The most common cause of long-term disability and death in young adults is a traumatic brain injury. The decision for surgical intervention for craniotomy is dependent on the injury type and the patient's neurologic exam. The potential subtypes of intracranial hemorrhage that may necessitate surgical intervention include subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage. We proposed a novel automatic method for segmenting the hemorrhage subtypes on a CT scan by integrated CT scan with bone window as input of a deep learning model. Brain CT scans were collected from adult patients and annotated regions of subdural hemorrhage, epidural hemorrhage, and intraparenchymal hemorrhage by neuroradiologists. Their raw DICOM images were preprocessed by two different window settings i.e., subdural and bone windows. The collected CT scans were divided into two datasets namely training and test datasets. A deep-learning model was modified to segment regions of each hemorrhage subtype. The model is a three-dimensional convolutional neural network including four parallel pathways that process the input at different resolutions. It was trained by a training dataset. After the segmentation result was produced by the deep-learning model, it was then improved in the post-processing step. The size of the segmented lesion was considered, and a region-growing algorithm was applied. We evaluated the performance of the proposed method on the test dataset. The method reached the median Dice similarity coefficients higher than 0.37 for each hemorrhage subtype. The proposed method demonstrates higher Dice similarity coefficients and improved segmentation performance compared to previously published literature.

Several large language models showed high accuracy and quality for translating radiology reports across high-resource and low-resource languages; however, no one-size-fits-all model was identified.

This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/.

BACKGROUND AND PURPOSE: Facial parameters are used for evaluating normal growth patterns, diagnosing patients with craniofacial abnormalities, and planning surgical procedures. However, these parameters vary by ethnicity and race. This study aims to describe soft-tissue and bony facial parameters based on CT of healthy pediatric and adolescent patients in Thailand. MATERIALS AND METHODS: CT imaging of the brain, orbit, facial bones, and neck was performed at Maharaj Nakorn Chiangmai Hospital, in patients from birth to 19 years old. Patients with known syndromic disease, craniofacial syndrome, facial trauma and/or infection, and previous surgery that deformed the study area were excluded. The key points of measurement were soft-tissue intercanthal, bony interorbital, and bony lateral orbital distances. RESULTS: There were 932 patients: 554 males (59.4%) and 378 females (40.6%). Facial parameters rapidly increased in the first 2 years of life. Significant differences in these parameters between the males and females were found at the age of ≥15 years. However, ratios of the interorbital to the lateral orbital distance were generally consistent among age groups in both sexes, at 0.25. CONCLUSIONS: This study, in Thailand, provides detailed age- and sex-specific normative data of the craniofacial measurements in children and adolescences based on CT imaging. These data can be used for evaluating individual patients with craniofacial abnormalities as well as determining the treatment in Thai and Asian populations, in whom craniofacial abnormalities, for example, frontoethmoidal encephalomeningocele, are common.

The RSNA Lumbar Degenerative Imaging Spine Classification dataset is the largest publicly available adult MRI lumbar spine dataset for degenerative disease. The dataset includes multisequence, multiplanar MRI scans from 2697 patients through contributions from eight institutions across six countries and five continents.