Minimally interactive segmentation of soft-tissue tumors on CT and MRI using deep learning.

Background: Segmentations are crucial in medical imaging for morphological, volumetric, and radiomics biomarkers. Manual segmentation is accurate but not feasible in clinical workflow, while automatic segmentation generally performs sub-par.

Purpose: To develop a minimally interactive deep learning-based segmentation method for soft-tissue tumors (STTs) on CT and MRI.

Designing interpretable deep learning applications for functional genomics: a quantitative analysis.

Deep learning applications have had a profound impact on many scientific fields, including functional genomics. Deep learning models can learn complex interactions between and within omics data; however, interpreting and explaining these models can be challenging. Interpretability is essential not only to help progress our understanding of the biological mechanisms underlying traits and diseases but also for establishing trust in these model's efficacy for healthcare applications.

Phenotype prediction using biologically interpretable neural networks on multi-cohort multi-omics data.

Integrating multi-omics data into predictive models has the potential to enhance accuracy, which is essential for precision medicine. In this study, we developed interpretable predictive models for multi-omics data by employing neural networks informed by prior biological knowledge, referred to as visible networks. These neural networks offer insights into the decision-making process and can unveil novel perspectives on the underlying biological mechanisms associated with traits and complex diseases.

Quantifying dysmorphologies of the neurocranium using artificial neural networks.

Background: Craniosynostosis, a congenital condition characterized by the premature fusion of cranial sutures, necessitates objective methods for evaluating cranial morphology to enhance patient treatment. Current subjective assessments often lead to inconsistent outcomes. This study introduces a novel, quantitative approach to classify craniosynostosis and measure its severity.

CAVE: Cerebral artery-vein segmentation in digital subtraction angiography.

Cerebral X-ray digital subtraction angiography (DSA) is a widely used imaging technique in patients with neurovascular disease, allowing for vessel and flow visualization with high spatio-temporal resolution. Automatic artery-vein segmentation in DSA plays a fundamental role in vascular analysis with quantitative biomarker extraction, facilitating a wide range of clinical applications. The widely adopted U-Net applied on static DSA frames often struggles with disentangling vessels from subtraction artifacts.

Automated Assessment of T2-Weighted MRI to Differentiate Malignant and Benign Primary Solid Liver Lesions in Noncirrhotic Livers Using Radiomics.

Rationale And Objectives: Distinguishing malignant from benign liver lesions based on magnetic resonance imaging (MRI) is an important but often challenging task, especially in noncirrhotic livers. We developed and externally validated a radiomics model to quantitatively assess T2-weighted MRI to distinguish the most common malignant and benign primary solid liver lesions in noncirrhotic livers.

Materials And Methods: Data sets were retrospectively collected from three tertiary referral centers (A, B, and C) between 2002 and 2018.

Reliability and Agreement of Automated Head Measurements From 3-Dimensional Photogrammetry in Young Children.

This study aimed to assess the reliability and agreement of automated head measurements using 3-dimensional (3D) photogrammetry in young children. Specifically, the study evaluated the agreement between manual and automated occipitofrontal circumference (OFC) measurements (n = 264) obtained from 3D images of 188 patients diagnosed with sagittal synostosis using a novel automated method proposed in this study. In addition, the study aimed to determine the interrater and intrarater reliability of the automatically extracted OFC, cephalic index, and volume.

Towards quantitative digital subtraction perfusion angiography: An animal study.

Background: X-ray digital subtraction angiography (DSA) is the imaging modality for peri-procedural guidance and treatment evaluation in (neuro-) vascular interventions. Perfusion image construction from DSA, as a means of quantitatively depicting cerebral hemodynamics, has been shown feasible. However, the quantitative property of perfusion DSA has not been well studied.

Evaluation of AR visualization approaches for catheter insertion into the ventricle cavity.

Augmented reality (AR) has shown potential in computer-aided surgery. It allows for the visualization of hidden anatomical structures as well as assists in navigating and locating surgical instruments at the surgical site. Various modalities (devices and/or visualizations) have been used in the literature, but few studies investigated the adequacy/superiority of one modality over the other.

Subclinical liver traits are associated with structural and hemodynamic brain imaging markers.

Background & Aims: Impaired liver function affects brain health and therefore understanding potential mechanisms for subclinical liver disease is essential. We assessed the liver-brain associations using liver measures with brain imaging markers, and cognitive measures in the general population.

Methods: Within the population-based Rotterdam Study, liver serum and imaging measures (ultrasound and transient elastography), metabolic dysfunction-associated fatty liver disease (MAFLD), non-alcoholic fatty liver disease (NAFLD) and fibrosis phenotypes, and brain structure were determined in 3493 non-demented and stroke-free participants in 2009-2014.

Computational methods for the analysis of early-pregnancy brain ultrasonography: a systematic review.

Background: Early screening of the brain is becoming routine clinical practice. Currently, this screening is performed by manual measurements and visual analysis, which is time-consuming and prone to errors. Computational methods may support this screening.

Association between prenatal alcohol exposure and children's facial shape: a prospective population-based cohort study.

Study Question: Is there an association between low-to-moderate levels of prenatal alcohol exposure (PAE) and children's facial shape?

Summary Answer: PAE before and during pregnancy, even at low level (<12 g of alcohol per week), was found associated with the facial shape of children, and these associations were found attenuated as children grow older.

What Is Known Already: High levels of PAE during pregnancy can have significant adverse associations with a child's health development resulting in recognizably abnormal facial development.

Study Design, Size, Duration: This study was based on the Generation R Study, a prospective cohort from fetal life onwards with maternal and offspring data.

Deep reinforcement learning for cerebral anterior vessel tree extraction from 3D CTA images.

Extracting the cerebral anterior vessel tree of patients with an intracranial large vessel occlusion (LVO) is relevant to investigate potential biomarkers that can contribute to treatment decision making. The purpose of our work is to develop a method that can achieve this from routinely acquired computed tomography angiography (CTA) and computed tomography perfusion (CTP) images. To this end, we regard the anterior vessel tree as a set of bifurcations and connected centerlines.

Combined molecular subtyping, grading, and segmentation of glioma using multi-task deep learning.

Background: Accurate characterization of glioma is crucial for clinical decision making. A delineation of the tumor is also desirable in the initial decision stages but is time-consuming. Previously, deep learning methods have been developed that can either non-invasively predict the genetic or histological features of glioma, or that can automatically delineate the tumor, but not both tasks at the same time.