A Self-supervised Deep Learning Model for Diagonal Sulcus Detection with Limited Labeled Data.

Sulci are a fundamental part of brain morphology, closely linked to brain function, cognition, and behavior. Tertiary sulci, characterized as the shallowest and smallest subtype, pose a challenging task for detection. The diagonal sulcus (ds), located in a crucial area in language processing, has a prevalence between 50% and 60%.

Assessing flow diverter porosity: a comparative analysis of quantification techniques based on imaging and simulation.

Background/purpose: Flow diverter porosity directly influences the blood flow reduction at the aneurysm neck level and the anatomical result of the treatment. In this research, we present and compare three methodologies to determine the local porosity of deployed flow diverters.

Method: Three-dimensional rotational angiography was used to obtain computational vessel models of three patients.

Modification of Woven Endo-Bridge After Intracranial Aneurysm Treatment: A Methodology for Three-Dimensional Analysis of Shape and Relative Position Changes.

During follow-up of patients treated with WEB devices, shape changes have been observed. The quantitative three-dimensional measurement of the WEB shape modification (WSM) would offer useful information to be studied in association with the anatomical results and try to better understand mechanisms implicated in this modification phenomenon. We present a methodology to quantify the morphology and position of the WEB device in relation to the vascular anatomy.

Evaluating sleep-stage classification: how age and early-late sleep affects classification performance.

Sleep stage classification is a common method used by experts to monitor the quantity and quality of sleep in humans, but it is a time-consuming and labour-intensive task with high inter- and intra-observer variability. Using wavelets for feature extraction and random forest for classification, an automatic sleep-stage classification method was sought and assessed. The age of the subjects, as well as the moment of sleep (early-night and late-night), were confronted to the performance of the classifier.