Label-efficient deep semantic segmentation of intracranial hemorrhages in CT-scans.

Intracranial hemorrhage (ICH) is a common finding in traumatic brain injury (TBI) and computed tomography (CT) is considered the gold standard for diagnosis. Automated detection of ICH provides clinical value in diagnostics and in the ability to feed robust quantification measures into future prediction models. Several studies have explored ICH detection and segmentation but the research process is somewhat hindered due to a lack of open large and labeled datasets, making validation and comparison almost impossible.

Machine learning slice-wise whole-lung CT emphysema score correlates with airway obstruction.

Objectives: Quantitative CT imaging is an important emphysema biomarker, especially in smoking cohorts, but does not always correlate to radiologists' visual CT assessments. The objectives were to develop and validate a neural network-based slice-wise whole-lung emphysema score (SWES) for chest CT, to validate SWES on unseen CT data, and to compare SWES with a conventional quantitative CT method.

Materials And Methods: Separate cohorts were used for algorithm development and validation.

ZYG-1 promotes limited centriole amplification in the C. elegans seam lineage.

Genome stability relies notably on the integrity of centrosomes and on the mitotic spindle they organize. Structural and numerical centrosome aberrations are frequently observed in human cancer, and there is increasing evidence that centrosome amplification can promote tumorigenesis. Here, we use C.