIs; and (2) 2-year neurologic outcome prediction using infant brain MRIs. The first aim is an extension of the first challenge in 2023, in that more brain MRIs without explicit lesion annotation will be provided to allow unsupervised algorithm development. The second aim is new and carries the ultimate clinical significance.Our data is the first public data for these tasks on HIE patients. This data was derived from clinical workflows over the past 20 years and a 3-4 year process of retrospective collection, curation, and annotation. Our laboratory, supported by grants R03, R61, and R21, has spent a decade meticulously organizing data, resulting in a dataset of nearly 300 samples.Technically, segmenting HIE MRI data is notably more complex than other tasks, such as segmenting brain tumors with larger, focal lesions (DICE: 0.8 and above). So, this HIE challenge represents AI tasks on small diffuse lesions, different from many other segmentation and recognition tasks on big focal target regions.

References

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