  Automatic intracranial hemorrhage segmentation in 3D non-contrast head CT
(NCCT) scans is significant in clinical practice. Existing hemorrhage
segmentation methods usually ignores the anisotropic nature of the NCCT, and
are evaluated on different in-house datasets with distinct metrics, making it
highly challenging to improve segmentation performance and perform objective
comparisons among different methods. The INSTANCE 2022 was a grand challenge
held in conjunction with the 2022 International Conference on Medical Image
Computing and Computer Assisted Intervention (MICCAI). It is intended to
resolve the above-mentioned problems and promote the development of both
intracranial hemorrhage segmentation and anisotropic data processing. The
INSTANCE released a training set of 100 cases with ground-truth and a
validation set with 30 cases without ground-truth labels that were available to
the participants. A held-out testing set with 70 cases is utilized for the
final evaluation and ranking. The methods from different participants are
ranked based on four metrics, including Dice Similarity Coefficient (DSC),
Hausdorff Distance (HD), Relative Volume Difference (RVD) and Normalized
Surface Dice (NSD). A total of 13 teams submitted distinct solutions to resolve
the challenges, making several baseline models, pre-processing strategies and
anisotropic data processing techniques available to future researchers. The
winner method achieved an average DSC of 0.6925, demonstrating a significant
growth over our proposed baseline method. To the best of our knowledge, the
proposed INSTANCE challenge releases the first intracranial hemorrhage
segmentation benchmark, and is also the first challenge that intended to
resolve the anisotropic problem in 3D medical image segmentation, which
provides new alternatives in these research fields.
