Assessment of Brain Injury and Brain Volumes after Posthemorrhagic Ventricular Dilatation: A Nested Substudy of the Randomized Controlled ELVIS Trial.

OBJECTIVE: To compare the effect of early and late intervention for posthemorrhagic ventricular dilatation on additional brain injury and ventricular volume using term-equivalent age-MRI. STUDY DESIGN: In the Early vs Late Ventricular Intervention Study (ELVIS) trial, 126 preterm infants ≤34 weeks of gestation with posthemorrhagic ventricular dilatation were randomized to low-threshold (ventricular index >p97 and anterior horn width >6 mm) or high-threshold (ventricular index >p97 + 4 mm and anterior horn width >10 mm) groups. In 88 of those (80%) with a term-equivalent age-MRI, the Kidokoro Global Brain Abnormality Score and the frontal and occipital horn ratio were measured. Automatic segmentation was used for volumetric analysis. RESULTS: The total Kidokoro score of the infants in the low-threshold group (n = 44) was lower than in the high-threshold group (n = 44; median, 8 [IQR, 5-12] vs median 12 [IQR, 9-17], respectively; P < .001). More infants in the low-threshold group had a normal or mildly increased score vs more infants in the high-threshold group with a moderately or severely increased score (46% vs 11% and 89% vs 54%, respectively; P = .002). The frontal and occipital horn ratio was lower in the low-threshold group (median, 0.42 [IQR, 0.34-0.63]) than the high-threshold group (median 0.48 [IQR, 0.37-0.68], respectively; P = .001). Ventricular cerebrospinal fluid volumes could be calculated in 47 infants and were smaller in the low-threshold group (P = .03). CONCLUSIONS: More brain injury and larger ventricular volumes were demonstrated in the high vs the low-threshold group. These results support the positive effects of early intervention for posthemorrhagic ventricular dilatation. TRIAL REGISTRATION: ISRCTN43171322.

Scaling data toward pan-cancer foundation models.

Recent advances in artificial intelligence (AI) have revolutionized computational pathology (CPath), particularly through deep learning (DL) and neural networks (NNs). In a recent study, Vorontsov et al. introduced Virchow, a new foundation model (FM) for CPath, which has shown promising results in cancer detection and biomarker prediction.

Brain segmentation in patients with perinatal arterial ischemic stroke.

BACKGROUND: Perinatal arterial ischemic stroke (PAIS) is associated with adverse neurological outcomes. Quantification of ischemic lesions and consequent brain development in newborn infants relies on labor-intensive manual assessment of brain tissues and ischemic lesions. Hence, we propose an automatic method utilizing convolutional neural networks (CNNs) to segment brain tissues and ischemic lesions in MRI scans of infants suffering from PAIS. MATERIALS AND METHODS: This single-center retrospective study included 115 patients with PAIS that underwent MRI after the stroke onset (baseline) and after three months (follow-up). Nine baseline and 12 follow-up MRI scans were manually annotated to provide reference segmentations (white matter, gray matter, basal ganglia and thalami, brainstem, ventricles, extra-ventricular cerebrospinal fluid, and cerebellum, and additionally on the baseline scans the ischemic lesions). Two CNNs were trained to perform automatic segmentation on the baseline and follow-up MRIs, respectively. Automatic segmentations were quantitatively evaluated using the Dice coefficient (DC) and the mean surface distance (MSD). Volumetric agreement between segmentations that were manually and automatically obtained was computed. Moreover, the scan quality and automatic segmentations were qualitatively evaluated in a larger set of MRIs without manual annotation by two experts. In addition, the scan quality was qualitatively evaluated in these scans to establish its impact on the automatic segmentation performance. RESULTS: Automatic brain tissue segmentation led to a DC and MSD between 0.78-0.92 and 0.18-1.08 mm for baseline, and between 0.88-0.95 and 0.10-0.58 mm for follow-up scans, respectively. For the ischemic lesions at baseline the DC and MSD were between 0.72-0.86 and 1.23-2.18 mm, respectively. Volumetric measurements indicated limited oversegmentation of the extra-ventricular cerebrospinal fluid in both the follow-up and baseline scans, oversegmentation of the ischemic lesions in the left hemisphere, and undersegmentation of the ischemic lesions in the right hemisphere. In scans without imaging artifacts, brain tissue segmentation was graded as excellent in more than 85% and 91% of cases, respectively for the baseline and follow-up scans. For the ischemic lesions at baseline, this was in 61% of cases. CONCLUSIONS: Automatic segmentation of brain tissue and ischemic lesions in MRI scans of patients with PAIS is feasible. The method may allow evaluation of the brain development and efficacy of treatment in large datasets.

Deep Learning Analysis of Coronary Arteries in Cardiac CT Angiography for Detection of Patients Requiring Invasive Coronary Angiography.

In patients with obstructive coronary artery disease, the functional significance of a coronary artery stenosis needs to be determined to guide treatment. This is typically established through fractional flow reserve (FFR) measurement, performed during invasive coronary angiography (ICA). We present a method for automatic and non-invasive detection of patients requiring ICA, employing deep unsupervised analysis of complete coronary arteries in cardiac CT angiography (CCTA) images. We retrospectively collected CCTA scans of 187 patients, 137 of them underwent invasive FFR measurement in 192 different coronary arteries. These FFR measurements served as a reference standard for the functional significance of the coronary stenosis. The centerlines of the coronary arteries were extracted and used to reconstruct straightened multi-planar reformatted (MPR) volumes. To automatically identify arteries with functionally significant stenosis that require ICA, each MPR volume was encoded into a fixed number of encodings using two disjoint 3D and 1D convolutional autoencoders performing spatial and sequential encodings, respectively. Thereafter, these encodings were employed to classify arteries using a support vector machine classifier. The detection of coronary arteries requiring invasive evaluation, evaluated using repeated cross-validation experiments, resulted in an area under the receiver operating characteristic curve of 0.81 ± 0.02 on the artery-level, and 0.87 ± 0.02 on the patient-level. The results demonstrate the feasibility of automatic non-invasive detection of patients that require ICA and possibly subsequent coronary artery intervention. This could potentially reduce the number of patients that unnecessarily undergo ICA.

Automatic extraction of the intracranial volume in fetal and neonatal MR scans using convolutional neural networks.

MR images of infants and fetuses allow non-invasive analysis of the brain. Quantitative analysis of brain development requires automatic brain tissue segmentation that is typically preceded by segmentation of the intracranial volume (ICV). Fast changes in the size and morphology of the developing brain, motion artifacts, and large variation in the field of view make ICV segmentation a challenging task. We propose an automatic method for segmentation of the ICV in fetal and neonatal MRI scans. The method was developed and tested with a diverse set of scans regarding image acquisition parameters (i.e. field strength, image acquisition plane, image resolution), infant age (23-45 weeks post menstrual age), and pathology (posthaemorrhagic ventricular dilatation, stroke, asphyxia, and Down syndrome). The results demonstrate that the method achieves accurate segmentation with a Dice coefficient (DC) ranging from 0.98 to 0.99 in neonatal and fetal scans regardless of image acquisition parameters or patient characteristics. Hence, the algorithm provides a generic tool for segmentation of the ICV that may be used as a preprocessing step for brain tissue segmentation in fetal and neonatal brain MR scans.