Search

Segmenting deep brain structures from magnetic resonance images is important for patient diagnosis, surgical planning, and research. Most current state-of-the-art solutions follow a segmentation-by-registration approach, where subject magnetic resonance imaging (MRIs) are mapped to a template with well-defined segmentations. However, registration-based pipelines are time-consuming, thus, limiting their clinical use. This paper uses deep learning to provide a one-step, robust, and efficient deep brain segmentation solution directly in the native space. The method consists of a preprocessing step to conform all MRI images to the same orientation, followed by a convolutional neural network using the nnU-Net framework. We use a total of 14 datasets from both research and clinical collections. Of these, seven were used for training and validation and seven were retained for testing. We trained the network to segment 30 deep brain structures, as well as a brain mask, using labels generated from a registration-based approach. We evaluated the generalizability of the network by performing a leave-one-dataset-out cross-validation, and independent testing on unseen datasets. Furthermore, we assessed cross-domain transportability by evaluating the results separately on different domains. We achieved an average dice score similarity of 0.89 ± 0.04 on the test datasets when compared to the registration-based gold standard. On our test system, the computation time decreased from 43 min for a reference registration-based pipeline to 1.3 min. Our proposed method is fast, robust, and generalizes with high reliability. It can be extended to the segmentation of other brain structures. It is publicly available on GitHub, and as a pip package for convenient usage.

Stereoelectroencephalography (sEEG) and deep brain stimulation (DBS) are effective surgical diagnostic and therapeutic procedures of the depth electrodes implantation in the brain. The benefit and outcome of these procedures directly depend on the electrode placement. Our goal was to accurately segment and visualize electrode position after the sEEG and DBS procedures. We trained a deep learning network to automatically segment electrodes trajectories and brain tissue from postsurgical CT images. We used 90 head CT scans that include intracerebral electrodes and their corresponding segmentation masks to train, validate and test the model. Mean accuracy and dice score in 5-fold cross-validation for the 3D-cascade U-Net model were 0.99 and 0.92, respectively. When the network was tested on an unseen test set, the dice overlap with the manual segmentations was 0.89. In this paper, we present a deep-learning approach for automatic patient-specific delineation of the brain, the sEEG and DBS electrodes from different varying quality of CT images. This robust method may inform on the postsurgical electrode positions fast and accurately. Moreover, it is useful as an input for neurosurgical and neuroscientific toolboxes and frameworks.

Item does not contain fulltext BACKGROUND: Parkinsonism is a common neurodegenerative syndrome in middle-aged and elderly persons. The etiology is multifactorial with a possible vascular contribution, but this has not been comprehensively studied. OBJECTIVE: To determine whether selected markers of subclinical vascular pathology are associated with the risk of all-cause parkinsonism in the general population. METHODS: We assessed a range of markers of subclinical vascular pathology (ankle-brachial index, carotid plaques and intima media thickness, retinal arteriolar and venular calibers) in 6199 persons from the population-based Rotterdam Study, who were free of parkinsonism and dementia at baseline. We followed these persons up till onset of parkinsonism, dementia, and death for 89,387 person-years until January 1, 2013. Hazard ratios (HRs) for all-cause parkinsonism and separately for Parkinson disease (PD) versus non-PD were estimated from competing risk regression models adjusting for potential confounders. RESULTS: During follow-up, we identified 211 cases of parkinsonism (110 had PD). None of the five markers of subclinical pathology was associated with all-cause parkinsonism. Only low ankle-brachial index was associated with a higher risk of non-PD parkinsonism (HR = 0.79, 95%CI: 0.68-0.92), but not with the risk of PD. CONCLUSION: We did not find a consistent pattern of associations between systemic vascular pathology markers with parkinsonism, suggesting that the potential involvement of vascular pathology is not prominent or needs further evaluation in studies with an even larger sample size.