APIS: A paired CT-MRI dataset for ischemic stroke segmentation challenge

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.

Parkinson gait modelling from an anomaly deep representation

Parkinson's Disease is associated with gait movement disorders, such as postural instability, stiffness, and tremors. Today, some approaches implemented learning representations to quantify kinematic patterns during locomotion, supporting clinical procedures such as diagnosis and treatment planning. These approaches assumes a large amount of stratified and labeled data to optimize discriminative representations. Nonetheless, these considerations may restrict the operability of approaches in real scenarios during clinical practice. This work introduces a self-supervised generative representation, under the pretext of video reconstruction and anomaly detection framework. This architecture is trained following a one-class weakly supervised learning to avoid inter-class variance and approach the multiple relationships that represent locomotion. For validation 14 PD patients and 23 control subjects were recorded, and trained with the control population only, achieving an AUC of 86.9%, homoscedasticity level of 80% and shapeness level of 70% in the classification task considering its generalization.