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Accurate detection and quantification of unruptured intracranial aneurysms (UIAs) is important for rupture risk assessment and to allow an informed treatment decision to be made. Currently, 2D manual measures used to assess UIAs on Time-of-Flight magnetic resonance angiographies (TOF-MRAs) lack 3D information and there is substantial inter-observer variability for both aneurysm detection and assessment of aneurysm size and growth. 3D measures could be helpful to improve aneurysm detection and quantification but are time-consuming and would therefore benefit from a reliable automatic UIA detection and segmentation method. The Aneurysm Detection and segMentation (ADAM) challenge was organised in which methods for automatic UIA detection and segmentation were developed and submitted to be evaluated on a diverse clinical TOF-MRA dataset. A training set (113 cases with a total of 129 UIAs) was released, each case including a TOF-MRA, a structural MR image (T1, T2 or FLAIR), annotation of any present UIA(s) and the centre voxel of the UIA(s). A test set of 141 cases (with 153 UIAs) was used for evaluation. Two tasks were proposed: (1) detection and (2) segmentation of UIAs on TOF-MRAs. Teams developed and submitted containerised methods to be evaluated on the test set. Task 1 was evaluated using metrics of sensitivity and false positive count. Task 2 was evaluated using dice similarity coefficient, modified hausdorff distance (95 th percentile) and volumetric similarity. For each task, a ranking was made based on the average of the metrics. In total, eleven teams participated in task 1 and nine of those teams participated in task 2. Task 1 was won by a method specifically designed for the detection task (i.e. not participating in task 2). Based on segmentation metrics, the top two methods for task 2 performed statistically significantly better than all other methods. The detection performance of the top-ranking methods was comparable to visual inspection for larger aneurysms. Segmentation performance of the top ranking method, after selection of true UIAs, was similar to interobserver performance. The ADAM challenge remains open for future submissions and improved submissions, with a live leaderboard to provide benchmarking for method developments at https://adam.isi.uu.nl/.

Cerebral computed tomography angiography is a widely available imaging technique that helps in the diagnosis of vascular pathologies. Contrast administration is needed to accurately assess the arteries. On non-contrast computed tomography, arteries are hardly distinguishable from the brain tissue, therefore, radiologists do not consider this imaging modality appropriate for the evaluation of vascular pathologies. There are known contraindications to administering iodinated contrast media, and in these cases, the patient has to undergo another examination to visualize cerebral arteries, such as magnetic resonance angiography. Deep learning for image segmentation has proven to perform well on medical data for a variety of tasks. The aim of this research was to apply deep learning methods to segment cerebral arteries on non-contrast computed tomography scans and consequently, generate angiographies without the need for contrast administration. The dataset for this research included 131 patients who underwent brain non-contrast computed tomography directly followed by computed tomography with contrast administration. Then, the segmentations of arteries were generated and aligned with non-contrast computed tomography scans. A deep learning model based on the U-net architecture was trained to perform the segmentation of blood vessels on non-contrast computed tomography. An evaluation was performed on separate test data, as well as using cross-validation, reaching Dice coefficients of 0.638 and 0.673, respectively. This study proves that deep learning methods can be leveraged to quickly solve problems that are difficult and time-consuming for a human observer, therefore providing physicians with additional information on the patient. To encourage the further development of similar tools, all code used for this research is publicly available.

Hydrocephalus is a common neurological condition that can have traumatic ramifications and can be lethal without treatment. Nowadays, during therapy radiologists have to spend a vast amount of time assessing the volume of cerebrospinal fluid (CSF) by manual segmentation on Computed Tomography (CT) images. Further, some of the segmentations are prone to radiologist bias and high intraobserver variability. To improve this, researchers are exploring methods to automate the process, which would enable faster and more unbiased results. In this study, we propose the application of U-Net convolutional neural network in order to automatically segment CT brain scans for location of CSF. U-Net is a neural network that has proven to be successful for various interdisciplinary segmentation tasks. We optimised training using state of the art methods, including “1cycle” learning rate policy, transfer learning, generalized dice loss function, mixed float precision, self-attention, and data augmentation. Even though the study was performed using a limited amount of data (80 CT images), our experiment has shown near human-level performance. We managed to achieve a 0.917 mean dice score with 0.0352 standard deviation on cross validation across the training data and a 0.9506 mean dice score on a separate test set. To our knowledge, these results are better than any known method for CSF segmentation in hydrocephalic patients, and thus, it is promising for potential practical applications.

We report a case of a patient with recurrent symptoms of superior mesenteric ischemia 7 months after successful percutaneous angioplasty and implantation of a stent in the superior mesenteric artery (SMA). Stent fracture and stent displacement were observed. To the best of our knowledge, this is the first report of SMA stent fracture with stent displacement.