  Stroke is the second leading cause of mortality worldwide. Immediate
attention and diagnosis play a crucial role regarding patient prognosis. The
key to diagnosis consists in localizing and delineating brain lesions. Standard
stroke examination protocols include the initial evaluation from a non-contrast
CT scan to discriminate between hemorrhage and ischemia. However, non-contrast
CTs may lack sensitivity in detecting subtle ischemic changes in the acute
phase. As a result, complementary diffusion-weighted MRI studies are captured
to provide valuable insights, allowing to recover and quantify stroke lesions.
This work introduced APIS, the first paired public dataset with NCCT and ADC
studies of acute ischemic stroke patients. APIS was presented as a challenge at
the 20th IEEE International Symposium on Biomedical Imaging 2023, where
researchers were invited to propose new computational strategies that leverage
paired data and deal with lesion segmentation over CT sequences. Despite all
the teams employing specialized deep learning tools, the results suggest that
the ischemic stroke segmentation task from NCCT remains challenging. The
annotated dataset remains accessible to the public upon registration, inviting
the scientific community to deal with stroke characterization from NCCT but
guided with paired DWI information.
