Benchmarking the CoW with the TopCoW Challenge: Topology-Aware Anatomical Segmentation of the Circle of Willis for CTA and MRA.

The Circle of Willis (CoW) is an important network of arteries connecting major circulations of the brain. Its vascular architecture is believed to affect the risk, severity, and clinical outcome of serious neuro-vascular diseases. However, characterizing the highly variable CoW anatomy is still a manual and time-consuming expert task. The CoW is usually imaged by two angiographic imaging modalities, magnetic resonance angiography (MRA) and computed tomography angiography (CTA), but there exist limited public datasets with annotations on CoW anatomy, especially for CTA. Therefore we organized the TopCoW Challenge in 2023 with the release of an annotated CoW dataset. The TopCoW dataset was the first public dataset with voxel-level annotations for thirteen possible CoW vessel components, enabled by virtual-reality (VR) technology. It was also the first large dataset with paired MRA and CTA from the same patients. TopCoW challenge formalized the CoW characterization problem as a multiclass anatomical segmentation task with an emphasis on topological metrics. We invited submissions worldwide for the CoW segmentation task, which attracted over 140 registered participants from four continents. The top performing teams managed to segment many CoW components to Dice scores around 90%, but with lower scores for communicating arteries and rare variants. There were also topological mistakes for predictions with high Dice scores. Additional topological analysis revealed further areas for improvement in detecting certain CoW components and matching CoW variant topology accurately. TopCoW represented a first attempt at benchmarking the CoW anatomical segmentation task for MRA and CTA, both morphologically and topologically.

Deep Learning Segmentation of the Right Ventricle in Cardiac MRI: The M&Ms Challenge.

In recent years, several deep learning models have been proposed to accurately quantify and diagnose cardiac pathologies. These automated tools heavily rely on the accurate segmentation of cardiac structures in MRI images. However, segmentation of the right ventricle is challenging due to its highly complex shape and ill-defined borders. Hence, there is a need for new methods to handle such structure's geometrical and textural complexities, notably in the presence of pathologies such as Dilated Right Ventricle, Tricuspid Regurgitation, Arrhythmogenesis, Tetralogy of Fallot, and Inter-atrial Communication. The last MICCAI challenge on right ventricle segmentation was held in 2012 and included only 48 cases from a single clinical center. As part of the 12th Workshop on Statistical Atlases and Computational Models of the Heart (STACOM 2021), the M&Ms-2 challenge was organized to promote the interest of the research community around right ventricle segmentation in multi-disease, multi-view, and multi-center cardiac MRI. Three hundred sixty CMR cases, including short-axis and long-axis 4-chamber views, were collected from three Spanish hospitals using nine different scanners from three different vendors, and included a diverse set of right and left ventricle pathologies. The solutions provided by the participants show that nnU-Net achieved the best results overall. However, multi-view approaches were able to capture additional information, highlighting the need to integrate multiple cardiac diseases, views, scanners, and acquisition protocols to produce reliable automatic cardiac segmentation algorithms.

Vessel-CAPTCHA: An efficient learning framework for vessel annotation and segmentation.

Deep learning techniques for 3D brain vessel image segmentation have not been as successful as in the segmentation of other organs and tissues. This can be explained by two factors. First, deep learning techniques tend to show poor performances at the segmentation of relatively small objects compared to the size of the full image. Second, due to the complexity of vascular trees and the small size of vessels, it is challenging to obtain the amount of annotated training data typically needed by deep learning methods. To address these problems, we propose a novel annotation-efficient deep learning vessel segmentation framework. The framework avoids pixel-wise annotations, only requiring weak patch-level labels to discriminate between vessel and non-vessel 2D patches in the training set, in a setup similar to the CAPTCHAs used to differentiate humans from bots in web applications. The user-provided weak annotations are used for two tasks: (1) to synthesize pixel-wise pseudo-labels for vessels and background in each patch, which are used to train a segmentation network, and (2) to train a classifier network. The classifier network allows to generate additional weak patch labels, further reducing the annotation burden, and it acts as a second opinion for poor quality images. We use this framework for the segmentation of the cerebrovascular tree in Time-of-Flight angiography (TOF) and Susceptibility-Weighted Images (SWI). The results show that the framework achieves state-of-the-art accuracy, while reducing the annotation time by ∼77% w.r.t. learning-based segmentation methods using pixel-wise labels for training.

Acute liver failure in Still's disease relapse during pregnancy: case report and discussion of a possible trigger role of DILI.

BACKGROUND: Still's disease is a rare systemic inflammatory disease with frequent but generally mild liver involvement. The most common cause of acute liver failure in western countries is drug-induced liver injury, while it has rarely been reported in subjects suffering from Still's disease. CASE PRESENTATION: We report a case of a young woman presenting with SD reactivation in pregnancy and acute liver failure after delivery with a possible triggering role of drug induced liver injury. CONCLUSIONS: The prompt recognition of Still's disease reactivation allowed early introduction of steroid therapy and resolution of the clinical picture. We discuss potential factors precipitating ALF in this case, and implications for the diagnosis and management of such patients.

Italian census on neurosciences: the ICoNe2 study.

BACKGROUND: The growing impact of the emergency neurology of trauma centers and of mechanical thrombectomy for the treatment of acute ischemic stroke is revolutionizing the domain of eurosciences. METHODS: A census focused on the demographic distribution of the three main cohorts of neurosciences (neurologists, neuroradiologists, and neurosurgeons) was conducted in Italy between December 2015 and February 2017, and results were compared to the estimated retirement rates and loss for other reasons. RESULTS: The total number of neurosciences specialists active in Italy was 4394 at the end of the period of the survey. The estimated retirement rates and losses seem not be supplied by the physicians in training in the same period. CONCLUSIONS: A proper redistribution of the resources and the modification of the training programs seem to be mandatory to maintain acceptable standards of care for the Italian neurosciences during the next decade.

Opioid antagonist naltrexone for the treatment of pathological gambling in Parkinson disease.

UNLABELLED: Pathological gambling (PG) is a potential complication related to the treatment of Parkinson disease (PD) with dopamine agonists (DA). The cause of this disorder is unknown, but altered dopamine neurotransmission may be involved. OBJECTIVE: We evaluated the efficacy and tolerability of the opioid antagonist naltrexone in the treatment of PG in PD. METHODS: Our cases included 3 patients with PD who developed PG after DA treatment. RESULTS: Pathological gambling did not improve after reduction or discontinuation of DA. These patients responded poorly to serotonin reuptake inhibitors, whereas treatment with opioid antagonist naltrexone resulted in the remission of PG. Naltrexone treatment was well tolerated. In one patient, higher dose of naltrexone resulted in hepatic abnormalities, which resolved after dosage reduction. CONCLUSIONS: The opioid antagonist naltrexone could be an effective option for the treatment of PG in PD.