Artificial Intelligence Application to Screen Abdominal Aortic Aneurysm Using Computed tomography Angiography.

The aim of our study is to validate a totally automated deep learning (DL)-based segmentation pipeline to screen abdominal aortic aneurysms (AAA) in computed tomography angiography (CTA) scans. We retrospectively evaluated 73 thoraco-abdominal CTAs (48 AAA and 25 control CTA) by means of a DL-based segmentation pipeline built on a 2.5D convolutional neural network (CNN) architecture to segment lumen and thrombus of the aorta. The maximum aortic diameter of the abdominal tract was compared using a threshold value (30 mm). Blinded manual measurements from a radiologist were done in order to create a true comparison. The screening pipeline was tested on 48 patients with aneurysm and 25 without aneurysm. The average diameter manually measured was 51.1 ± 14.4 mm for patients with aneurysms and 21.7 ± 3.6 mm for patients without aneurysms. The pipeline correctly classified 47 AAA out of 48 and 24 control patients out of 25 with 97% accuracy, 98% sensitivity, and 96% specificity. The automated pipeline of aneurysm measurements in the abdominal tract reported a median error with regard to the maximum abdominal diameter measurement of 1.3 mm. Our approach allowed for the maximum diameter of 51.2 ± 14.3 mm in patients with aneurysm and 22.0 ± 4.0 mm in patients without an aneurysm. The DL-based screening for AAA is a feasible and accurate method, calling for further validation using a larger pool of diagnostic images towards its clinical use.

A fully automated deep learning pipeline for micro-CT-imaging-based densitometry of lung fibrosis murine models.

Idiopathic pulmonary fibrosis, the archetype of pulmonary fibrosis (PF), is a chronic lung disease of a poor prognosis, characterized by progressively worsening of lung function. Although histology is still the gold standard for PF assessment in preclinical practice, histological data typically involve less than 1% of total lung volume and are not amenable to longitudinal studies. A miniaturized version of computed tomography (µCT) has been introduced to radiologically examine lung in preclinical murine models of PF. The linear relationship between X-ray attenuation and tissue density allows lung densitometry on total lung volume. However, the huge density changes caused by PF usually require manual segmentation by trained operators, limiting µCT deployment in preclinical routine. Deep learning approaches have achieved state-of-the-art performance in medical image segmentation. In this work, we propose a fully automated deep learning approach to segment right and left lung on µCT imaging and subsequently derive lung densitometry. Our pipeline first employs a convolutional network (CNN) for pre-processing at low-resolution and then a 2.5D CNN for higher-resolution segmentation, combining computational advantage of 2D and ability to address 3D spatial coherence without compromising accuracy. Finally, lungs are divided into compartments based on air content assessed by density. We validated this pipeline on 72 mice with different grades of PF, achieving a Dice score of 0.967 on test set. Our tests demonstrate that this automated tool allows for rapid and comprehensive analysis of µCT scans of PF murine models, thus laying the ground for its wider exploitation in preclinical settings.

Automatic 3D Segmentation and Identification of Anomalous Aortic Origin of the Coronary Arteries Combining Multi-view 2D Convolutional Neural Networks.

This work aimed to automatically segment and classify the coronary arteries with either normal or anomalous origin from the aorta (AAOCA) using convolutional neural networks (CNNs), seeking to enhance and fasten clinician diagnosis. We implemented three single-view 2D Attention U-Nets with 3D view integration and trained them to automatically segment the aortic root and coronary arteries of 124 computed tomography angiographies (CTAs), with normal coronaries or AAOCA. Furthermore, we automatically classified the segmented geometries as normal or AAOCA using a decision tree model. For CTAs in the test set (n = 13), we obtained median Dice score coefficients of 0.95 and 0.84 for the aortic root and the coronary arteries, respectively. Moreover, the classification between normal and AAOCA showed excellent performance with accuracy, precision, and recall all equal to 1 in the test set. We developed a deep learning-based method to automatically segment and classify normal coronary and AAOCA. Our results represent a step towards an automatic screening and risk profiling of patients with AAOCA, based on CTA.

Dynamic contrast-enhanced magnetic resonance imaging in the assessment of disease activity in patients with juvenile idiopathic arthritis.

OBJECTIVE: To determine the capability and reliability of dynamic contrast-enhanced MRI (DCE-MRI) in the assessment of disease activity in juvenile idiopathic arthritis (JIA). METHODS: DCE-MRI of the clinically more affected wrist or hip joints was undertaken in 21 patients, coupled with standard clinical assessment and biochemical analysis. Synovial inflammation was assessed by computing the maximum level of synovial enhancement (ME), the maximum rate of enhancement (MV) and the rate of early enhancement (REE) from the enhancement curves generated from region of interest independently delineated by two readers in the area of the ME. Correlations between dynamic parameters and clinical measures of disease activity, and static MRI synovitis score were investigated. RESULTS: In patients with wrist arthritis, REE correlated with the wrist swelling score (r(s) = 0.72), ESR (r(s) = 0.69), pain assessment scale (r(s) = 0.63) and childhood HAQ (r(s) = 0.60). In patients with hip arthritis, ME correlated with the hip limitation of motion (r(s) = 0.69). Static MRI synovitis score based on post-gadolinium enhancement correlated with MV (r(s) = 0.63) in patients with wrist arthritis and with ME (r = 0.68) in those with hip arthritis. The inter-reader agreement assessed by intra-class correlation coefficient (ICC) for ME, MV and REE (ICC = 0.98, 0.97 and 0.84, respectively) was excellent. CONCLUSIONS: DCE-MRI represents a promising method for the assessment of disease activity in JIA, especially in patients with wrist arthritis. As far as we know, this study is the first to demonstrate the feasibility, reliability and construct validity of DCE-MRI in JIA. These results should be confirmed in large-scale longitudinal studies in view of its further application in therapeutic decision making and in clinical trials.

