Deep learning solution for medical image localization and orientation detection.

Magnetic Resonance (MR) imaging plays an important role in medical diagnosis and biomedical research. Due to the high in-slice resolution and low through-slice resolution nature of MR imaging, the usefulness of the reconstruction highly depends on the positioning of the slice group. Traditional clinical workflow relies on time-consuming manual adjustment that cannot be easily reproduced.

Structure Correction for Robust Volume Segmentation in Presence of Tumors.

CNN based lung segmentation models in absence of diverse training dataset fail to segment lung volumes in presence of severe pathologies such as large masses, scars, and tumors. To rectify this problem, we propose a multi-stage algorithm for lung volume segmentation from CT scans. The algorithm uses a 3D CNN in the first stage to obtain a coarse segmentation of the left and right lungs.

Impact of an educational intervention on errors in death certification: An observational study from the intensive care unit of a tertiary care teaching hospital.

Background: A high incidence of errors occur while filling up death certificates in hospitals. The purpose of this study was to analyze the impact of an educational intervention on errors in death certification in an intensive care unit (ICU). Patients admitted to ICUs by virtue of being critically ill have a higher mortality than other hospitalized patients.