Overview

We invite you to participate in the first MBH-Seg Challenge. The focus of this year's challenge is to segment various types of brain hemorrhages base on non-contrast CT scans in a semi-supervised setting.

Intracranial hemorrhage (ICH) encompasses bleeding within the skull or brain, manifesting in several types based on the anatomical location of the bleed relative to the brain and its surrounding membranes. These types include extradural hemorrhage (EDH), subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), intraparenchymal hemorrhage (IPH), and intraventricular hemorrhage (IVH). The causes of ICH are varied, ranging from trauma and vascular malformations to hypertension and venous thrombosis. Detection and characterization of ICH are typically performed using non-contrast CT scans, which provide crucial insights into the type and distribution of the hemorrhage. Accurate localization, quantification, and classification of these hemorrhages are vital for clinical management, as they help clinicians assess severity, predict patient outcomes, and determine appropriate treatment strategies. Despite advances in deep learning that have propelled the automation of medical image segmentation, the segmentation of different ICH classes remains a challenge, particularly due to the scarcity of public datasets with multi-class, pixel-level annotations. Most existing datasets focus on hemorrhage classification or single-class segmentation (foreground vs. background), thus limiting the development of detailed segmentation tools.

This challenge enhances a public ICH dataset to create a comprehensive 3D, multi-class ICH dataset. This dataset will feature precise pixel-level hemorrhage annotations alongside a significantly larger pool of unannotated data. Participants are encouraged to employ semi-supervised learning techniques to develop diagnostic tools that are not only more consistent and rapid but also more accurate. Such advancements could potentially improve patient outcomes by reducing mortality and furthering the application of deep learning in medical imaging.

Prizes

1. Cash Awards:
First Prize: $800
Second Prize: $400
Third Prize: $200

2. Certificates for the top-rank teams.

3. The top teams will be invited to give oral presentations at the MICCAI 2024 Event in person or virtually.

4. Co-author of the challenge paper, which will be submitted to a top journal (MedIA/TMI).