3D Automatic Segmentation of Aortic Computed Tomography Angiography Combining Multi-View 2D Convolutional Neural Networks.

PURPOSE: The quantitative analysis of contrast-enhanced Computed Tomography Angiography (CTA) is essential to assess aortic anatomy, identify pathologies, and perform preoperative planning in vascular surgery. To overcome the limitations given by manual and semi-automatic segmentation tools, we apply a deep learning-based pipeline to automatically segment the CTA scans of the aortic lumen, from the ascending aorta to the iliac arteries, accounting for 3D spatial coherence. METHODS: A first convolutional neural network (CNN) is used to coarsely segment and locate the aorta in the whole sub-sampled CTA volume, then three single-view CNNs are used to effectively segment the aortic lumen from axial, sagittal, and coronal planes under higher resolution. Finally, the predictions of the three orthogonal networks are integrated to obtain a segmentation with spatial coherence. RESULTS: The coarse segmentation performed to identify the aortic lumen achieved a Dice coefficient (DSC) of 0.92 ± 0.01. Single-view axial, sagittal, and coronal CNNs provided a DSC of 0.92 ± 0.02, 0.92 ± 0.04, and 0.91 ± 0.02, respectively. Multi-view integration provided a DSC of 0.93 ± 0.02 and an average surface distance of 0.80 ± 0.26 mm on a test set of 10 CTA scans. The generation of the ground truth dataset took about 150 h and the overall training process took 18 h. In prediction phase, the adopted pipeline takes around 25 ± 1 s to get the final segmentation. CONCLUSION: The achieved results show that the proposed pipeline can effectively localize and segment the aortic lumen in subjects with aneurysm.

USim: A New Device and App for Case-Specific, Intraoperative Ultrasound Simulation and Rehearsal in Neurosurgery. A Preliminary Study.

BACKGROUND: Intraoperative ultrasound (iUS) is an excellent aid for neurosurgeons to perform better and safer operations thanks to real time, continuous, and high-quality intraoperative visualization. OBJECTIVE: To develop an innovative training method to teach how to perform iUS in neurosurgery. METHODS: Patients undergoing surgery for different brain or spine lesions were iUS scanned (before opening the dura) in order to arrange a collection of 3-dimensional, US images; this set of data was matched and paired to preoperatively acquired magnetic resonance images in order to create a library of neurosurgical cases to be studied offline for training and rehearsal purposes. This new iUS training approach was preliminarily tested on 14 European neurosurgery residents, who participated at the 2016 European Association of Neurosurgical Societies Training Course (Sofia, Bulgaria). RESULTS: USim was developed by Camelot and the Besta NeuroSim Center as a dedicated app that transforms any smartphone into a "virtual US probe," in order to simulate iUS applied to neurosurgery on a series of anonymized, patient-specific cases of different central nervous system tumors (eg, gliomas, metastases, meningiomas) for education, simulation, and rehearsal purposes. USim proved to be easy to use and allowed residents to quickly learn to handle a US probe and interpret iUS semiotics. CONCLUSION: USim could help neurosurgeons learn neurosurgical iUS safely. Furthermore, neurosurgeons could simulate many cases, of different brain/spinal cord tumors, that resemble the specific cases they have to operate on. Finally, the library of cases would be continuously updated, upgraded, and made available to neurosurgeons.

Unsupervised tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging.

OBJECTIVE: Design, implement, and validate an unsupervised method for tissue segmentation from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). METHODS: For each DCE-MRI acquisition, after a spatial registration phase, the time-varying intensity of each voxel is represented as a sparse linear combination of adaptive basis signals. Both the basis signals and the sparse coefficients are learned by minimizing a functional consisting of a data fidelity term and a sparsity inducing penalty. Tissue segmentation is then obtained by applying a standard clustering algorithm to the computed representation. RESULTS: Quantitative estimates on two real data sets are presented. In the first case, the overlap with expert annotation measured with the DICE metric is nearly 90% and thus 5% more accurate than state-of-the-art techniques. In the second case, assessment of the correlation between quantitative scores, obtained by the proposed method against imagery manually annotated by two experts, achieved a Pearson coefficient of 0.83 and 0.87, and a Spearman coefficient of 0.83 and 0.71, respectively. CONCLUSIONS: The sparse representation of DCE MRI signals obtained by means of adaptive dictionary learning techniques appears to be well-suited for unsupervised tissue segmentation and applicable to different clinical contexts with little effort.